Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

PubMed

Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

2016-02-01

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

Effects of Self-esteem, Optimism, and Perceived Control on Depressive Symptoms in Stroke Survivor-Spouse Dyads.

PubMed

Chung, Misook L; Bakas, Tamilyn; Plue, Laura D; Williams, Linda S

2016-01-01

Depressive symptoms are common in stroke survivors and their family caregivers. Given the interdependent relationship between the members of dyads in poststroke management, improving depressive symptoms in dyads may depend on their partner's characteristics. Self-esteem, optimism, and perceived control, all known to be associated with depressive symptoms in an individual, may also contribute to their partner's depressive symptoms. The purpose of this study is to examine actor and partner effects of self-esteem, optimism, and perceived control on depression in stroke survivors and their spousal caregivers. A total of 112 ischemic stroke survivors (78% white, 34% women; mean age, 62.5 ± 12.3 years) and their spouses (mean age, 60.6 ± 12.9 years) completed surveys in which depressive symptoms, self-esteem, optimism, and perceived control were assessed using the Patient Health Questionnaire, the Rosenberg Self-esteem Scale, the Revised Life Orientation Test, and the Sense of Control Scale. Multilevel modeling, actor-partner interdependence model regression was used to determine influences on depressive symptoms within the dyad. Individuals with lower self-esteem, optimism, and perceived control had higher levels of depressive symptoms. Stroke survivors whose spouses had lower levels of self-esteem (B = -0.338, P < .001) and optimism (B = -0.361, P < .027) tended to have higher levels of depressive symptoms. Spouses whose stroke survivors had lower levels of self-esteem (B = -0.047, P = .036) also had higher levels of depressive symptoms. We found significant partner effects of self-esteem on depression for both members and partner effect of optimism on patient's depressive symptoms. These findings suggest that further research is needed to determine if dyadic interventions may help to improve self-esteem, optimism, and depressive symptoms in both patients and their caregivers.

Effects of Self-Esteem, Optimism, and Perceived Control on Depressive Symptoms in Stroke Survivor-Spouse Dyads

PubMed Central

Chung, Misook L.; Bakas, Tamilyn; Plue, Laura D.; Williams, Linda S.

2014-01-01

Background Depressive symptoms are common in stroke survivors and their family caregivers. Given the interdependent relationship between the members of dyads in post-stroke management, improving depressive symptoms in dyads may depend on their partner's characteristics. Self-esteem, optimism, and perceived control, all known to be associated with depressive symptoms in an individual, may also contribute to their partner's depressive symptoms. Purpose The purpose of this study was to examine actor and partner effects of self-esteem, optimism, and perceived control on depression in the stroke survivors and their spousal caregivers. Methods A total of 112 ischemic stroke survivors (78% white, 34% female, mean age 62.5 ± 12.3) and their spouses (mean age 60.6 ±12.9) completed surveys in which depressive symptoms, self-esteem, optimism, and perceived control were assessed using the Patient Health Questionnaire, the Rosenberg Self-esteem Scale, the Revised Life Orientation Test, and the Sense of Control Scale. Multilevel modeling, actor-partner interdependence model regression was used to determine influences on depressive symptoms within the dyad. Results Individuals with lower self-esteem, optimism, and perceived control had higher levels of depressive symptoms. Stroke survivors whose spouses had lower levels of self-esteem (B= −.338, P<.001) and optimism (B= −.361, P<.027) tended to have higher levels of depressive symptoms. Spouses whose stroke survivors had lower levels of self-esteem (B= −.047, P=.036) also had higher levels of depressive symptoms. Conclusion We found significant partner effects of self-esteem on depression for both members and partner effect of optimism on patient's depressive symptoms. These findings suggest that further research is needed to determine if dyadic interventions may help to improve self-esteem, optimism, and depressive symptoms in both patients and their caregivers. PMID:25658182

Optimal control problems of epidemic systems with parameter uncertainties: application to a malaria two-age-classes transmission model with asymptomatic carriers.

PubMed

Mwanga, Gasper G; Haario, Heikki; Capasso, Vicenzo

2015-03-01

The main scope of this paper is to study the optimal control practices of malaria, by discussing the implementation of a catalog of optimal control strategies in presence of parameter uncertainties, which is typical of infectious diseases data. In this study we focus on a deterministic mathematical model for the transmission of malaria, including in particular asymptomatic carriers and two age classes in the human population. A partial qualitative analysis of the relevant ODE system has been carried out, leading to a realistic threshold parameter. For the deterministic model under consideration, four possible control strategies have been analyzed: the use of Long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic and asymptomatic individuals. The numerical results show that using optimal control the disease can be brought to a stable disease free equilibrium when all four controls are used. The Incremental Cost-Effectiveness Ratio (ICER) for all possible combinations of the disease-control measures is determined. The numerical simulations of the optimal control in the presence of parameter uncertainty demonstrate the robustness of the optimal control: the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the designing of cost-effective strategies for disease controls with multiple interventions, even under considerable uncertainty of model parameters. Copyright © 2014 Elsevier Inc. All rights reserved.

Topology optimization of embedded piezoelectric actuators considering control spillover effects

NASA Astrophysics Data System (ADS)

Gonçalves, Juliano F.; De Leon, Daniel M.; Perondi, Eduardo A.

2017-02-01

This article addresses the problem of active structural vibration control by means of embedded piezoelectric actuators. The topology optimization method using the solid isotropic material with penalization (SIMP) approach is employed in this work to find the optimum design of actuators taken into account the control spillover effects. A coupled finite element model of the structure is derived assuming a two-phase material and this structural model is written into the state-space representation. The proposed optimization formulation aims to determine the distribution of piezoelectric material which maximizes the controllability for a given vibration mode. The undesirable effects of the feedback control on the residual modes are limited by including a spillover constraint term containing the residual controllability Gramian eigenvalues. The optimization of the shape and placement of the conventionally embedded piezoelectric actuators are performed using a Sequential Linear Programming (SLP) algorithm. Numerical examples are presented considering the control of the bending vibration modes for a cantilever and a fixed beam. A Linear-Quadratic Regulator (LQR) is synthesized for each case of controlled structure in order to compare the influence of the additional constraint.

Effects of Edge Directions on the Structural Controllability of Complex Networks.

PubMed

Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

2015-01-01

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain "inappropriate" edge directions. However, the existence of multiple sets of "inappropriate" edge directions suggests that different edges have different effects on optimal controllability-that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions-utilizing only local information-which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.

Closed loop models for analyzing the effects of simulator characteristics. [digital simulation of human operators

NASA Technical Reports Server (NTRS)

Baron, S.; Muralidharan, R.; Kleinman, D. L.

1978-01-01

The optimal control model of the human operator is used to develop closed loop models for analyzing the effects of (digital) simulator characteristics on predicted performance and/or workload. Two approaches are considered: the first utilizes a continuous approximation to the discrete simulation in conjunction with the standard optimal control model; the second involves a more exact discrete description of the simulator in a closed loop multirate simulation in which the optimal control model simulates the pilot. Both models predict that simulator characteristics can have significant effects on performance and workload.

Prediction of pilot-aircraft stability boundaries and performance contours

NASA Technical Reports Server (NTRS)

Stengel, R. F.; Broussard, J. R.

1977-01-01

Control-theoretic pilot models can provide important new insights regarding the stability and performance characteristics of the pilot-aircraft system. Optimal-control pilot models can be formed for a wide range of flight conditions, suggesting that the human pilot can maintain stability if he adapts his control strategy to the aircraft's changing dynamics. Of particular concern is the effect of sub-optimal pilot adaptation as an aircraft transitions from low to high angle-of-attack during rapid maneuvering, as the changes in aircraft stability and control response can be extreme. This paper examines the effects of optimal and sub-optimal effort during a typical 'high-g' maneuver, and it introduces the concept of minimum-control effort (MCE) adaptation. Limited experimental results tend to support the MCE adaptation concept.

A predictive control framework for optimal energy extraction of wind farms

NASA Astrophysics Data System (ADS)

Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.

2016-09-01

This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.

OPTIMIZATION OF INTEGRATED URBAN WET-WEATHER CONTROL STRATEGIES

EPA Science Inventory

An optimization method for urban wet weather control (WWC) strategies is presented. The developed optimization model can be used to determine the most cost-effective strategies for the combination of centralized storage-release systems and distributed on-site WWC alternatives. T...

Optimal dynamic pricing for deteriorating items with reference-price effects

NASA Astrophysics Data System (ADS)

Xue, Musen; Tang, Wansheng; Zhang, Jianxiong

2016-07-01

In this paper, a dynamic pricing problem for deteriorating items with the consumers' reference-price effect is studied. An optimal control model is established to maximise the total profit, where the demand not only depends on the current price, but also is sensitive to the historical price. The continuous-time dynamic optimal pricing strategy with reference-price effect is obtained through solving the optimal control model on the basis of Pontryagin's maximum principle. In addition, numerical simulations and sensitivity analysis are carried out. Finally, some managerial suggestions that firm may adopt to formulate its pricing policy are proposed.

Identification of optimal feedback control rules from micro-quadrotor and insect flight trajectories.

PubMed

Faruque, Imraan A; Muijres, Florian T; Macfarlane, Kenneth M; Kehlenbeck, Andrew; Humbert, J Sean

2018-06-01

This paper presents "optimal identification," a framework for using experimental data to identify the optimality conditions associated with the feedback control law implemented in the measurements. The technique compares closed loop trajectory measurements against a reduced order model of the open loop dynamics, and uses linear matrix inequalities to solve an inverse optimal control problem as a convex optimization that estimates the controller optimality conditions. In this study, the optimal identification technique is applied to two examples, that of a millimeter-scale micro-quadrotor with an engineered controller on board, and the example of a population of freely flying Drosophila hydei maneuvering about forward flight. The micro-quadrotor results show that the performance indices used to design an optimal flight control law for a micro-quadrotor may be recovered from the closed loop simulated flight trajectories, and the Drosophila results indicate that the combined effect of the insect longitudinal flight control sensing and feedback acts principally to regulate pitch rate.

Optimal control of malaria: combining vector interventions and drug therapies.

PubMed

Khamis, Doran; El Mouden, Claire; Kura, Klodeta; Bonsall, Michael B

2018-04-24

The sterile insect technique and transgenic equivalents are considered promising tools for controlling vector-borne disease in an age of increasing insecticide and drug-resistance. Combining vector interventions with artemisinin-based therapies may achieve the twin goals of suppressing malaria endemicity while managing artemisinin resistance. While the cost-effectiveness of these controls has been investigated independently, their combined usage has not been dynamically optimized in response to ecological and epidemiological processes. An optimal control framework based on coupled models of mosquito population dynamics and malaria epidemiology is used to investigate the cost-effectiveness of combining vector control with drug therapies in homogeneous environments with and without vector migration. The costs of endemic malaria are weighed against the costs of administering artemisinin therapies and releasing modified mosquitoes using various cost structures. Larval density dependence is shown to reduce the cost-effectiveness of conventional sterile insect releases compared with transgenic mosquitoes with a late-acting lethal gene. Using drug treatments can reduce the critical vector control release ratio necessary to cause disease fadeout. Combining vector control and drug therapies is the most effective and efficient use of resources, and using optimized implementation strategies can substantially reduce costs.

Multiple control strategies for prevention of avian influenza pandemic.

PubMed

Ullah, Roman; Zaman, Gul; Islam, Saeed

2014-01-01

We present the prevention of avian influenza pandemic by adjusting multiple control functions in the human-to-human transmittable avian influenza model. First we show the existence of the optimal control problem; then by using both analytical and numerical techniques, we investigate the cost-effective control effects for the prevention of transmission of disease. To do this, we use three control functions, the effort to reduce the number of contacts with human infected with mutant avian influenza, the antiviral treatment of infected individuals, and the effort to reduce the number of infected birds. We completely characterized the optimal control and compute numerical solution of the optimality system by using an iterative method.

Effects of Edge Directions on the Structural Controllability of Complex Networks

PubMed Central

Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

2015-01-01

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks. PMID:26281042

Optimal reorientation of asymmetric underactuated spacecraft using differential flatness and receding horizon control

NASA Astrophysics Data System (ADS)

Cai, Wei-wei; Yang, Le-ping; Zhu, Yan-wei

2015-01-01

This paper presents a novel method integrating nominal trajectory optimization and tracking for the reorientation control of an underactuated spacecraft with only two available control torque inputs. By employing a pseudo input along the uncontrolled axis, the flatness property of a general underactuated spacecraft is extended explicitly, by which the reorientation trajectory optimization problem is formulated into the flat output space with all the differential constraints eliminated. Ultimately, the flat output optimization problem is transformed into a nonlinear programming problem via the Chebyshev pseudospectral method, which is improved by the conformal map and barycentric rational interpolation techniques to overcome the side effects of the differential matrix's ill-conditions on numerical accuracy. Treating the trajectory tracking control as a state regulation problem, we develop a robust closed-loop tracking control law using the receding-horizon control method, and compute the feedback control at each control cycle rapidly via the differential transformation method. Numerical simulation results show that the proposed control scheme is feasible and effective for the reorientation maneuver.

Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes.

PubMed

Liu, Ping; Li, Guodong; Liu, Xinggao

2015-09-01

Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

PubMed

Hashim, H A; Abido, M A

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Instrumentation for optimizing an underground coal-gasification process

NASA Astrophysics Data System (ADS)

Seabaugh, W.; Zielinski, R. E.

1982-06-01

While the United States has a coal resource base of 6.4 trillion tons, only seven percent is presently recoverable by mining. The process of in-situ gasification can recover another twenty-eight percent of the vast resource, however, viable technology must be developed for effective in-situ recovery. The key to this technology is system that can optimize and control the process in real-time. An instrumentation system is described that optimizes the composition of the injection gas, controls the in-situ process and conditions the product gas for maximum utilization. The key elements of this system are Monsanto PRISM Systems, a real-time analytical system, and a real-time data acquisition and control system. This system provides from complete automation of the process but can easily be overridden by manual control. The use of this cost effective system can provide process optimization and is an effective element in developing a viable in-situ technology.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study

PubMed Central

Hashim, H. A.; Abido, M. A.

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. PMID:25960738

Impact and cost-effectiveness of snail control to achieve disease control targets for schistosomiasis.

PubMed

Lo, Nathan C; Gurarie, David; Yoon, Nara; Coulibaly, Jean T; Bendavid, Eran; Andrews, Jason R; King, Charles H

2018-01-23

Schistosomiasis is a parasitic disease that affects over 240 million people globally. To improve population-level disease control, there is growing interest in adding chemical-based snail control interventions to interrupt the lifecycle of Schistosoma in its snail host to reduce parasite transmission. However, this approach is not widely implemented, and given environmental concerns, the optimal conditions for when snail control is appropriate are unclear. We assessed the potential impact and cost-effectiveness of various snail control strategies. We extended previously published dynamic, age-structured transmission and cost-effectiveness models to simulate mass drug administration (MDA) and focal snail control interventions against Schistosoma haematobium across a range of low-prevalence (5-20%) and high-prevalence (25-50%) rural Kenyan communities. We simulated strategies over a 10-year period of MDA targeting school children or entire communities, snail control, and combined strategies. We measured incremental cost-effectiveness in 2016 US dollars per disability-adjusted life year and defined a strategy as optimally cost-effective when maximizing health gains (averted disability-adjusted life years) with an incremental cost-effectiveness below a Kenya-specific economic threshold. In both low- and high-prevalence settings, community-wide MDA with additional snail control reduced total disability by an additional 40% compared with school-based MDA alone. The optimally cost-effective scenario included the addition of snail control to MDA in over 95% of simulations. These results support inclusion of snail control in global guidelines and national schistosomiasis control strategies for optimal disease control, especially in settings with high prevalence, "hot spots" of transmission, and noncompliance to MDA. Copyright © 2018 the Author(s). Published by PNAS.

Impact and cost-effectiveness of snail control to achieve disease control targets for schistosomiasis

PubMed Central

Yoon, Nara; Coulibaly, Jean T.; Bendavid, Eran; Andrews, Jason R.; King, Charles H.

2018-01-01

Schistosomiasis is a parasitic disease that affects over 240 million people globally. To improve population-level disease control, there is growing interest in adding chemical-based snail control interventions to interrupt the lifecycle of Schistosoma in its snail host to reduce parasite transmission. However, this approach is not widely implemented, and given environmental concerns, the optimal conditions for when snail control is appropriate are unclear. We assessed the potential impact and cost-effectiveness of various snail control strategies. We extended previously published dynamic, age-structured transmission and cost-effectiveness models to simulate mass drug administration (MDA) and focal snail control interventions against Schistosoma haematobium across a range of low-prevalence (5–20%) and high-prevalence (25–50%) rural Kenyan communities. We simulated strategies over a 10-year period of MDA targeting school children or entire communities, snail control, and combined strategies. We measured incremental cost-effectiveness in 2016 US dollars per disability-adjusted life year and defined a strategy as optimally cost-effective when maximizing health gains (averted disability-adjusted life years) with an incremental cost-effectiveness below a Kenya-specific economic threshold. In both low- and high-prevalence settings, community-wide MDA with additional snail control reduced total disability by an additional 40% compared with school-based MDA alone. The optimally cost-effective scenario included the addition of snail control to MDA in over 95% of simulations. These results support inclusion of snail control in global guidelines and national schistosomiasis control strategies for optimal disease control, especially in settings with high prevalence, “hot spots” of transmission, and noncompliance to MDA. PMID:29301964

Optimal control for a tuberculosis model with undetected cases in Cameroon

NASA Astrophysics Data System (ADS)

Moualeu, D. P.; Weiser, M.; Ehrig, R.; Deuflhard, P.

2015-03-01

This paper considers the optimal control of tuberculosis through education, diagnosis campaign and chemoprophylaxis of latently infected. A mathematical model which includes important components such as undiagnosed infectious, diagnosed infectious, latently infected and lost-sight infectious is formulated. The model combines a frequency dependent and a density dependent force of infection for TB transmission. Through optimal control theory and numerical simulations, a cost-effective balance of two different intervention methods is obtained. Seeking to minimize the amount of money the government spends when tuberculosis remain endemic in the Cameroonian population, Pontryagin's maximum principle is used to characterize the optimal control. The optimality system is derived and solved numerically using the forward-backward sweep method (FBSM). Results provide a framework for designing cost-effective strategies for diseases with multiple intervention methods. It comes out that combining chemoprophylaxis and education, the burden of TB can be reduced by 80% in 10 years.

Optimal control on bladder cancer growth model with BCG immunotherapy and chemotherapy

NASA Astrophysics Data System (ADS)

Dewi, C.; Trisilowati

2015-03-01

In this paper, an optimal control model of the growth of bladder cancer with BCG (Basil Calmate Guerin) immunotherapy and chemotherapy is discussed. The purpose of this optimal control is to determine the number of BCG vaccine and drug should be given during treatment such that the growth of bladder cancer cells can be suppressed. Optimal control is obtained by applying Pontryagin principle. Furthermore, the optimal control problem is solved numerically using Forward-Backward Sweep method. Numerical simulations show the effectiveness of the vaccine and drug in controlling the growth of cancer cells. Hence, it can reduce the number of cancer cells that is not infected with BCG as well as minimize the cost of the treatment.

Effect of a road safety training program on drivers' comparative optimism.

PubMed

Perrissol, Stéphane; Smeding, Annique; Laumond, Francis; Le Floch, Valérie

2011-01-01

Reducing comparative optimism regarding risk perceptions in traffic accidents has been proven to be particularly difficult (Delhomme, 2000). This is unfortunate because comparative optimism is assumed to impede preventive action. The present study tested whether a road safety training course could reduce drivers' comparative optimism in high control situations. Results show that the training course efficiently reduced comparative optimism in high control, but not in low control situations. Mechanisms underlying this finding and implications for the design of road safety training courses are discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

DOE PAGES

Malikopoulos, Andreas

2015-01-01

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.more » Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.« less

The design of multirate digital control systems

NASA Technical Reports Server (NTRS)

Berg, M. C.

1986-01-01

The successive loop closures synthesis method is the only method for multirate (MR) synthesis in common use. A new method for MR synthesis is introduced which requires a gradient-search solution to a constrained optimization problem. Some advantages of this method are that the control laws for all control loops are synthesized simultaneously, taking full advantage of all cross-coupling effects, and that simple, low-order compensator structures are easily accomodated. The algorithm and associated computer program for solving the constrained optimization problem are described. The successive loop closures , optimal control, and constrained optimization synthesis methods are applied to two example design problems. A series of compensator pairs are synthesized for each example problem. The succesive loop closure, optimal control, and constrained optimization synthesis methods are compared, in the context of the two design problems.

USMC Inventory Control Using Optimization Modeling and Discrete Event Simulation

DTIC Science & Technology

2016-09-01

release. Distribution is unlimited. USMC INVENTORY CONTROL USING OPTIMIZATION MODELING AND DISCRETE EVENT SIMULATION by Timothy A. Curling...USING OPTIMIZATION MODELING AND DISCRETE EVENT SIMULATION 5. FUNDING NUMBERS 6. AUTHOR(S) Timothy A. Curling 7. PERFORMING ORGANIZATION NAME(S...optimization and discrete -event simulation. This construct can potentially provide an effective means in improving order management decisions. However

Real Time Optimal Control of Supercapacitor Operation for Frequency Response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Yusheng; Panwar, Mayank; Mohanpurkar, Manish

2016-07-01

Supercapacitors are gaining wider applications in power systems due to fast dynamic response. Utilizing supercapacitors by means of power electronics interfaces for power compensation is a proven effective technique. For applications such as requency restoration if the cost of supercapacitors maintenance as well as the energy loss on the power electronics interfaces are addressed. It is infeasible to use traditional optimization control methods to mitigate the impacts of frequent cycling. This paper proposes a Front End Controller (FEC) using Generalized Predictive Control featuring real time receding optimization. The optimization constraints are based on cost and thermal management to enhance tomore » the utilization efficiency of supercapacitors. A rigorous mathematical derivation is conducted and test results acquired from Digital Real Time Simulator are provided to demonstrate effectiveness.« less

Optimal Corrosion Control Treatment Evaluation Technical Recommendations

EPA Pesticide Factsheets

This document provides technical recommendations that both systems and primacy agencies can use to comply with LCR CCT requirements and effective evaluation and designation of optimal corrosion control treatment (OCCT).

Hodograph analysis in aircraft trajectory optimization

NASA Technical Reports Server (NTRS)

Cliff, Eugene M.; Seywald, Hans; Bless, Robert R.

1993-01-01

An account is given of key geometrical concepts involved in the use of a hodograph as an optimal control theory resource which furnishes a framework for geometrical interpretation of the minimum principle. Attention is given to the effects of different convexity properties on the hodograph, which bear on the existence of solutions and such types of controls as chattering controls, 'bang-bang' control, and/or singular control. Illustrative aircraft trajectory optimization problems are examined in view of this use of the hodograph.

Adaptive disturbance compensation finite control set optimal control for PMSM systems based on sliding mode extended state observer

NASA Astrophysics Data System (ADS)

Wu, Yun-jie; Li, Guo-fei

2018-01-01

Based on sliding mode extended state observer (SMESO) technique, an adaptive disturbance compensation finite control set optimal control (FCS-OC) strategy is proposed for permanent magnet synchronous motor (PMSM) system driven by voltage source inverter (VSI). So as to improve robustness of finite control set optimal control strategy, a SMESO is proposed to estimate the output-effect disturbance. The estimated value is fed back to finite control set optimal controller for implementing disturbance compensation. It is indicated through theoretical analysis that the designed SMESO could converge in finite time. The simulation results illustrate that the proposed adaptive disturbance compensation FCS-OC possesses better dynamical response behavior in the presence of disturbance.

Optimal Control Surface Layout for an Aeroservoelastic Wingbox

NASA Technical Reports Server (NTRS)

Stanford, Bret K.

2017-01-01

This paper demonstrates a technique for locating the optimal control surface layout of an aeroservoelastic Common Research Model wingbox, in the context of maneuver load alleviation and active utter suppression. The combinatorial actuator layout design is solved using ideas borrowed from topology optimization, where the effectiveness of a given control surface is tied to a layout design variable, which varies from zero (the actuator is removed) to one (the actuator is retained). These layout design variables are optimized concurrently with a large number of structural wingbox sizing variables and control surface actuation variables, in order to minimize the sum of structural weight and actuator weight. Results are presented that demonstrate interdependencies between structural sizing patterns and optimal control surface layouts, for both static and dynamic aeroelastic physics.

Psychological Effects of Group Hypnotherapy on Breast Cancer Patients During Chemotherapy.

PubMed

Téllez, Arnoldo; Rodríguez-Padilla, Cristina; Martínez-Rodríguez, Jorge Luis; Juárez-García, Dehisy M; Sanchez-Armass, Omar; Sánchez, Teresa; Segura, Guillermo; Jaime-Bernal, Leticia

2017-07-01

The purpose of this study was to evaluate the effect of group hypnotherapy on anxiety, depression, stress, self-esteem, optimism, and social support during chemotherapy, in patients with breast cancer, compared with a control group with standard medical care. Hypnotherapy consisted of 24 sessions that included suggestions to encourage relaxation, self-esteem, the resolution of past traumatic events, physical healing, and optimism. Results show that the hypnotherapy group significantly decreased anxiety, distress, increased self-esteem, and optimism in the first 12 sessions. However, at the end of the 24 sessions, only self-esteem and optimism remained significant compared with the control group. The convenience of using hypnotherapy to encourage optimism and self-esteem in patients with breast cancer during chemotherapy treatment is discussed given its protective effect on health.

Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms.

PubMed

Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali

2018-05-11

The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal preview control for a linear continuous-time stochastic control system in finite-time horizon

NASA Astrophysics Data System (ADS)

Wu, Jiang; Liao, Fucheng; Tomizuka, Masayoshi

2017-01-01

This paper discusses the design of the optimal preview controller for a linear continuous-time stochastic control system in finite-time horizon, using the method of augmented error system. First, an assistant system is introduced for state shifting. Then, in order to overcome the difficulty of the state equation of the stochastic control system being unable to be differentiated because of Brownian motion, the integrator is introduced. Thus, the augmented error system which contains the integrator vector, control input, reference signal, error vector and state of the system is reconstructed. This leads to the tracking problem of the optimal preview control of the linear stochastic control system being transformed into the optimal output tracking problem of the augmented error system. With the method of dynamic programming in the theory of stochastic control, the optimal controller with previewable signals of the augmented error system being equal to the controller of the original system is obtained. Finally, numerical simulations show the effectiveness of the controller.

Vaccination and treatment as control interventions in an infectious disease model with their cost optimization

NASA Astrophysics Data System (ADS)

Kumar, Anuj; Srivastava, Prashant K.

2017-03-01

In this work, an optimal control problem with vaccination and treatment as control policies is proposed and analysed for an SVIR model. We choose vaccination and treatment as control policies because both these interventions have their own practical advantage and ease in implementation. Also, they are widely applied to control or curtail a disease. The corresponding total cost incurred is considered as weighted combination of costs because of opportunity loss due to infected individuals and costs incurred in providing vaccination and treatment. The existence of optimal control paths for the problem is established and guaranteed. Further, these optimal paths are obtained analytically using Pontryagin's Maximum Principle. We analyse our results numerically to compare three important strategies of proposed controls, viz.: vaccination only; with both treatment and vaccination; and treatment only. We note that first strategy (vaccination only) is less effective as well as expensive. Though, for a highly effective vaccine, vaccination alone may also work well in comparison with treatment only strategy. Among all the strategies, we observe that implementation of both treatment and vaccination is most effective and less expensive. Moreover, in this case the infective population is found to be relatively very low. Thus, we conclude that the comprehensive effect of vaccination and treatment not only minimizes cost burden due to opportunity loss and applied control policies but also keeps a tab on infective population.

Perceived Academic Control: Mediating the Effects of Optimism and Social Support on College Students' Psychological Health

ERIC Educational Resources Information Center

Ruthig, Joelle C.; Haynes, Tara L.; Stupnisky, Robert H.; Perry, Raymond P.

2009-01-01

The first year of college presents numerous challenges experienced as overwhelming by some freshmen who may become overly stressed and depressed. This longitudinal study examined perceived academic control (PAC) as a mediator of optimism and social support's buffering effects on freshman students' psychological health. Multiple regressions…

Advanced launch system trajectory optimization using suboptimal control

NASA Technical Reports Server (NTRS)

Shaver, Douglas A.; Hull, David G.

1993-01-01

The maximum-final mass trajectory of a proposed configuration of the Advanced Launch System is presented. A model for the two-stage rocket is given; the optimal control problem is formulated as a parameter optimization problem; and the optimal trajectory is computed using a nonlinear programming code called VF02AD. Numerical results are presented for the controls (angle of attack and velocity roll angle) and the states. After the initial rotation, the angle of attack goes to a positive value to keep the trajectory as high as possible, returns to near zero to pass through the transonic regime and satisfy the dynamic pressure constraint, returns to a positive value to keep the trajectory high and to take advantage of minimum drag at positive angle of attack due to aerodynamic shading of the booster, and then rolls off to negative values to satisfy the constraints. Because the engines cannot be throttled, the maximum dynamic pressure occurs at a single point; there is no maximum dynamic pressure subarc. To test approximations for obtaining analytical solutions for guidance, two additional optimal trajectories are computed: one using untrimmed aerodynamics and one using no atmospheric effects except for the dynamic pressure constraint. It is concluded that untrimmed aerodynamics has a negligible effect on the optimal trajectory and that approximate optimal controls should be able to be obtained by treating atmospheric effects as perturbations.

Memory control beliefs and everyday forgetfulness in adulthood: the effects of selection, optimization, and compensation strategies.

PubMed

Scheibner, Gunnar Benjamin; Leathem, Janet

2012-01-01

Controlling for age, gender, education, and self-rated health, the present study used regression analyses to examine the relationships between memory control beliefs and self-reported forgetfulness in the context of the meta-theory of Selective Optimization with Compensation (SOC). Findings from this online survey (N = 409) indicate that, among adult New Zealanders, a higher sense of memory control accounts for a 22.7% reduction in self-reported forgetfulness. Similarly, optimization was found to account for a 5% reduction in forgetfulness while the strategies of selection and compensation were not related to self-reports of forgetfulness. Optimization partially mediated the beneficial effects that some memory beliefs (e.g., believing that memory decline is inevitable and believing in the potential for memory improvement) have on forgetfulness. It was concluded that memory control beliefs are important predictors of self-reported forgetfulness while the support for the SOC model in the context of memory controllability and everyday forgetfulness is limited.

Optimality, stochasticity, and variability in motor behavior

PubMed Central

Guigon, Emmanuel; Baraduc, Pierre; Desmurget, Michel

2008-01-01

Recent theories of motor control have proposed that the nervous system acts as a stochastically optimal controller, i.e. it plans and executes motor behaviors taking into account the nature and statistics of noise. Detrimental effects of noise are converted into a principled way of controlling movements. Attractive aspects of such theories are their ability to explain not only characteristic features of single motor acts, but also statistical properties of repeated actions. Here, we present a critical analysis of stochastic optimality in motor control which reveals several difficulties with this hypothesis. We show that stochastic control may not be necessary to explain the stochastic nature of motor behavior, and we propose an alternative framework, based on the action of a deterministic controller coupled with an optimal state estimator, which relieves drawbacks of stochastic optimality and appropriately explains movement variability. PMID:18202922

Optimization of shape control of a cantilever beam using dielectric elastomer actuators

NASA Astrophysics Data System (ADS)

Liu, Chong; Mao, Boyong; Huang, Gangting; Wu, Qichen; Xie, Shilin; Xu, Minglong

2018-05-01

Dielectric elastomer (DE) is a kind of smart soft material that has many advantages such as large deformation, fast response, lightweight and easy synthesis. These features make dielectric elastomer a suitable material for actuators. This article focuses on the shape control of a cantilever beam by using dielectric elastomer actuators. The shape control equation in finite element formulation of the cantilever beam partially covered with dielectric elastomer actuators is derived based on the constitutive equation of dielectric elastomer material by using Hamilton principle. The actuating forces produced by dielectric elastomer actuators depend on the number of layers, the position and the actuation voltage of dielectric elastomer actuators. First, effects of these factors on the shape control accuracy when one pair or multiple pairs of actuators are employed are simulated, respectively. The simulation results demonstrate that increasing the number of actuators or the number of layers can improve the control effect and reduce the actuation voltages effectively. Second, to achieve the optimal shape control effect, the position of the actuators and the drive voltages are all determined using a genetic algorithm. The robustness of the genetic algorithm is analyzed. Moreover, the implications of using one pair and multiple pairs of actuators to drive the cantilever beam to the expected shape are investigated. The results demonstrate that a small number of actuators with optimal placement and optimal voltage values can achieve the shape control of the beam effectively. Finally, a preliminary experimental verification of the control effect is carried out, which shows the correctness of the theoretical method.

Optimal strategies for the control of autonomous vehicles in data assimilation

NASA Astrophysics Data System (ADS)

McDougall, D.; Moore, R. O.

2017-08-01

We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for the locally optimal control algorithm presented here, with objective formed from the variance trace of the expected posterior distribution. We present results for linear flows near hyperbolic fixed points.

Computer optimization techniques for NASA Langley's CSI evolutionary model's real-time control system

NASA Technical Reports Server (NTRS)

Elliott, Kenny B.; Ugoletti, Roberto; Sulla, Jeff

1992-01-01

The evolution and optimization of a real-time digital control system is presented. The control system is part of a testbed used to perform focused technology research on the interactions of spacecraft platform and instrument controllers with the flexible-body dynamics of the platform and platform appendages. The control system consists of Computer Automated Measurement and Control (CAMAC) standard data acquisition equipment interfaced to a workstation computer. The goal of this work is to optimize the control system's performance to support controls research using controllers with up to 50 states and frame rates above 200 Hz. The original system could support a 16-state controller operating at a rate of 150 Hz. By using simple yet effective software improvements, Input/Output (I/O) latencies and contention problems are reduced or eliminated in the control system. The final configuration can support a 16-state controller operating at 475 Hz. Effectively the control system's performance was increased by a factor of 3.

The Analysis of Fixed Final State Optimal Control in Bilinear System Applied to Bone Marrow by Cell-Cycle Specific (CCS) Chemotherapy

NASA Astrophysics Data System (ADS)

Rainarli, E.; E Dewi, K.

2017-04-01

The research conducted by Fister & Panetta shown an optimal control model of bone marrow cells against Cell Cycle Specific chemotherapy drugs. The model used was a bilinear system model. Fister & Panetta research has proved existence, uniqueness, and characteristics of optimal control (the chemotherapy effect). However, by using this model, the amount of bone marrow at the final time could achieve less than 50 percent from the amount of bone marrow before given treatment. This could harm patients because the lack of bone marrow cells made the number of leukocytes declining and patients will experience leukemia. This research would examine the optimal control of a bilinear system that applied to fixed final state. It will be used to determine the length of optimal time in administering chemotherapy and kept bone marrow cells on the allowed level at the same time. Before simulation conducted, this paper shows that the system could be controlled by using a theory of Lie Algebra. Afterward, it shows the characteristics of optimal control. Based on the simulation, it indicates that strong chemotherapy drug given in a short time frame is the most optimal condition to keep bone marrow cells spine on the allowed level but still could put playing an effective treatment. It gives preference of the weight of treatment for keeping bone marrow cells. The result of chemotherapy’s effect (u) is not able to reach the maximum value. On the other words, it needs to make adjustments of medicine’s dosage to satisfy the final treatment condition e.g. the number of bone marrow cells should be at the allowed level.

Implications of the degree of controllability of controlled plants in the sense of LQR optimal control

NASA Astrophysics Data System (ADS)

Xia, Yaping; Yin, Minghui; Zou, Yun

2018-01-01

In this paper, the relationship between the degree of controllability (DOC) of controlled plants and the corresponding quadratic optimal performance index in LQR control is investigated for the electro-hydraulic synchronising servo control systems and wind turbine systems, respectively. It is shown that for these two types of systems, the higher the DOC of a controlled plant is, the better the quadratic optimal performance index is. It implies that in some LQR controller designs, the measure of the DOC of a controlled plant can be used as an index for the optimisation of adjustable plant parameters, by which the plant can be controlled more effectively.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

PubMed

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

Epidemic spreading on random surfer networks with optimal interaction radius

NASA Astrophysics Data System (ADS)

Feng, Yun; Ding, Li; Hu, Ping

2018-03-01

In this paper, the optimal control problem of epidemic spreading on random surfer heterogeneous networks is considered. An epidemic spreading model is established according to the classification of individual's initial interaction radii. Then, a control strategy is proposed based on adjusting individual's interaction radii. The global stability of the disease free and endemic equilibrium of the model is investigated. We prove that an optimal solution exists for the optimal control problem and the explicit form of which is presented. Numerical simulations are conducted to verify the correctness of the theoretical results. It is proved that the optimal control strategy is effective to minimize the density of infected individuals and the cost associated with the adjustment of interaction radii.

Sustainability and optimal control of an exploited prey predator system through provision of alternative food to predator.

PubMed

Kar, T K; Ghosh, Bapan

2012-08-01

In the present paper, we develop a simple two species prey-predator model in which the predator is partially coupled with alternative prey. The aim is to study the consequences of providing additional food to the predator as well as the effects of harvesting efforts applied to both the species. It is observed that the provision of alternative food to predator is not always beneficial to the system. A complete picture of the long run dynamics of the system is discussed based on the effort pair as control parameters. Optimal augmentations of prey and predator biomass at final time have been investigated by optimal control theory. Also the short and large time effects of the application of optimal control have been discussed. Finally, some numerical illustrations are given to verify our analytical results with the help of different sets of parameters. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Optimal Micro-Vane Flow Control for Compact Air Vehicle Inlets

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Miller, Daniel N.; Addington, Gregory A.; Agrell, Johan

2004-01-01

The purpose of this study on micro-vane secondary flow control is to demonstrate the viability and economy of Response Surface Methodology (RSM) to optimally design micro-vane secondary flow control arrays, and to establish that the aeromechanical effects of engine face distortion can also be included in the design and optimization process. These statistical design concepts were used to investigate the design characteristics of "low unit strength" micro-effector arrays. "Low unit strength" micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. Therefore, this report examines optimal micro-vane secondary flow control array designs for compact inlets through a Response Surface Methodology.

Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach.

PubMed

Liu, Derong; Wang, Ding; Li, Hongliang

2014-02-01

In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.

A multilevel control system for the large space telescope. [numerical analysis/optimal control

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Sundareshan, S. K.; Vukcevic, M. B.

1975-01-01

A multilevel scheme was proposed for control of Large Space Telescope (LST) modeled by a three-axis-six-order nonlinear equation. Local controllers were used on the subsystem level to stabilize motions corresponding to the three axes. Global controllers were applied to reduce (and sometimes nullify) the interactions among the subsystems. A multilevel optimization method was developed whereby local quadratic optimizations were performed on the subsystem level, and global control was again used to reduce (nullify) the effect of interactions. The multilevel stabilization and optimization methods are presented as general tools for design and then used in the design of the LST Control System. The methods are entirely computerized, so that they can accommodate higher order LST models with both conceptual and numerical advantages over standard straightforward design techniques.

An optimal control strategies using vaccination and fogging in dengue fever transmission model

NASA Astrophysics Data System (ADS)

Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan

2017-08-01

This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging optimally, the number of vector can be minimized. In this case, we considered the giving of vaccination as a control variable because it is one of the efforts that are being developed to reduce the spreading of dengue fever. We used Pontryagin Minimum Principle to solve the optimal control problem. Furthermore, the numerical simulation results are given to show the effect of the optimal control strategies in order to minimize the epidemic of dengue fever.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Multi-disciplinary optimization of aeroservoelastic systems

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1990-01-01

Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process which allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum State aerodynamic approximations and dynamic residualization methods are used to construct a high accuracy, low order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins and control effectiveness with respect to structural and control design variables are presented. The performance requirements are utilized by equality constraints which affect the sensitivity derivatives. A gradient-based optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.

Multidisciplinary optimization of aeroservoelastic systems using reduced-size models

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1992-01-01

Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process which allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum State aerodynamic approximations and dynamic residualization methods are used to construct a high accuracy, low order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins and control effectiveness with respect to structural and control design variables are presented. The performance requirements are utilized by equality constraints which affect the sensitivity derivatives. A gradient-based optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.

Combined control-structure optimization

NASA Technical Reports Server (NTRS)

Salama, M.; Milman, M.; Bruno, R.; Scheid, R.; Gibson, S.

1989-01-01

An approach for combined control-structure optimization keyed to enhancing early design trade-offs is outlined and illustrated by numerical examples. The approach employs a homotopic strategy and appears to be effective for generating families of designs that can be used in these early trade studies. Analytical results were obtained for classes of structure/control objectives with linear quadratic Gaussian (LQG) and linear quadratic regulator (LQR) costs. For these, researchers demonstrated that global optima can be computed for small values of the homotopy parameter. Conditions for local optima along the homotopy path were also given. Details of two numerical examples employing the LQR control cost were given showing variations of the optimal design variables along the homotopy path. The results of the second example suggest that introducing a second homotopy parameter relating the two parts of the control index in the LQG/LQR formulation might serve to enlarge the family of Pareto optima, but its effect on modifying the optimal structural shapes may be analogous to the original parameter lambda.

Chaos control in solar fed DC-DC boost converter by optimal parameters using nelder-mead algorithm powered enhanced BFOA

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Rajasekar, N.; Akhil, Saya; Jyotheeswara Reddy, K.

2017-11-01

The boost converter is the most desirable DC-DC power converter for renewable energy applications for its favorable continuous input current characteristics. In other hand, these DC-DC converters known as practical nonlinear systems are prone to several types of nonlinear phenomena including bifurcation, quasiperiodicity, intermittency and chaos. These undesirable effects has to be controlled for maintaining normal periodic operation of the converter and to ensure the stability. This paper presents an effective solution to control the chaos in solar fed DC-DC boost converter since the converter experiences wide range of input power variation which leads to chaotic phenomena. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Nelder-Mead Enhanced Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The results are compared with the traditional methods. The obtained results of the proposed system ensures the operation of the converter within the controllable region.

The Inverse Optimal Control Problem for a Three-Loop Missile Autopilot

NASA Astrophysics Data System (ADS)

Hwang, Donghyeok; Tahk, Min-Jea

2018-04-01

The performance characteristics of the autopilot must have a fast response to intercept a maneuvering target and reasonable robustness for system stability under the effect of un-modeled dynamics and noise. By the conventional approach, the three-loop autopilot design is handled by time constant, damping factor and open-loop crossover frequency to achieve the desired performance requirements. Note that the general optimal theory can be also used to obtain the same gain as obtained from the conventional approach. The key idea of using optimal control technique for feedback gain design revolves around appropriate selection and interpretation of the performance index for which the control is optimal. This paper derives an explicit expression, which relates the weight parameters appearing in the quadratic performance index to the design parameters such as open-loop crossover frequency, phase margin, damping factor, or time constant, etc. Since all set of selection of design parameters do not guarantee existence of optimal control law, explicit inequalities, which are named the optimality criteria for the three-loop autopilot (OC3L), are derived to find out all set of design parameters for which the control law is optimal. Finally, based on OC3L, an efficient gain selection procedure is developed, where time constant is set to design objective and open-loop crossover frequency and phase margin as design constraints. The effectiveness of the proposed technique is illustrated through numerical simulations.

Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control: Maximization of wind plant AEP by optimization of layout and wake control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gebraad, Pieter; Thomas, Jared J.; Ning, Andrew

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power productionmore » with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses.« less

Performance of Optimized Actuator and Sensor Arrays in an Active Noise Control System

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.; Lyle, K. H.; Cline, J. H.; Cabell, R. H.

1996-01-01

Experiments have been conducted in NASA Langley's Acoustics and Dynamics Laboratory to determine the effectiveness of optimized actuator/sensor architectures and controller algorithms for active control of harmonic interior noise. Tests were conducted in a large scale fuselage model - a composite cylinder which simulates a commuter class aircraft fuselage with three sections of trim panel and a floor. Using an optimization technique based on the component transfer functions, combinations of 4 out of 8 piezoceramic actuators and 8 out of 462 microphone locations were evaluated against predicted performance. A combinatorial optimization technique called tabu search was employed to select the optimum transducer arrays. Three test frequencies represent the cases of a strong acoustic and strong structural response, a weak acoustic and strong structural response and a strong acoustic and weak structural response. Noise reduction was obtained using a Time Averaged/Gradient Descent (TAGD) controller. Results indicate that the optimization technique successfully predicted best and worst case performance. An enhancement of the TAGD control algorithm was also evaluated. The principal components of the actuator/sensor transfer functions were used in the PC-TAGD controller. The principal components are shown to be independent of each other while providing control as effective as the standard TAGD.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

NASA Astrophysics Data System (ADS)

Nazemizadeh, M.; Rahimi, H. N.; Amini Khoiy, K.

2012-03-01

This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange's principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.

The Effects of an Emotion Strengthening Training Program on the Optimism Level of Nurses

ERIC Educational Resources Information Center

Balci Celik, Seher

2008-01-01

The aim of this study is to investigate the effects of emotion strengthening as a training programme for optimism for nurses. The experimental and control group of this research was totally composed of 20 nurses. A pre-test post-test research model with control group was used in this research. Nurses' optimistm levels have been measured by…

Health benefit modelling and optimization of vehicular pollution control strategies

NASA Astrophysics Data System (ADS)

Sonawane, Nayan V.; Patil, Rashmi S.; Sethi, Virendra

2012-12-01

This study asserts that the evaluation of pollution reduction strategies should be approached on the basis of health benefits. The framework presented could be used for decision making on the basis of cost effectiveness when the strategies are applied concurrently. Several vehicular pollution control strategies have been proposed in literature for effective management of urban air pollution. The effectiveness of these strategies has been mostly studied as a one at a time approach on the basis of change in pollution concentration. The adequacy and practicality of such an approach is studied in the present work. Also, the assessment of respective benefits of these strategies has been carried out when they are implemented simultaneously. An integrated model has been developed which can be used as a tool for optimal prioritization of various pollution management strategies. The model estimates health benefits associated with specific control strategies. ISC-AERMOD View has been used to provide the cause-effect relation between control options and change in ambient air quality. BenMAP, developed by U.S. EPA, has been applied for estimation of health and economic benefits associated with various management strategies. Valuation of health benefits has been done for impact indicators of premature mortality, hospital admissions and respiratory syndrome. An optimization model has been developed to maximize overall social benefits with determination of optimized percentage implementations for multiple strategies. The model has been applied for sub-urban region of Mumbai city for vehicular sector. Several control scenarios have been considered like revised emission standards, electric, CNG, LPG and hybrid vehicles. Reduction in concentration and resultant health benefits for the pollutants CO, NOx and particulate matter are estimated for different control scenarios. Finally, an optimization model has been applied to determine optimized percentage implementation of specific control strategies with maximization of social benefits, when these strategies are applied simultaneously.

The near optimality of the stabilizing control in a weakly nonlinear system with state-dependent coefficients

NASA Astrophysics Data System (ADS)

Dmitriev, Mikhail G.; Makarov, Dmitry A.

2016-08-01

We carried out analysis of near optimality of one computationally effective nonlinear stabilizing control built for weakly nonlinear systems with coefficients depending on the state and the formal small parameter. First investigation of that problem was made in [M. G. Dmitriev, and D. A. Makarov, "The suboptimality of stabilizing regulator in a quasi-linear system with state-depended coefficients," in 2016 International Siberian Conference on Control and Communications (SIBCON) Proceedings, National Research University, Moscow, 2016]. In this paper, another optimal control and gain matrix representations were used and theoretical results analogous to cited work above were obtained. Also as in the cited work above the form of quality criterion on which this close-loop control is optimal was constructed.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

Choosing Sensor Configuration for a Flexible Structure Using Full Control Synthesis

NASA Technical Reports Server (NTRS)

Lind, Rick; Nalbantoglu, Volkan; Balas, Gary

1997-01-01

Optimal locations and types for feedback sensors which meet design constraints and control requirements are difficult to determine. This paper introduces an approach to choosing a sensor configuration based on Full Control synthesis. A globally optimal Full Control compensator is computed for each member of a set of sensor configurations which are feasible for the plant. The sensor configuration associated with the Full Control system achieving the best closed-loop performance is chosen for feedback measurements to an output feedback controller. A flexible structure is used as an example to demonstrate this procedure. Experimental results show sensor configurations chosen to optimize the Full Control performance are effective for output feedback controllers.

Aerodynamic shape optimization of wing and wing-body configurations using control theory

NASA Technical Reports Server (NTRS)

Reuther, James; Jameson, Antony

1995-01-01

This paper describes the implementation of optimization techniques based on control theory for wing and wing-body design. In previous studies it was shown that control theory could be used to devise an effective optimization procedure for airfoils and wings in which the shape and the surrounding body-fitted mesh are both generated analytically, and the control is the mapping function. Recently, the method has been implemented for both potential flows and flows governed by the Euler equations using an alternative formulation which employs numerically generated grids, so that it can more easily be extended to treat general configurations. Here results are presented both for the optimization of a swept wing using an analytic mapping, and for the optimization of wing and wing-body configurations using a general mesh.

Optimal Control for Fast and Robust Generation of Entangled States in Anisotropic Heisenberg Chains

NASA Astrophysics Data System (ADS)

Zhang, Xiong-Peng; Shao, Bin; Zou, Jian

2017-05-01

Motivated by some recent results of the optimal control (OC) theory, we study anisotropic XXZ Heisenberg spin-1/2 chains with control fields acting on a single spin, with the aim of exploring how maximally entangled state can be prepared. To achieve the goal, we use a numerical optimization algorithm (e.g., the Krotov algorithm, which was shown to be capable of reaching the quantum speed limit) to search an optimal set of control parameters, and then obtain OC pulses corresponding to the target fidelity. We find that the minimum time for implementing our target state depending on the anisotropy parameter Δ of the model. Finally, we analyze the robustness of the obtained results for the optimal fidelities and the effectiveness of the Krotov method under some realistic conditions.

An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Krishnakumar, Kalmanje; Boskovic, Jovan

2008-01-01

This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.

Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

PubMed

Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

2017-01-01

In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.

Optimized method for manufacturing large aspheric surfaces

NASA Astrophysics Data System (ADS)

Zhou, Xusheng; Li, Shengyi; Dai, Yifan; Xie, Xuhui

2007-12-01

Aspheric optics are being used more and more widely in modern optical systems, due to their ability of correcting aberrations, enhancing image quality, enlarging the field of view and extending the range of effect, while reducing the weight and volume of the system. With optical technology development, we have more pressing requirement to large-aperture and high-precision aspheric surfaces. The original computer controlled optical surfacing (CCOS) technique cannot meet the challenge of precision and machining efficiency. This problem has been thought highly of by researchers. Aiming at the problem of original polishing process, an optimized method for manufacturing large aspheric surfaces is put forward. Subsurface damage (SSD), full aperture errors and full band of frequency errors are all in control of this method. Lesser SSD depth can be gained by using little hardness tool and small abrasive grains in grinding process. For full aperture errors control, edge effects can be controlled by using smaller tools and amendment model with material removal function. For full band of frequency errors control, low frequency errors can be corrected with the optimized material removal function, while medium-high frequency errors by using uniform removing principle. With this optimized method, the accuracy of a K9 glass paraboloid mirror can reach rms 0.055 waves (where a wave is 0.6328μm) in a short time. The results show that the optimized method can guide large aspheric surface manufacturing effectively.

Optimal control of LQR for discrete time-varying systems with input delays

NASA Astrophysics Data System (ADS)

Yin, Yue-Zhu; Yang, Zhong-Lian; Yin, Zhi-Xiang; Xu, Feng

2018-04-01

In this work, we consider the optimal control problem of linear quadratic regulation for discrete time-variant systems with single input and multiple input delays. An innovative and simple method to derive the optimal controller is given. The studied problem is first equivalently converted into a problem subject to a constraint condition. Last, with the established duality, the problem is transformed into a static mathematical optimisation problem without input delays. The optimal control input solution to minimise performance index function is derived by solving this optimisation problem with two methods. A numerical simulation example is carried out and its results show that our two approaches are both feasible and very effective.

Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System

NASA Astrophysics Data System (ADS)

Bendjeghaba, Omar

2014-01-01

This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Optimal control of 2-wheeled mobile robot at energy performance index

NASA Astrophysics Data System (ADS)

Kaliński, Krzysztof J.; Mazur, Michał

2016-03-01

The paper presents the application of the optimal control method at the energy performance index towards motion control of the 2-wheeled mobile robot. With the use of the proposed method of control the 2-wheeled mobile robot can realise effectively the desired trajectory. The problem of motion control of mobile robots is usually neglected and thus performance of the realisation of the high level control tasks is limited.

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Optimized Controller Design for a 12-Pulse Voltage Source Converter Based HVDC System

NASA Astrophysics Data System (ADS)

Agarwal, Ruchi; Singh, Sanjeev

2017-12-01

The paper proposes an optimized controller design scheme for power quality improvement in 12-pulse voltage source converter based high voltage direct current system. The proposed scheme is hybrid combination of golden section search and successive linear search method. The paper aims at reduction of current sensor and optimization of controller. The voltage and current controller parameters are selected for optimization due to its impact on power quality. The proposed algorithm for controller optimizes the objective function which is composed of current harmonic distortion, power factor, and DC voltage ripples. The detailed designs and modeling of the complete system are discussed and its simulation is carried out in MATLAB-Simulink environment. The obtained results are presented to demonstrate the effectiveness of the proposed scheme under different transient conditions such as load perturbation, non-linear load condition, voltage sag condition, and tapped load fault under one phase open condition at both points-of-common coupling.

Mixed H(2)/H(sub infinity): Control with output feedback compensators using parameter optimization

NASA Technical Reports Server (NTRS)

Schoemig, Ewald; Ly, Uy-Loi

1992-01-01

Among the many possible norm-based optimization methods, the concept of H-infinity optimal control has gained enormous attention in the past few years. Here the H-infinity framework, based on the Small Gain Theorem and the Youla Parameterization, effectively treats system uncertainties in the control law synthesis. A design approach involving a mixed H(sub 2)/H-infinity norm strives to combine the advantages of both methods. This advantage motivates researchers toward finding solutions to the mixed H(sub 2)/H-infinity control problem. The approach developed in this research is based on a finite time cost functional that depicts an H-infinity bound control problem in a H(sub 2)-optimization setting. The goal is to define a time-domain cost function that optimizes the H(sub 2)-norm of a system with an H-infinity-constraint function.

Mixed H2/H(infinity)-Control with an output-feedback compensator using parameter optimization

NASA Technical Reports Server (NTRS)

Schoemig, Ewald; Ly, Uy-Loi

1992-01-01

Among the many possible norm-based optimization methods, the concept of H-infinity optimal control has gained enormous attention in the past few years. Here the H-infinity framework, based on the Small Gain Theorem and the Youla Parameterization, effectively treats system uncertainties in the control law synthesis. A design approach involving a mixed H(sub 2)/H-infinity norm strives to combine the advantages of both methods. This advantage motivates researchers toward finding solutions to the mixed H(sub 2)/H-infinity control problem. The approach developed in this research is based on a finite time cost functional that depicts an H-infinity bound control problem in a H(sub 2)-optimization setting. The goal is to define a time-domain cost function that optimizes the H(sub 2)-norm of a system with an H-infinity-constraint function.

Modeling the role of information and limited optimal treatment on disease prevalence.

PubMed

Kumar, Anuj; Srivastava, Prashant K; Takeuchi, Yasuhiro

2017-02-07

Disease outbreaks induce behavioural changes in healthy individuals to avoid contracting infection. We first propose a compartmental model which accounts for the effect of individual's behavioural response due to information of the disease prevalence. It is assumed that the information is growing as a function of infective population density that saturates at higher density of infective population and depends on active educational and social programmes. Model analysis has been performed and the global stability of equilibrium points is established. Further, choosing the treatment (a pharmaceutical intervention) and the effect of information (a non-pharmaceutical intervention) as controls, an optimal control problem is formulated to minimize the cost and disease fatality. In the cost functional, the nonlinear effect of controls is accounted. Analytical characterization of optimal control paths is done with the help of Pontryagin's Maximum Principle. Numerical findings suggest that if only control via information is used, it is effective and economical for early phase of disease spread whereas treatment works well for long term control except for initial phase. Furthermore, we observe that the effect of information induced behavioural response plays a crucial role in the absence of pharmaceutical control. Moreover, comprehensive use of both the control interventions is more effective than any single applied control policy and it reduces the number of infective individuals and minimizes the economic cost generated from disease burden and applied controls. Thus, the combined effect of both the control policies is found more economical during the entire epidemic period whereas the implementation of a single policy is not found economically viable. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal teaching strategy in periodic impulsive knowledge dissemination system.

PubMed

Liu, Dan-Qing; Wu, Zhen-Qiang; Wang, Yu-Xin; Guo, Qiang; Liu, Jian-Guo

2017-01-01

Accurately describing the knowledge dissemination process is significant to enhance the performance of personalized education. In this study, considering the effect of periodic teaching activities on the learning process, we propose a periodic impulsive knowledge dissemination system to regenerate the knowledge dissemination process. Meanwhile, we put forward learning effectiveness which is an outcome of a trade-off between the benefits and costs raised by knowledge dissemination as objective function. Further, we investigate the optimal teaching strategy which can maximize learning effectiveness, to obtain the optimal effect of knowledge dissemination affected by the teaching activities. We solve this dynamic optimization problem by optimal control theory and get the optimization system. At last we numerically solve this system in several practical examples to make the conclusions intuitive and specific. The optimal teaching strategy proposed in this paper can be applied widely in the optimization problem of personal education and beneficial for enhancing the effect of knowledge dissemination.

Optimal teaching strategy in periodic impulsive knowledge dissemination system

PubMed Central

Liu, Dan-Qing; Wu, Zhen-Qiang; Wang, Yu-Xin; Guo, Qiang

2017-01-01

Accurately describing the knowledge dissemination process is significant to enhance the performance of personalized education. In this study, considering the effect of periodic teaching activities on the learning process, we propose a periodic impulsive knowledge dissemination system to regenerate the knowledge dissemination process. Meanwhile, we put forward learning effectiveness which is an outcome of a trade-off between the benefits and costs raised by knowledge dissemination as objective function. Further, we investigate the optimal teaching strategy which can maximize learning effectiveness, to obtain the optimal effect of knowledge dissemination affected by the teaching activities. We solve this dynamic optimization problem by optimal control theory and get the optimization system. At last we numerically solve this system in several practical examples to make the conclusions intuitive and specific. The optimal teaching strategy proposed in this paper can be applied widely in the optimization problem of personal education and beneficial for enhancing the effect of knowledge dissemination. PMID:28665961

Corner flow control in high through-flow axial commercial fan/booster using blade 3-D optimization

NASA Astrophysics Data System (ADS)

Zhu, Fang; Jin, Donghai; Gui, Xingmin

2012-02-01

This study is aimed at using blade 3-D optimization to control corner flows in the high through-flow fan/booster of a high bypass ratio commercial turbofan engine. Two kinds of blade 3-D optimization, end-bending and bow, are focused on. On account of the respective operation mode and environment, the approach to 3-D aerodynamic modeling of rotor blades is different from stator vanes. Based on the understanding of the mechanism of the corner flow and the consideration of intensity problem for rotors, this paper uses a variety of blade 3-D optimization approaches, such as loading distribution optimization, perturbation of departure angles and stacking-axis manipulation, which are suitable for rotors and stators respectively. The obtained 3-D blades and vanes can improve the corner flow features by end-bending and bow effects. The results of this study show that flows in corners of the fan/booster, such as the fan hub region, the tip and hub of the vanes of the booster, are very complex and dominated by 3-D effects. The secondary flows there are found to have a strong detrimental effect on the compressor performance. The effects of both end-bending and bow can improve the flow separation in corners, but the specific ways they work and application scope are somewhat different. Redesigning the blades via blade 3-D optimization to control the corner flow has effectively reduced the loss generation and improved the stall margin by a large amount.

Deployment-based lifetime optimization for linear wireless sensor networks considering both retransmission and discrete power control.

PubMed

Li, Ruiying; Ma, Wenting; Huang, Ning; Kang, Rui

2017-01-01

A sophisticated method for node deployment can efficiently reduce the energy consumption of a Wireless Sensor Network (WSN) and prolong the corresponding network lifetime. Pioneers have proposed many node deployment based lifetime optimization methods for WSNs, however, the retransmission mechanism and the discrete power control strategy, which are widely used in practice and have large effect on the network energy consumption, are often neglected and assumed as a continuous one, respectively, in the previous studies. In this paper, both retransmission and discrete power control are considered together, and a more realistic energy-consumption-based network lifetime model for linear WSNs is provided. Using this model, we then propose a generic deployment-based optimization model that maximizes network lifetime under coverage, connectivity and transmission rate success constraints. The more accurate lifetime evaluation conduces to a longer optimal network lifetime in the realistic situation. To illustrate the effectiveness of our method, both one-tiered and two-tiered uniformly and non-uniformly distributed linear WSNs are optimized in our case studies, and the comparisons between our optimal results and those based on relatively inaccurate lifetime evaluation show the advantage of our method when investigating WSN lifetime optimization problems.

Stress Exposure and Depression in Disadvantaged Women: The Protective Effects of Optimism and Perceived Control

ERIC Educational Resources Information Center

Grote, Nancy K.; Bledsoe, Sarah E.; Larkin, Jill; Lemay, Edward P., Jr.; Brown, Charlotte

2007-01-01

In the present study, the authors predicted that the individual protective factors of optimism and perceived control over acute and chronic stressors would buffer the relations between acute and chronic stress exposure and severity of depression, controlling for household income, in a sample of financially disadvantaged women. Ninety-seven African…

Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems.

PubMed

Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong

2014-12-01

In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach.

Optimization Control of the Color-Coating Production Process for Model Uncertainty

PubMed Central

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results. PMID:27247563

Optimization Control of the Color-Coating Production Process for Model Uncertainty.

PubMed

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.

A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems

NASA Astrophysics Data System (ADS)

Heinkenschloss, Matthias

2005-01-01

We study a class of time-domain decomposition-based methods for the numerical solution of large-scale linear quadratic optimal control problems. Our methods are based on a multiple shooting reformulation of the linear quadratic optimal control problem as a discrete-time optimal control (DTOC) problem. The optimality conditions for this DTOC problem lead to a linear block tridiagonal system. The diagonal blocks are invertible and are related to the original linear quadratic optimal control problem restricted to smaller time-subintervals. This motivates the application of block Gauss-Seidel (GS)-type methods for the solution of the block tridiagonal systems. Numerical experiments show that the spectral radii of the block GS iteration matrices are larger than one for typical applications, but that the eigenvalues of the iteration matrices decay to zero fast. Hence, while the GS method is not expected to convergence for typical applications, it can be effective as a preconditioner for Krylov-subspace methods. This is confirmed by our numerical tests.A byproduct of this research is the insight that certain instantaneous control techniques can be viewed as the application of one step of the forward block GS method applied to the DTOC optimality system.

Real-time optimal guidance for orbital maneuvering.

NASA Technical Reports Server (NTRS)

Cohen, A. O.; Brown, K. R.

1973-01-01

A new formulation for soft-constraint trajectory optimization is presented as a real-time optimal feedback guidance method for multiburn orbital maneuvers. Control is always chosen to minimize burn time plus a quadratic penalty for end condition errors, weighted so that early in the mission (when controllability is greatest) terminal errors are held negligible. Eventually, as controllability diminishes, the method partially relaxes but effectively still compensates perturbations in whatever subspace remains controllable. Although the soft-constraint concept is well-known in optimal control, the present formulation is novel in addressing the loss of controllability inherent in multiple burn orbital maneuvers. Moreover the necessary conditions usually obtained from a Bolza formulation are modified in this case so that the fully hard constraint formulation is a numerically well behaved subcase. As a result convergence properties have been greatly improved.

Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control

NASA Astrophysics Data System (ADS)

Maringanti, Chetan; Chaubey, Indrajeet; Popp, Jennie

2009-06-01

Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA-II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade-off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

PubMed

Biswas, Santanu; Subramanian, Abhishek; ELMojtaba, Ibrahim M; Chattopadhyay, Joydev; Sarkar, Ram Rup

2017-01-01

Visceral leishmaniasis (VL) is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

Optimality based repetitive controller design for track-following servo system of optical disk drives.

PubMed

Chen, Wentao; Zhang, Weidong

2009-10-01

In an optical disk drive servo system, to attenuate the external periodic disturbances induced by inevitable disk eccentricity, repetitive control has been used successfully. The performance of a repetitive controller greatly depends on the bandwidth of the low-pass filter included in the repetitive controller. However, owing to the plant uncertainty and system stability, it is difficult to maximize the bandwidth of the low-pass filter. In this paper, we propose an optimality based repetitive controller design method for the track-following servo system with norm-bounded uncertainties. By embedding a lead compensator in the repetitive controller, both the system gain at periodic signal's harmonics and the bandwidth of the low-pass filter are greatly increased. The optimal values of the repetitive controller's parameters are obtained by solving two optimization problems. Simulation and experimental results are provided to illustrate the effectiveness of the proposed method.

Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems

NASA Technical Reports Server (NTRS)

Nguyen, Nhan

2006-01-01

This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.

Control of three-dimensional waves on thin liquid films. I - Optimal control and transverse mode effects

NASA Astrophysics Data System (ADS)

Tomlin, Ruben; Gomes, Susana; Pavliotis, Greg; Papageorgiou, Demetrios

2017-11-01

We consider a weakly nonlinear model for interfacial waves on three-dimensional thin films on inclined flat planes - the Kuramoto-Sivashinsky equation. The flow is driven by gravity, and is allowed to be overlying or hanging on the flat substrate. Blowing and suction controls are applied at the substrate surface. In this talk we explore the instability of the transverse modes for hanging arrangements, which are unbounded and grow exponentially. The structure of the equations allows us to construct optimal transverse controls analytically to prevent this transverse growth. In this case and the case of an overlying film, we additionally study the influence of controlling to non-trivial transverse states on the streamwise and mixed mode dynamics. Finally, we solve the full optimal control problem by deriving the first order necessary conditions for existence of an optimal control, and solving these numerically using the forward-backward sweep method.

Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm.

PubMed

Ahmed, Ashik; Al-Amin, Rasheduzzaman; Amin, Ruhul

2014-01-01

This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

Backward bifurcation and optimal control of Plasmodium Knowlesi malaria

NASA Astrophysics Data System (ADS)

Abdullahi, Mohammed Baba; Hasan, Yahya Abu; Abdullah, Farah Aini

2014-07-01

A deterministic model for the transmission dynamics of Plasmodium Knowlesi malaria with direct transmission is developed. The model is analyzed using dynamical system techniques and it shows that the backward bifurcation occurs for some range of parameters. The model is extended to assess the impact of time dependent preventive (biological and chemical control) against the mosquitoes and vaccination for susceptible humans, while treatment for infected humans. The existence of optimal control is established analytically by the use of optimal control theory. Numerical simulations of the problem, suggest that applying the four control measure can effectively reduce if not eliminate the spread of Plasmodium Knowlesi in a community.

Optimal locations and orientations of piezoelectric transducers on cylindrical shell based on gramians of contributed and undesired Rayleigh-Ritz modes using genetic algorithm

NASA Astrophysics Data System (ADS)

Biglar, Mojtaba; Mirdamadi, Hamid Reza; Danesh, Mohammad

2014-02-01

In this study, the active vibration control and configurational optimization of a cylindrical shell are analyzed by using piezoelectric transducers. The piezoelectric patches are attached to the surface of the cylindrical shell. The Rayleigh-Ritz method is used for deriving dynamic modeling of cylindrical shell and piezoelectric sensors and actuators based on the Donnel-Mushtari shell theory. The major goal of this study is to find the optimal locations and orientations of piezoelectric sensors and actuators on the cylindrical shell. The optimization procedure is designed based on desired controllability and observability of each contributed and undesired mode. Further, in order to limit spillover effects, the residual modes are taken into consideration. The optimization variables are the positions and orientations of piezoelectric patches. Genetic algorithm is utilized to evaluate the optimal configurations. In this article, for improving the maximum power and capacity of actuators for amplitude depreciation of negative velocity feedback strategy, we have proposed a new control strategy, called "Saturated Negative Velocity Feedback Rule (SNVF)". The numerical results show that the optimization procedure is effective for vibration reduction, and specifically, by locating actuators and sensors in their optimal locations and orientations, the vibrations of cylindrical shell are suppressed more quickly.

ODECS -- A computer code for the optimal design of S.I. engine control strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arsie, I.; Pianese, C.; Rizzo, G.

1996-09-01

The computer code ODECS (Optimal Design of Engine Control Strategies) for the design of Spark Ignition engine control strategies is presented. This code has been developed starting from the author`s activity in this field, availing of some original contributions about engine stochastic optimization and dynamical models. This code has a modular structure and is composed of a user interface for the definition, the execution and the analysis of different computations performed with 4 independent modules. These modules allow the following calculations: (1) definition of the engine mathematical model from steady-state experimental data; (2) engine cycle test trajectory corresponding to amore » vehicle transient simulation test such as ECE15 or FTP drive test schedule; (3) evaluation of the optimal engine control maps with a steady-state approach; (4) engine dynamic cycle simulation and optimization of static control maps and/or dynamic compensation strategies, taking into account dynamical effects due to the unsteady fluxes of air and fuel and the influences of combustion chamber wall thermal inertia on fuel consumption and emissions. Moreover, in the last two modules it is possible to account for errors generated by a non-deterministic behavior of sensors and actuators and the related influences on global engine performances, and compute robust strategies, less sensitive to stochastic effects. In the paper the four models are described together with significant results corresponding to the simulation and the calculation of optimal control strategies for dynamic transient tests.« less

Feedback quantum control of molecular electronic population transfer

NASA Astrophysics Data System (ADS)

Bardeen, Christopher J.; Yakovlev, Vladislav V.; Wilson, Kent R.; Carpenter, Scott D.; Weber, Peter M.; Warren, Warren S.

1997-11-01

Feedback quantum control, where the sample `teaches' a computer-controlled arbitrary lightform generator to find the optimal light field, is experimentally demonstrated for a molecular system. Femtosecond pulses tailored by a computer-controlled acousto-optic pulse shaper excite fluorescence from laser dye molecules in solution. Fluorescence and laser power are monitored, and the computer uses the experimental data and a genetic algorithm to optimize population transfer from ground to first excited state. Both efficiency (the ratio of excited state population to laser energy) and effectiveness (total excited state population) are optimized. Potential use as an `automated theory tester' is discussed.

AIAA/USAF/NASA/OAI Symposium on Multidisciplinary Analysis and Optimization, 4th, Cleveland, OH, Sept. 21-23, 1992, Technical Papers. Pts. 1 & 2

NASA Technical Reports Server (NTRS)

1992-01-01

The papers presented at the symposium cover aerodynamics, design applications, propulsion systems, high-speed flight, structures, controls, sensitivity analysis, optimization algorithms, and space structures applications. Other topics include helicopter rotor design, artificial intelligence/neural nets, and computational aspects of optimization. Papers are included on flutter calculations for a system with interacting nonlinearities, optimization in solid rocket booster application, improving the efficiency of aerodynamic shape optimization procedures, nonlinear control theory, and probabilistic structural analysis of space truss structures for nonuniform thermal environmental effects.

Optimal management of non-Markovian biological populations

USGS Publications Warehouse

Williams, B.K.

2007-01-01

Wildlife populations typically are described by Markovian models, with population dynamics influenced at each point in time by current but not previous population levels. Considerable work has been done on identifying optimal management strategies under the Markovian assumption. In this paper we generalize this work to non-Markovian systems, for which population responses to management are influenced by lagged as well as current status and/or controls. We use the maximum principle of optimal control theory to derive conditions for the optimal management such a system, and illustrate the effects of lags on the structure of optimal habitat strategies for a predator-prey system.

Modelling and Optimal Control of Typhoid Fever Disease with Cost-Effective Strategies.

PubMed

Tilahun, Getachew Teshome; Makinde, Oluwole Daniel; Malonza, David

2017-01-01

We propose and analyze a compartmental nonlinear deterministic mathematical model for the typhoid fever outbreak and optimal control strategies in a community with varying population. The model is studied qualitatively using stability theory of differential equations and the basic reproductive number that represents the epidemic indicator is obtained from the largest eigenvalue of the next-generation matrix. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. The model exhibits a forward transcritical bifurcation and the sensitivity analysis is performed. The optimal control problem is designed by applying Pontryagin maximum principle with three control strategies, namely, the prevention strategy through sanitation, proper hygiene, and vaccination; the treatment strategy through application of appropriate medicine; and the screening of the carriers. The cost functional accounts for the cost involved in prevention, screening, and treatment together with the total number of the infected persons averted. Numerical results for the typhoid outbreak dynamics and its optimal control revealed that a combination of prevention and treatment is the best cost-effective strategy to eradicate the disease.

Automatic Control of Personal Rapid Transit Vehicles

NASA Technical Reports Server (NTRS)

Smith, P. D.

1972-01-01

The requirements for automatic longitudinal control of a string of closely packed personal vehicles are outlined. Optimal control theory is used to design feedback controllers for strings of vehicles. An important modification of the usual optimal control scheme is the inclusion of jerk in the cost functional. While the inclusion of the jerk term was considered, the effect of its inclusion was not sufficiently studied. Adding the jerk term will increase passenger comfort.

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

PubMed

Aprasoff, Jonathan; Donchin, Opher

2012-04-01

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments

NASA Astrophysics Data System (ADS)

Sun, Ning; Wu, Yiming; Chen, He; Fang, Yongchun

2018-03-01

Underactuated cranes play an important role in modern industry. Specifically, in most situations of practical applications, crane systems exhibit significant double pendulum characteristics, which makes the control problem quite challenging. Moreover, most existing planners/controllers obtained with standard methods/techniques for double pendulum cranes cannot minimize the energy consumption when fulfilling the transportation tasks. Therefore, from a practical perspective, this paper proposes an energy-optimal solution for transportation control of double pendulum cranes. By applying the presented approach, the transportation objective, including fast trolley positioning and swing elimination, is achieved with minimized energy consumption, and the residual oscillations are suppressed effectively with all the state constrains being satisfied during the entire transportation process. As far as we know, this is the first energy-optimal solution for transportation control of underactuated double pendulum cranes with various state and control constraints. Hardware experimental results are included to verify the effectiveness of the proposed approach, whose superior performance is reflected by being experimentally compared with some comparative controllers.

Time-optical spinup maneuvers of flexible spacecraft

NASA Technical Reports Server (NTRS)

Singh, G.; Kabamba, P. T.; Mcclamroch, N. H.

1990-01-01

Attitude controllers for spacecraft have been based on the assumption that the bodies being controlled are rigid. Future spacecraft, however, may be quite flexible. Many applications require spinning up/down these vehicles. In this work the minimum time control of these maneuvers is considered. The time-optimal control is shown to possess an important symmetry property. Taking advantage of this property, the necessary and sufficient conditions for optimality are transformed into a system of nonlinear algebraic equations in the control switching times during one half of the maneuver, the maneuver time, and the costates at the mid-maneuver time. These equations can be solved using a homotopy approach. Control spillover measures are introduced and upper bounds on these measures are obtained. For a special case these upper bounds can be expressed in closed form for an infinite dimensional evaluation model. Rotational stiffening effects are ignored in the optimal control analysis. Based on a heuristic argument a simple condition is given which justifies the omission of these nonlinear effects. This condition is validated by numerical simulation.

Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

NASA Technical Reports Server (NTRS)

Lan, C. Edward; Ge, Fuying

1989-01-01

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

Maximum Entropy/Optimal Projection (MEOP) control design synthesis: Optimal quantification of the major design tradeoffs

NASA Technical Reports Server (NTRS)

Hyland, D. C.; Bernstein, D. S.

1987-01-01

The underlying philosophy and motivation of the optimal projection/maximum entropy (OP/ME) stochastic modeling and reduced control design methodology for high order systems with parameter uncertainties are discussed. The OP/ME design equations for reduced-order dynamic compensation including the effect of parameter uncertainties are reviewed. The application of the methodology to several Large Space Structures (LSS) problems of representative complexity is illustrated.

Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach.

PubMed

Zhang, Huaguang; Feng, Tao; Yang, Guang-Hong; Liang, Hongjing

2015-07-01

In this paper, the inverse optimal approach is employed to design distributed consensus protocols that guarantee consensus and global optimality with respect to some quadratic performance indexes for identical linear systems on a directed graph. The inverse optimal theory is developed by introducing the notion of partial stability. As a result, the necessary and sufficient conditions for inverse optimality are proposed. By means of the developed inverse optimal theory, the necessary and sufficient conditions are established for globally optimal cooperative control problems on directed graphs. Basic optimal cooperative design procedures are given based on asymptotic properties of the resulting optimal distributed consensus protocols, and the multiagent systems can reach desired consensus performance (convergence rate and damping rate) asymptotically. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

PubMed

Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

2018-01-01

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

Impact of Release Rates on the Effectiveness of Augmentative Biological Control Agents

PubMed Central

Crowder, David W.

2007-01-01

To access the effect of augmentative biological control agents, 31 articles were reviewed that investigated the impact of release rates of 35 augmentative biological control agents on the control of 42 arthropod pests. In 64% of the cases, the release rate of the biological control agent did not significantly affect the density or mortality of the pest insect. Results where similar when parasitoidsor predators were utilized as the natural enemy. Within any order of natural enemy, there were more cases where release rates did not affect augmentative biological control than cases where release rates were significant. There were more cases in which release rates did not affect augmentative biological control when pests were from the orders Hemiptera, Acari, or Diptera, but not with pests from the order Lepidoptera. In most cases, there was an optimal release rate that produced effective control of a pest species. This was especially true when predators were used as a biological control agent. Increasing the release rate above the optimal rate did not improve control of the pest and thus would be economically detrimental. Lower release rates were of ten optimal when biological control was used in conjunction with insecticides. In many cases, the timing and method of biological control applications were more significant factors impacting the effectiveness of biological control than the release rate. Additional factors that may limit the relative impact of release rates include natural enemy fecundity, establishment rates, prey availability, dispersal, and cannibalism. PMID:20307240

Effect of rotation rate on the forces of a rotating cylinder: Simulation and control

NASA Technical Reports Server (NTRS)

Burns, John A.; Ou, Yuh-Roung

1993-01-01

In this paper we present numerical solutions to several optimal control problems for an unsteady viscous flow. The main thrust of this work is devoted to simulation and control of an unsteady flow generated by a circular cylinder undergoing rotary motion. By treating the rotation rate as a control variable, we can formulate two optimal control problems and use a central difference/pseudospectral transform method to numerically compute the optimal control rates. Several types of rotations are considered as potential controls, and we show that a proper synchronization of forcing frequency with the natural vortex shedding frequency can greatly influence the flow. The results here indicate that using moving boundary controls for such systems may provide a feasible mechanism for flow control.

Optimal intervention strategies for cholera outbreak by education and chlorination

NASA Astrophysics Data System (ADS)

Bakhtiar, Toni

2016-01-01

This paper discusses the control of infectious diseases in the framework of optimal control approach. A case study on cholera control was studied by considering two control strategies, namely education and chlorination. We distinct the former control into one regarding person-to-person behaviour and another one concerning person-to-environment conduct. Model are divided into two interacted populations: human population which follows an SIR model and pathogen population. Pontryagin maximum principle was applied in deriving a set of differential equations which consists of dynamical and adjoin systems as optimality conditions. Then, the fourth order Runge-Kutta method was exploited to numerically solve the equation system. An illustrative example was provided to assess the effectiveness of the control strategies toward a set of control scenarios.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

The integrated manual and automatic control of complex flight systems

NASA Technical Reports Server (NTRS)

Schmidt, D. K.

1985-01-01

Pilot/vehicle analysis techniques for optimizing aircraft handling qualities are presented. The analysis approach considered is based on the optimal control frequency domain techniques. These techniques stem from an optimal control approach of a Neal-Smith like analysis on aircraft attitude dynamics extended to analyze the flared landing task. Some modifications to the technique are suggested and discussed. An in depth analysis of the effect of the experimental variables, such as prefilter, is conducted to gain further insight into the flared land task for this class of vehicle dynamics.

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Real-Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

NASA Astrophysics Data System (ADS)

Zhu, Feilin; Zhong, Ping-An; Sun, Yimeng; Yeh, William W.-G.

2017-12-01

Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Mechanism of bandwidth improvement in passively cooled SMA position actuators

NASA Astrophysics Data System (ADS)

Gorbet, R. B.; Morris, K. A.; Chau, R. C. C.

2009-09-01

The heating of shape memory alloy (SMA) materials leads to a thermally driven phase change which can be used to do work. An SMA wire can be thermally cycled by controlling electric current through the wire, creating an electro-mechanical actuator. Such actuators are typically heated electrically and cooled through convection. The thermal time constants and lack of active cooling limit the operating frequencies. In this work, the bandwidth of a still-air-cooled SMA wire controlled with a PID controller is improved through optimization of the controller gains. Results confirm that optimization can improve the ability of the actuator to operate at a given frequency. Overshoot is observed in the optimal controllers at low frequencies. This is a result of hysteresis in the wire's contraction-temperature characteristic, since different input temperatures can achieve the same output value. The optimal controllers generate overshoot during heating, in order to cause the system to operate at a point on the hysteresis curve where faster cooling can be achieved. The optimization results in a controller which effectively takes advantage of the multi-valued nature of the hysteresis to improve performance.

The effect of spectral filters on visual search in stroke patients.

PubMed

Beasley, Ian G; Davies, Leon N

2013-01-01

Visual search impairment can occur following stroke. The utility of optimal spectral filters on visual search in stroke patients has not been considered to date. The present study measured the effect of optimal spectral filters on visual search response time and accuracy, using a task requiring serial processing. A stroke and control cohort undertook the task three times: (i) using an optimally selected spectral filter; (ii) the subjects were randomly assigned to two groups with group 1 using an optimal filter for two weeks, whereas group 2 used a grey filter for two weeks; (iii) the groups were crossed over with group 1 using a grey filter for a further two weeks and group 2 given an optimal filter, before undertaking the task for the final time. Initial use of an optimal spectral filter improved visual search response time but not error scores in the stroke cohort. Prolonged use of neither an optimal nor a grey filter improved response time or reduced error scores. In fact, response times increased with the filter, regardless of its type, for stroke and control subjects; this outcome may be due to contrast reduction or a reflection of task design, given that significant practice effects were noted.

Age Effects in Postural Control Analyzed via a Principal Component Analysis of Kinematic Data and Interpreted in Relation to Predictions of the Optimal Feedback Control Theory

PubMed Central

Haid, Thomas H.; Doix, Aude-Clémence M.; Nigg, Benno M.; Federolf, Peter A.

2018-01-01

Optimal feedback control theory suggests that control of movement is focused on movement dimensions that are important for the task's success. The current study tested the hypotheses that age effects would emerge in the control of only specific movement components and that these components would be linked to the task relevance. Fifty healthy volunteers, 25 young and 25 older adults, performed a 80s-tandem stance while their postural movements were recorded using a standard motion capture system. The postural movements were decomposed by a principal component analysis into one-dimensional movement components, PMk, whose control was assessed through two variables, Nk and σk, which characterized the tightness and the regularity of the neuro-muscular control, respectively. The older volunteers showed less tight and more irregular control in PM2 (N2: −9.2%, p = 0.007; σ2: +14.3.0%, p = 0.017) but tighter control in PM8 and PM9 (N8: +4.7%, p = 0.020; N9: +2.5%, p = 0.043; σ9: −8.8%, p = 0.025). These results suggest that aging effects alter the postural control system not as a whole, but emerge in specific, task relevant components. The findings of the current study thus support the hypothesis that the minimal intervention principle, as described in the context of optimal feedback control (OFC), may be relevant when assessing aging effects on postural control. PMID:29459826

Optimization of Sensing and Feedback Control for Vibration/Flutter of Rotating Disk by PZT Actuators via Air Coupled Pressure

PubMed Central

Yan, Tianhong; Xu, Xinsheng; Han, Jianqiang; Lin, Rongming; Ju, Bingfeng; Li, Qing

2011-01-01

In this paper, a feedback control mechanism and its optimization for rotating disk vibration/flutter via changes of air-coupled pressure generated using piezoelectric patch actuators are studied. A thin disk rotates in an enclosure, which is equipped with a feedback control loop consisting of a micro-sensor, a signal processor, a power amplifier, and several piezoelectric (PZT) actuator patches distributed on the cover of the enclosure. The actuator patches are mounted on the inner or the outer surfaces of the enclosure to produce necessary control force required through the airflow around the disk. The control mechanism for rotating disk flutter using enclosure surfaces bonded with sensors and piezoelectric actuators is thoroughly studied through analytical simulations. The sensor output is used to determine the amount of input to the actuator for controlling the response of the disk in a closed loop configuration. The dynamic stability of the disk-enclosure system, together with the feedback control loop, is analyzed as a complex eigenvalue problem, which is solved using Galerkin’s discretization procedure. The results show that the disk flutter can be reduced effectively with proper configurations of the control gain and the phase shift through the actuations of PZT patches. The effectiveness of different feedback control methods in altering system characteristics and system response has been investigated. The control capability, in terms of control gain, phase shift, and especially the physical configuration of actuator patches, are also evaluated by calculating the complex eigenvalues and the maximum displacement produced by the actuators. To achieve a optimal control performance, sizes, positions and shapes of PZT patches used need to be optimized and such optimization has been achieved through numerical simulations. PMID:22163788

Optimization of sensing and feedback control for vibration/flutter of rotating disk by PZT actuators via air coupled pressure.

PubMed

Yan, Tianhong; Xu, Xinsheng; Han, Jianqiang; Lin, Rongming; Ju, Bingfeng; Li, Qing

2011-01-01

In this paper, a feedback control mechanism and its optimization for rotating disk vibration/flutter via changes of air-coupled pressure generated using piezoelectric patch actuators are studied. A thin disk rotates in an enclosure, which is equipped with a feedback control loop consisting of a micro-sensor, a signal processor, a power amplifier, and several piezoelectric (PZT) actuator patches distributed on the cover of the enclosure. The actuator patches are mounted on the inner or the outer surfaces of the enclosure to produce necessary control force required through the airflow around the disk. The control mechanism for rotating disk flutter using enclosure surfaces bonded with sensors and piezoelectric actuators is thoroughly studied through analytical simulations. The sensor output is used to determine the amount of input to the actuator for controlling the response of the disk in a closed loop configuration. The dynamic stability of the disk-enclosure system, together with the feedback control loop, is analyzed as a complex eigenvalue problem, which is solved using Galerkin's discretization procedure. The results show that the disk flutter can be reduced effectively with proper configurations of the control gain and the phase shift through the actuations of PZT patches. The effectiveness of different feedback control methods in altering system characteristics and system response has been investigated. The control capability, in terms of control gain, phase shift, and especially the physical configuration of actuator patches, are also evaluated by calculating the complex eigenvalues and the maximum displacement produced by the actuators. To achieve a optimal control performance, sizes, positions and shapes of PZT patches used need to be optimized and such optimization has been achieved through numerical simulations.

The dynamics and optimal control of spinning spacecraft and movable telescoping appendages, part A. [two axis control with single offset boom

NASA Technical Reports Server (NTRS)

Bainum, P. M.; Sellappan, R.

1977-01-01

The problem of optimal control with a minimum time criterion as applied to a single boom system for achieving two axis control is discussed. The special case where the initial conditions are such that the system can be driven to the equilibrium state with only a single switching maneuver in the bang-bang optimal sequence is analyzed. The system responses are presented. Application of the linear regulator problem for the optimal control of the telescoping system is extended to consider the effects of measurement and plant noises. The noise uncertainties are included with an application of the estimator - Kalman filter problem. Different schemes for measuring the components of the angular velocity are considered. Analytical results are obtained for special cases, and numerical results are presented for the general case.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Policy Iteration for $H_\\infty $ Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao

2018-02-01

Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest -gain and the associated optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.

Synthesizing epidemiological and economic optima for control of immunizing infections.

PubMed

Klepac, Petra; Laxminarayan, Ramanan; Grenfell, Bryan T

2011-08-23

Epidemic theory predicts that the vaccination threshold required to interrupt local transmission of an immunizing infection like measles depends only on the basic reproductive number and hence transmission rates. When the search for optimal strategies is expanded to incorporate economic constraints, the optimum for disease control in a single population is determined by relative costs of infection and control, rather than transmission rates. Adding a spatial dimension, which precludes local elimination unless it can be achieved globally, can reduce or increase optimal vaccination levels depending on the balance of costs and benefits. For weakly coupled populations, local optimal strategies agree with the global cost-effective strategy; however, asymmetries in costs can lead to divergent control optima in more strongly coupled systems--in particular, strong regional differences in costs of vaccination can preclude local elimination even when elimination is locally optimal. Under certain conditions, it is locally optimal to share vaccination resources with other populations.

Optimal charge control strategies for stationary photovoltaic battery systems

NASA Astrophysics Data System (ADS)

Li, Jiahao; Danzer, Michael A.

2014-07-01

Battery systems coupled to photovoltaic (PV) modules for example fulfill one major function: they locally decouple PV generation and consumption of electrical power leading to two major effects. First, they reduce the grid load, especially at peak times and therewith reduce the necessity of a network expansion. And second, they increase the self-consumption in households and therewith help to reduce energy expenses. For the management of PV batteries charge control strategies need to be developed to reach the goals of both the distribution system operators and the local power producer. In this work optimal control strategies regarding various optimization goals are developed on the basis of the predicted household loads and PV generation profiles using the method of dynamic programming. The resulting charge curves are compared and essential differences discussed. Finally, a multi-objective optimization shows that charge control strategies can be derived that take all optimization goals into account.

Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT

NASA Astrophysics Data System (ADS)

Wang, Qingrui; Liu, Ruimin; Men, Cong; Guo, Lijia

2018-05-01

The genetic algorithm (GA) was combined with the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model to obtain an optimized land use pattern for controlling non-point source (NPS) pollution. The performance of the combination was evaluated. The effect of the optimized land use pattern on the NPS pollution control was estimated by the Soil and Water Assessment Tool (SWAT) model and an assistant map was drawn to support the land use plan for the future. The Xiangxi River watershed was selected as the study area. Two scenarios were used to simulate the land use change. Under the historical trend scenario (Markov chain prediction), the forest area decreased by 2035.06 ha, and was mainly converted into paddy and dryland area. In contrast, under the optimized scenario (genetic algorithm (GA) prediction), up to 3370 ha of dryland area was converted into forest area. Spatially, the conversion of paddy and dryland into forest occurred mainly in the northwest and southeast of the watershed, where the slope land occupied a large proportion. The organic and inorganic phosphorus loads decreased by 3.6% and 3.7%, respectively, in the optimized scenario compared to those in the historical trend scenario. GA showed a better performance in optimized land use prediction. A comparison of the land use patterns in 2010 under the real situation and in 2020 under the optimized situation showed that Shennongjia and Shuiyuesi should convert 1201.76 ha and 1115.33 ha of dryland into forest areas, respectively, which represented the greatest changes in all regions in the watershed. The results of this study indicated that GA and the CLUE-S model can be used to optimize the land use patterns in the future and that SWAT can be used to evaluate the effect of land use optimization on non-point source pollution control. These methods may provide support for land use plan of an area.

Optimized strategy for the control and prevention of newly emerging influenza revealed by the spread dynamics model.

PubMed

Zhang, Wen-Dou; Zu, Zheng-Hu; Xu, Qing; Xu, Zhi-Jing; Liu, Jin-Jie; Zheng, Tao

2014-01-01

No matching vaccine is immediately available when a novel influenza strain breaks out. Several nonvaccine-related strategies must be employed to control an influenza epidemic, including antiviral treatment, patient isolation, and immigration detection. This paper presents the development and application of two regional dynamic models of influenza with Pontryagin's Maximum Principle to determine the optimal control strategies for an epidemic and the corresponding minimum antiviral stockpiles. Antiviral treatment was found to be the most effective measure to control new influenza outbreaks. In the case of inadequate antiviral resources, the preferred approach was the centralized use of antiviral resources in the early stage of the epidemic. Immigration detection was the least cost-effective; however, when used in combination with the other measures, it may play a larger role. The reasonable mix of the three control measures could reduce the number of clinical cases substantially, to achieve the optimal control of new influenza.

Coordinated Control of Cross-Flow Turbines

NASA Astrophysics Data System (ADS)

Strom, Benjamin; Brunton, Steven; Polagye, Brian

2016-11-01

Cross-flow turbines, also known as vertical-axis turbines, have several advantages over axial-flow turbines for a number of applications including urban wind power, high-density arrays, and marine or fluvial currents. By controlling the angular velocity applied to the turbine as a function of angular blade position, we have demonstrated a 79 percent increase in cross-flow turbine efficiency over constant-velocity control. This strategy uses the downhill simplex method to optimize control parameter profiles during operation of a model turbine in a recirculating water flume. This optimization method is extended to a set of two turbines, where the blade motions and position of the downstream turbine are optimized to beneficially interact with the coherent structures in the wake of the upstream turbine. This control scheme has the potential to enable high-density arrays of cross-flow turbines to operate at cost-effective efficiency. Turbine wake and force measurements are analyzed for insight into the effect of a coordinated control strategy.

Optimized Strategy for the Control and Prevention of Newly Emerging Influenza Revealed by the Spread Dynamics Model

PubMed Central

Zhang, Wen-Dou; Zu, Zheng-Hu; Xu, Qing; Xu, Zhi-Jing; Liu, Jin-Jie; Zheng, Tao

2014-01-01

No matching vaccine is immediately available when a novel influenza strain breaks out. Several nonvaccine-related strategies must be employed to control an influenza epidemic, including antiviral treatment, patient isolation, and immigration detection. This paper presents the development and application of two regional dynamic models of influenza with Pontryagin’s Maximum Principle to determine the optimal control strategies for an epidemic and the corresponding minimum antiviral stockpiles. Antiviral treatment was found to be the most effective measure to control new influenza outbreaks. In the case of inadequate antiviral resources, the preferred approach was the centralized use of antiviral resources in the early stage of the epidemic. Immigration detection was the least cost-effective; however, when used in combination with the other measures, it may play a larger role. The reasonable mix of the three control measures could reduce the number of clinical cases substantially, to achieve the optimal control of new influenza. PMID:24392151

Experimental Investigation of the Application of Microramp Flow Control to an Oblique Shock Interaction

NASA Technical Reports Server (NTRS)

Hirt, Stefanie M.; Anderson, Bernhard H.

2009-01-01

The effectiveness of microramp flow control devices in controlling an oblique shock interaction was tested in the 15- by 15-Centimeter Supersonic Wind Tunnel at NASA Glenn Research Center. Fifteen microramp geometries were tested varying the height, chord length, and spacing between ramps. Measurements of the boundary layer properties downstream of the shock reflection were analyzed using design of experiments methods. Results from main effects, D-optimal, full factorial, and central composite designs were compared. The designs provided consistent results for a single variable optimization.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform.

PubMed

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-08-14

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS(®); then, to analyze the system's kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB(®) SIMULINK(®) controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform

PubMed Central

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-01-01

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS®; then, to analyze the system’s kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB® SIMULINK® controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance. PMID:26287210

Optimization of Driving Styles for Fuel Economy Improvement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malikopoulos, Andreas; Aguilar, Juan P.

2012-01-01

Modern vehicles have sophisticated electronic control units, particularly to control engine operation with respect to a balance between fuel economy, emissions, and power. These control units are designed for specific driving conditions and testing. However, each individual driving style is different and rarely meets those driving conditions. In the research reported here we investigate those driving style factors that have a major impact on fuel economy. An optimization framework is proposed with the aim of optimizing driving styles with respect to these driving factors. A set of polynomial metamodels are constructed to reflect the responses produced by changes of themore » driving factors. Then we compare the optimized driving styles to the original ones and evaluate the efficiency and effectiveness of the optimization formulation.« less

Initial state-specific photodissociation dynamics of pyrrole via 1 π σ ∗/ S 0 conical intersection initiated with optimally controlled UV-laser pulses

NASA Astrophysics Data System (ADS)

Nandipati, K. R.; Kanakati, Arun Kumar; Singh, H.; Lan, Z.; Mahapatra, S.

2017-09-01

Optimal initiation of quantum dynamics of N-H photodissociation of pyrrole on the S0-1πσ∗(1A2) coupled electronic states by UV-laser pulses in an effort to guide the subsequent dynamics to dissociation limits is studied theoretically. Specifically, the task of designing optimal laser pulses that act on initial vibrational states of the system for an effective UV-photodissociation is considered by employing optimal control theory. The associated control mechanism(s) for the initial state dependent photodissociation dynamics of pyrrole in the presence of control pulses is examined and discussed in detail. The initial conditions determine implicitly the variation in the dissociation probabilities for the two channels, upon interaction with the field. The optimal pulse corresponds to the objective fixed as maximization of overall reactive flux subject to constraints of reasonable fluence and quantum dynamics. The simple optimal pulses obtained by the use of genetic algorithm based optimization are worth an experimental implementation given the experimental relevance of πσ∗-photochemistry in recent times.

Optimization of bump and blowing to control the flow through a transonic compressor blade cascade

NASA Astrophysics Data System (ADS)

Mazaheri, K.; Khatibirad, S.

2018-03-01

Shock control bump (SCB) and blowing are two flow control methods, used here to improve the aerodynamic performance of transonic compressors. Both methods are applied to a NASA rotor 67 blade section and are optimized to minimize the total pressure loss. A continuous adjoint algorithm is used for multi-point optimization of a SCB to improve the aerodynamic performance of the rotor blade section, for a range of operational conditions around its design point. A multi-point and two single-point optimizations are performed in the design and off-design conditions. It is shown that the single-point optimized shapes have the best performance for their respective operating conditions, but the multi-point one has an overall better performance over the whole operating range. An analysis is given regarding how similarly both single- and multi-point optimized SCBs change the wave structure between blade sections resulting in a more favorable flow pattern. Interactions of the SCB with the boundary layer and the wave structure, and its effects on the separation regions are also studied. We have also introduced the concept of blowing for control of shock wave and boundary-layer interaction. A geometrical model is introduced, and the geometrical and physical parameters of blowing are optimized at the design point. The performance improvements of blowing are compared with the SCB. The physical interactions of SCB with the boundary layer and the shock wave are analyzed. The effects of SCB on the wave structure in the flow domain outside the boundary-layer region are investigated. It is shown that the effects of the blowing mechanism are very similar to the SCB.

Modified optimal control pilot model for computer-aided design and analysis

NASA Technical Reports Server (NTRS)

Davidson, John B.; Schmidt, David K.

1992-01-01

This paper presents the theoretical development of a modified optimal control pilot model based upon the optimal control model (OCM) of the human operator developed by Kleinman, Baron, and Levison. This model is input compatible with the OCM and retains other key aspects of the OCM, such as a linear quadratic solution for the pilot gains with inclusion of control rate in the cost function, a Kalman estimator, and the ability to account for attention allocation and perception threshold effects. An algorithm designed for each implementation in current dynamic systems analysis and design software is presented. Example results based upon the analysis of a tracking task using three basic dynamic systems are compared with measured results and with similar analyses performed with the OCM and two previously proposed simplified optimal pilot models. The pilot frequency responses and error statistics obtained with this modified optimal control model are shown to compare more favorably to the measured experimental results than the other previously proposed simplified models evaluated.

Finite horizon optimum control with and without a scrap value

NASA Astrophysics Data System (ADS)

Neck, R.; Blueschke-Nikolaeva, V.; Blueschke, D.

2017-06-01

In this paper, we study the effects of scrap values on the solutions of optimal control problems with finite time horizon. We show how to include a scrap value, either for the state variables or for the state and the control variables, in the OPTCON2 algorithm for the optimal control of dynamic economic systems. We ask whether the introduction of a scrap value can serve as a substitute for an infinite horizon in economic policy optimization problems where the latter option is not available. Using a simple numerical macroeconomic model, we demonstrate that the introduction of a scrap value cannot induce control policies which can be expected for problems with an infinite time horizon.

Optimal design of green and grey stormwater infrastructure for small urban catchment based on life-cycle cost-effectiveness analysis

NASA Astrophysics Data System (ADS)

Yang, Y.; Chui, T. F. M.

2016-12-01

Green infrastructure (GI) is identified as sustainable and environmentally friendly alternatives to the conventional grey stormwater infrastructure. Commonly used GI (e.g. green roof, bioretention, porous pavement) can provide multifunctional benefits, e.g. mitigation of urban heat island effects, improvements in air quality. Therefore, to optimize the design of GI and grey drainage infrastructure, it is essential to account for their benefits together with the costs. In this study, a comprehensive simulation-optimization modelling framework that considers the economic and hydro-environmental aspects of GI and grey infrastructure for small urban catchment applications is developed. Several modelling tools (i.e., EPA SWMM model, the WERF BMP and LID Whole Life Cycle Cost Modelling Tools) and optimization solvers are coupled together to assess the life-cycle cost-effectiveness of GI and grey infrastructure, and to further develop optimal stormwater drainage solutions. A typical residential lot in New York City is examined as a case study. The life-cycle cost-effectiveness of various GI and grey infrastructure are first examined at different investment levels. The results together with the catchment parameters are then provided to the optimization solvers, to derive the optimal investment and contributing area of each type of the stormwater controls. The relationship between the investment and optimized environmental benefit is found to be nonlinear. The optimized drainage solutions demonstrate that grey infrastructure is preferred at low total investments while more GI should be adopted at high investments. The sensitivity of the optimized solutions to the prices the stormwater controls is evaluated and is found to be highly associated with their utilizations in the base optimization case. The overall simulation-optimization framework can be easily applied to other sites world-wide, and to be further developed into powerful decision support systems.

A simple approach to optimal control of invasive species.

PubMed

Hastings, Alan; Hall, Richard J; Taylor, Caz M

2006-12-01

The problem of invasive species and their control is one of the most pressing applied issues in ecology today. We developed simple approaches based on linear programming for determining the optimal removal strategies of different stage or age classes for control of invasive species that are still in a density-independent phase of growth. We illustrate the application of this method to the specific example of invasive Spartina alterniflora in Willapa Bay, WA. For all such systems, linear programming shows in general that the optimal strategy in any time step is to prioritize removal of a single age or stage class. The optimal strategy adjusts which class is the focus of control through time and can be much more cost effective than prioritizing removal of the same stage class each year.

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Optimal active vibration absorber: Design and experimental results

NASA Technical Reports Server (NTRS)

Lee-Glauser, Gina; Juang, Jer-Nan; Sulla, Jeffrey L.

1992-01-01

An optimal active vibration absorber can provide guaranteed closed-loop stability and control for large flexible space structures with collocated sensors/actuators. The active vibration absorber is a second-order dynamic system which is designed to suppress any unwanted structural vibration. This can be designed with minimum knowledge of the controlled system. Two methods for optimizing the active vibration absorber parameters are illustrated: minimum resonant amplitude and frequency matched active controllers. The Controls-Structures Interaction Phase-1 Evolutionary Model at NASA LaRC is used to demonstrate the effectiveness of the active vibration absorber for vibration suppression. Performance is compared numerically and experimentally using acceleration feedback.

Multi-step optimization strategy for fuel-optimal orbital transfer of low-thrust spacecraft

NASA Astrophysics Data System (ADS)

Rasotto, M.; Armellin, R.; Di Lizia, P.

2016-03-01

An effective method for the design of fuel-optimal transfers in two- and three-body dynamics is presented. The optimal control problem is formulated using calculus of variation and primer vector theory. This leads to a multi-point boundary value problem (MPBVP), characterized by complex inner constraints and a discontinuous thrust profile. The first issue is addressed by embedding the MPBVP in a parametric optimization problem, thus allowing a simplification of the set of transversality constraints. The second problem is solved by representing the discontinuous control function by a smooth function depending on a continuation parameter. The resulting trajectory optimization method can deal with different intermediate conditions, and no a priori knowledge of the control structure is required. Test cases in both the two- and three-body dynamics show the capability of the method in solving complex trajectory design problems.

Aerodynamic design and optimization in one shot

NASA Technical Reports Server (NTRS)

Ta'asan, Shlomo; Kuruvila, G.; Salas, M. D.

1992-01-01

This paper describes an efficient numerical approach for the design and optimization of aerodynamic bodies. As in classical optimal control methods, the present approach introduces a cost function and a costate variable (Lagrange multiplier) in order to achieve a minimum. High efficiency is achieved by using a multigrid technique to solve for all the unknowns simultaneously, but restricting work on a design variable only to grids on which their changes produce nonsmooth perturbations. Thus, the effort required to evaluate design variables that have nonlocal effects on the solution is confined to the coarse grids. However, if a variable has a nonsmooth local effect on the solution in some neighborhood, it is relaxed in that neighborhood on finer grids. The cost of solving the optimal control problem is shown to be approximately two to three times the cost of the equivalent analysis problem. Examples are presented to illustrate the application of the method to aerodynamic design and constraint optimization.

Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

NASA Technical Reports Server (NTRS)

Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

2010-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems

PubMed Central

Chen, Sanfeng; Li, Shuai; Liu, Bo; Lou, Yuesheng; Liang, Yongsheng

2012-01-01

Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method. PMID:22778633

A Workflow for Subsurface Pressure Control in Geological CO2 Storage: Optimization of Brine Extraction

NASA Astrophysics Data System (ADS)

Birkholzer, J. T.; Gonzalez-Nicolas, A.; Cihan, A.

2017-12-01

Industrial-scale injection of CO2 into the subsurface increases the fluid pressure in the reservoir, sometimes to the point that the resulting stress increases must be properly controlled to prevent potential damaging impacts such as fault activation, leakage through abandoned wells, or caprock fracturing. Brine extraction is one approach for managing formation pressure, effective stress, and plume movement in response to CO2 injection. However, the management of the extracted brine adds cost to the carbon capture and sequestration operations; therefore optimizing (minimizing) the extraction volume of brine is of great importance. In this study, we apply an adaptive management approach that optimizes extraction rates of brine for pressure control in an integrated optimization framework involving site monitoring, model calibration, and optimization. We investigate the optimization performance as affected by initial site characterization data and introduction of newly acquired data during the injection phase. More accurate initial reservoir characterization data reduce the risk of pressure buildup damage with better estimations of initial extraction rates, which results in better control of pressure during the overall injection time periods. Results also show that low frequencies of model calibration and optimization with the new data, especially at early injection periods, may lead to optimization problems, either that pressure buildup constraints are violated or excessively high extraction rates are proposed. These optimization problems can be eliminated if more frequent data collection and model calibration are conducted, especially at early injection time periods. Approaches such as adaptive pressure management may constitute an effective tool to manage pressure buildup under uncertain and unknown reservoir conditions by minimizing the brine extraction volumes while not exceeding critical pressure buildups of the reservoir.

Application of characteristic time concepts for hydraulic fracture configuration design, control, and optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Advani, S.H.; Lee, T.S.; Moon, H.

1992-10-01

The analysis of pertinent energy components or affiliated characteristic times for hydraulic stimulation processes serves as an effective tool for fracture configuration designs optimization, and control. This evaluation, in conjunction with parametric sensitivity studies, provides a rational base for quantifying dominant process mechanisms and the roles of specified reservoir properties relative to controllable hydraulic fracture variables for a wide spectrum of treatment scenarios. Results are detailed for the following multi-task effort: (a) Application of characteristic time concept and parametric sensitivity studies for specialized fracture geometries (rectangular, penny-shaped, elliptical) and three-layered elliptic crack models (in situ stress, elastic moduli, and fracturemore » toughness contrasts). (b) Incorporation of leak-off effects for models investigated in (a). (c) Simulation of generalized hydraulic fracture models and investigation of the role of controllable vaxiables and uncontrollable system properties. (d) Development of guidelines for hydraulic fracture design and optimization.« less

Application of characteristic time concepts for hydraulic fracture configuration design, control, and optimization. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Advani, S.H.; Lee, T.S.; Moon, H.

1992-10-01

The analysis of pertinent energy components or affiliated characteristic times for hydraulic stimulation processes serves as an effective tool for fracture configuration designs optimization, and control. This evaluation, in conjunction with parametric sensitivity studies, provides a rational base for quantifying dominant process mechanisms and the roles of specified reservoir properties relative to controllable hydraulic fracture variables for a wide spectrum of treatment scenarios. Results are detailed for the following multi-task effort: (a) Application of characteristic time concept and parametric sensitivity studies for specialized fracture geometries (rectangular, penny-shaped, elliptical) and three-layered elliptic crack models (in situ stress, elastic moduli, and fracturemore » toughness contrasts). (b) Incorporation of leak-off effects for models investigated in (a). (c) Simulation of generalized hydraulic fracture models and investigation of the role of controllable vaxiables and uncontrollable system properties. (d) Development of guidelines for hydraulic fracture design and optimization.« less

Assessment of optimal control mechanism complexity by experimental landscape Hessian analysis: fragmentation of CH2BrI

NASA Astrophysics Data System (ADS)

Xing, Xi; Rey-de-Castro, Roberto; Rabitz, Herschel

2014-12-01

Optimally shaped femtosecond laser pulses can often be effectively identified in adaptive feedback quantum control experiments, but elucidating the underlying control mechanism can be a difficult task requiring significant additional analysis. We introduce landscape Hessian analysis (LHA) as a practical experimental tool to aid in elucidating control mechanism insights. This technique is applied to the dissociative ionization of CH2BrI using shaped fs laser pulses for optimization of the absolute yields of ionic fragments as well as their ratios for the competing processes of breaking the C-Br and C-I bonds. The experimental results suggest that these nominally complex problems can be reduced to a low-dimensional control space with insights into the control mechanisms. While the optimal yield for some fragments is dominated by a non-resonant intensity-driven process, the optimal generation of other fragments maa difficult task requiring significant additionaly be explained by a non-resonant process coupled to few level resonant dynamics. Theoretical analysis and modeling is consistent with the experimental observations.

CLFs-based optimization control for a class of constrained visual servoing systems.

PubMed

Song, Xiulan; Miaomiao, Fu

2017-03-01

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal control of a two-strain tuberculosis-HIV/AIDS co-infection model.

PubMed

Agusto, F B; Adekunle, A I

2014-05-01

Tuberculosis is a bacterial disease caused by Mycobacterium tuberculosis (TB). The risk for TB infection greatly increases with HIV infection; TB disease occurs in 7-10% of patients with HIV infection each year, increasing the potential for transmission of drug-resistant Mycobacterium tuberculosis strains. In this paper a deterministic model is presented and studied for the transmission of TB-HIV/AIDS co-infection. Optimal control theory is then applied to investigate optimal strategies for controlling the spread of the disease using treatment of infected individuals with TB as the system control variables. Various combination strategies were examined so as to investigate the impact of the controls on the spread of the disease. And incremental cost-effectiveness ratio (ICER) was used to investigate the cost effectiveness of all the control strategies. Our results show that the implementation of the combination strategy involving the prevention of treatment failure in drug-sensitive TB infectious individuals and the treatment of individuals with drug-resistant TB is the most cost-effective control strategy. Similar results were obtained with different objective functionals involving the minimization of the number of individuals with drug-sensitive TB-only and drug-resistant TB-only with the efforts involved in applying the control. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Control design methods for floating wind turbines for optimal disturbance rejection

NASA Astrophysics Data System (ADS)

Lemmer, Frank; Schlipf, David; Cheng, Po Wen

2016-09-01

An analysis of the floating wind turbine as a multi-input-multi-output system investigating the effect of the control inputs on the system outputs is shown. These effects are compared to the ones of the disturbances from wind and waves in order to give insights for the selection of the control layout. The frequencies with the largest impact on the outputs due to limited effect of the controlled variables are identified. Finally, an optimal controller is designed as a benchmark and compared to a conventional PI-controller using only the rotor speed as input. Here, the previously found system properties, especially the difficulties to damp responses to wave excitation, are confirmed and verified through a spectral analysis with realistic environmental conditions. This comparison also assesses the quality of the employed simplified linear simulation model compared to the nonlinear model and shows that such an efficient frequency-domain evaluation for control design is feasible.

Multigrid one shot methods for optimal control problems: Infinite dimensional control

NASA Technical Reports Server (NTRS)

Arian, Eyal; Taasan, Shlomo

1994-01-01

The multigrid one shot method for optimal control problems, governed by elliptic systems, is introduced for the infinite dimensional control space. ln this case, the control variable is a function whose discrete representation involves_an increasing number of variables with grid refinement. The minimization algorithm uses Lagrange multipliers to calculate sensitivity gradients. A preconditioned gradient descent algorithm is accelerated by a set of coarse grids. It optimizes for different scales in the representation of the control variable on different discretization levels. An analysis which reduces the problem to the boundary is introduced. It is used to approximate the two level asymptotic convergence rate, to determine the amplitude of the minimization steps, and the choice of a high pass filter to be used when necessary. The effectiveness of the method is demonstrated on a series of test problems. The new method enables the solutions of optimal control problems at the same cost of solving the corresponding analysis problems just a few times.

On Optimizing H. 264/AVC Rate Control by Improving R-D Model and Incorporating HVS Characteristics

NASA Astrophysics Data System (ADS)

Zhu, Zhongjie; Wang, Yuer; Bai, Yongqiang; Jiang, Gangyi

2010-12-01

The state-of-the-art JVT-G012 rate control algorithm of H.264 is improved from two aspects. First, the quadratic rate-distortion (R-D) model is modified based on both empirical observations and theoretical analysis. Second, based on the existing physiological and psychological research findings of human vision, the rate control algorithm is optimized by incorporating the main characteristics of the human visual system (HVS) such as contrast sensitivity, multichannel theory, and masking effect. Experiments are conducted, and experimental results show that the improved algorithm can simultaneously enhance the overall subjective visual quality and improve the rate control precision effectively.

Development of Chemical Process Design and Control for ...

EPA Pesticide Factsheets

This contribution describes a novel process systems engineering framework that couples advanced control with sustainability evaluation and decision making for the optimization of process operations to minimize environmental impacts associated with products, materials, and energy. The implemented control strategy combines a biologically inspired method with optimal control concepts for finding more sustainable operating trajectories. The sustainability assessment of process operating points is carried out by using the U.S. E.P.A.’s Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) tool that provides scores for the selected indicators in the economic, material efficiency, environmental and energy areas. The indicator scores describe process performance on a sustainability measurement scale, effectively determining which operating point is more sustainable if there are more than several steady states for one specific product manufacturing. Through comparisons between a representative benchmark and the optimal steady-states obtained through implementation of the proposed controller, a systematic decision can be made in terms of whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous fermentation process for fuel production, whose materi

A disturbance based control/structure design algorithm

NASA Technical Reports Server (NTRS)

Mclaren, Mark D.; Slater, Gary L.

1989-01-01

Some authors take a classical approach to the simultaneous structure/control optimization by attempting to simultaneously minimize the weighted sum of the total mass and a quadratic form, subject to all of the structural and control constraints. Here, the optimization will be based on the dynamic response of a structure to an external unknown stochastic disturbance environment. Such a response to excitation approach is common to both the structural and control design phases, and hence represents a more natural control/structure optimization strategy than relying on artificial and vague control penalties. The design objective is to find the structure and controller of minimum mass such that all the prescribed constraints are satisfied. Two alternative solution algorithms are presented which have been applied to this problem. Each algorithm handles the optimization strategy and the imposition of the nonlinear constraints in a different manner. Two controller methodologies, and their effect on the solution algorithm, will be considered. These are full state feedback and direct output feedback, although the problem formulation is not restricted solely to these forms of controller. In fact, although full state feedback is a popular choice among researchers in this field (for reasons that will become apparent), its practical application is severely limited. The controller/structure interaction is inserted by the imposition of appropriate closed-loop constraints, such as closed-loop output response and control effort constraints. Numerical results will be obtained for a representative flexible structure model to illustrate the effectiveness of the solution algorithms.

Intelligent and robust optimization frameworks for smart grids

NASA Astrophysics Data System (ADS)

Dhansri, Naren Reddy

A smart grid implies a cyberspace real-time distributed power control system to optimally deliver electricity based on varying consumer characteristics. Although smart grids solve many of the contemporary problems, they give rise to new control and optimization problems with the growing role of renewable energy sources such as wind or solar energy. Under highly dynamic nature of distributed power generation and the varying consumer demand and cost requirements, the total power output of the grid should be controlled such that the load demand is met by giving a higher priority to renewable energy sources. Hence, the power generated from renewable energy sources should be optimized while minimizing the generation from non renewable energy sources. This research develops a demand-based automatic generation control and optimization framework for real-time smart grid operations by integrating conventional and renewable energy sources under varying consumer demand and cost requirements. Focusing on the renewable energy sources, the intelligent and robust control frameworks optimize the power generation by tracking the consumer demand in a closed-loop control framework, yielding superior economic and ecological benefits and circumvent nonlinear model complexities and handles uncertainties for superior real-time operations. The proposed intelligent system framework optimizes the smart grid power generation for maximum economical and ecological benefits under an uncertain renewable wind energy source. The numerical results demonstrate that the proposed framework is a viable approach to integrate various energy sources for real-time smart grid implementations. The robust optimization framework results demonstrate the effectiveness of the robust controllers under bounded power plant model uncertainties and exogenous wind input excitation while maximizing economical and ecological performance objectives. Therefore, the proposed framework offers a new worst-case deterministic optimization algorithm for smart grid automatic generation control.

Intelligent Tires Based on Measurement of Tire Deformation

NASA Astrophysics Data System (ADS)

Matsuzaki, Ryosuke; Todoroki, Akira

From a traffic safety point-of-view, there is an urgent need for intelligent tires as a warning system for road conditions, for optimized braking control on poor road surfaces and as a tire fault detection system. Intelligent tires, equipped with sensors for monitoring applied strain, are effective in improving reliability and control systems such as anti-lock braking systems (ABSs). In previous studies, we developed a direct tire deformation or strain measurement system with sufficiently low stiffness and high elongation for practical use, and a wireless communication system between tires and vehicle that operates without a battery. The present study investigates the application of strain data for an optimized braking control and road condition warning system. The relationships between strain sensor outputs and tire mechanical parameters, including braking torque, effective radius and contact patch length, are calculated using finite element analysis. Finally, we suggested the possibility of optimized braking control and road condition warning systems. Optimized braking control can be achieved by keeping the slip ratio constant. The road condition warning would be actuated if the recorded friction coefficient at a certain slip ratio is lower than a ‘safe’ reference value.

Intelligent tires for improved tire safety using wireless strain measurement

NASA Astrophysics Data System (ADS)

Matsuzaki, Ryosuke; Todoroki, Akira

2008-03-01

From a traffic safety point-of-view, there is an urgent need for intelligent tires as a warning system for road conditions, for optimized braking control on poor road surfaces and as a tire fault detection system. Intelligent tires, equipped with sensors for monitoring applied strain, are effective in improving reliability and control systems such as anti-lock braking systems (ABSs). In previous studies, we developed a direct tire deformation or strain measurement system with sufficiently low stiffness and high elongation for practical use, and a wireless communication system between tires and vehicle that operates without a battery. The present study investigates the application of strain data for an optimized braking control and road condition warning system. The relationships between strain sensor outputs and tire mechanical parameters, including braking torque, effective radius and contact patch length, are calculated using finite element analysis. Finally, we suggested the possibility of optimized braking control and road condition warning systems. Optimized braking control can be achieved by keeping the slip ratio constant. The road condition warning would be actuated if the recorded friction coefficient at a certain slip ratio is lower than a 'safe' reference value.

Investigating the optimal passive and active vibration controls of adjacent buildings based on performance indices using genetic algorithms

NASA Astrophysics Data System (ADS)

Hadi, Muhammad N. S.; Uz, Mehmet E.

2015-02-01

This study proposes the optimal passive and active damper parameters for achieving the best results in seismic response mitigation of coupled buildings connected to each other by dampers. The optimization to minimize the H2 and H∞ norms in the performance indices is carried out by genetic algorithms (GAs). The final passive and active damper parameters are checked for adjacent buildings connected to each other under El Centro NS 1940 and Kobe NS 1995 excitations. Using real coded GA in H∞ norm, the optimal controller gain is obtained by different combinations of the measurement as the feedback for designing the control force between the buildings. The proposed method is more effective than other metaheuristic methods and more feasible, although the control force increased. The results in the active control system show that the response of adjacent buildings is reduced in an efficient manner.

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

Pre-breeding food restriction promotes the optimization of parental investment in house mice, Mus musculus.

PubMed

Dušek, Adam; Bartoš, Luděk; Sedláček, František

2017-01-01

Litter size is one of the most reliable state-dependent life-history traits that indicate parental investment in polytocous (litter-bearing) mammals. The tendency to optimize litter size typically increases with decreasing availability of resources during the period of parental investment. To determine whether this tactic is also influenced by resource limitations prior to reproduction, we examined the effect of experimental, pre-breeding food restriction on the optimization of parental investment in lactating mice. First, we investigated the optimization of litter size in 65 experimental and 72 control families (mothers and their dependent offspring). Further, we evaluated pre-weaning offspring mortality, and the relationships between maternal and offspring condition (body weight), as well as offspring mortality, in 24 experimental and 19 control families with litter reduction (the death of one or more offspring). Assuming that pre-breeding food restriction would signal unpredictable food availability, we hypothesized that the optimization of parental investment would be more effective in the experimental rather than in the control mice. In comparison to the controls, the experimental mice produced larger litters and had a more selective (size-dependent) offspring mortality and thus lower litter reduction (the proportion of offspring deaths). Selective litter reduction helped the experimental mothers to maintain their own optimum condition, thereby improving the condition and, indirectly, the survival of their remaining offspring. Hence, pre-breeding resource limitations may have facilitated the mice to optimize their inclusive fitness. On the other hand, in the control females, the absence of environmental cues indicating a risky environment led to "maternal optimism" (overemphasizing good conditions at the time of breeding), which resulted in the production of litters of super-optimal size and consequently higher reproductive costs during lactation, including higher offspring mortality. Our study therefore provides the first evidence that pre-breeding food restriction promotes the optimization of parental investment, including offspring number and developmental success.

Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)

2016-01-01

Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

Optimization of the Controlled Evaluation of Closed Relational Queries

NASA Astrophysics Data System (ADS)

Biskup, Joachim; Lochner, Jan-Hendrik; Sonntag, Sebastian

For relational databases, controlled query evaluation is an effective inference control mechanism preserving confidentiality regarding a previously declared confidentiality policy. Implementations of controlled query evaluation usually lack efficiency due to costly theorem prover calls. Suitably constrained controlled query evaluation can be implemented efficiently, but is not flexible enough from the perspective of database users and security administrators. In this paper, we propose an optimized framework for controlled query evaluation in relational databases, being efficiently implementable on the one hand and relaxing the constraints of previous approaches on the other hand.

Changing smokers' risk perceptions--for better or worse?

PubMed

Myers, Lynn B

2014-03-01

This study investigated the effect of a smoking health message on smokers' comparative optimism. Two groups watched an anti-smoking scenario, with one group imagining being part of the scenario. Participants, including controls, completed comparative optimism ratings for four smoking-related illnesses. The intervention had negative consequences with both intervention groups reporting significantly higher comparative optimism versus the control group for all four smoking-related illnesses. It is concluded that media health messages can be powerful tools in changing comparative optimism but are influenced by peoples' prior perceptions. Health messages need to be systematically assessed to understand prior beliefs of the target audience.

A comparison of design variables for control theory based airfoil optimization

NASA Technical Reports Server (NTRS)

Reuther, James; Jameson, Antony

1995-01-01

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work in the area it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using either the potential flow or the Euler equations with either a conformal mapping or a general coordinate system. We have also explored three-dimensional extensions of these formulations recently. The goal of our present work is to demonstrate the versatility of the control theory approach by designing airfoils using both Hicks-Henne functions and B-spline control points as design variables. The research also demonstrates that the parameterization of the design space is an open question in aerodynamic design.

Mutation particle swarm optimization of the BP-PID controller for piezoelectric ceramics

NASA Astrophysics Data System (ADS)

Zheng, Huaqing; Jiang, Minlan

2016-01-01

PID control is the most common used method in industrial control because its structure is simple and it is easy to implement. PID controller has good control effect, now it has been widely used. However, PID method has a few limitations. The overshoot of the PID controller is very big. The adjustment time is long. When the parameters of controlled plant are changing over time, the parameters of controller could hardly change automatically to adjust to changing environment. Thus, it can't meet the demand of control quality in the process of controlling piezoelectric ceramic. In order to effectively control the piezoelectric ceramic and improve the control accuracy, this paper replaced the learning algorithm of the BP with the mutation particle swarm optimization algorithm(MPSO) on the process of the parameters setting of BP-PID. That designed a better self-adaptive controller which is combing the BP neural network based on mutation particle swarm optimization with the conventional PID control theory. This combination is called the MPSO-BP-PID. In the mechanism of the MPSO, the mutation operation is carried out with the fitness variance and the global best fitness value as the standard. That can overcome the precocious of the PSO and strengthen its global search ability. As a result, the MPSO-BP-PID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSO-BP-PID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BP-PID and PSO-BP-PID, it proved that MPSO is effective and the MPSO-BP-PID has stronger adaptability and robustness.

Optimal Micro-Jet Flow Control for Compact Air Vehicle Inlets

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Miller, Daniel N.; Addington, Gregory A.; Agrell, Johan

2004-01-01

The purpose of this study on micro-jet secondary flow control is to demonstrate the viability and economy of Response Surface Methodology (RSM) to optimally design micro-jet secondary flow control arrays, and to establish that the aeromechanical effects of engine face distortion can also be included in the design and optimization process. These statistical design concepts were used to investigate the design characteristics of "low mass" micro-jet array designs. The term "low mass" micro-jet may refers to fluidic jets with total (integrated) mass flow ratios between 0.10 and 1.0 percent of the engine face mass flow. Therefore, this report examines optimal micro-jet array designs for compact inlets through a Response Surface Methodology.

Safe-trajectory optimization and tracking control in ultra-close proximity to a failed satellite

NASA Astrophysics Data System (ADS)

Zhang, Jingrui; Chu, Xiaoyu; Zhang, Yao; Hu, Quan; Zhai, Guang; Li, Yanyan

2018-03-01

This paper presents a trajectory-optimization method for a chaser spacecraft operating in ultra-close proximity to a failed satellite. Based on the combination of active and passive trajectory protection, the constraints in the optimization framework are formulated for collision avoidance and successful docking in the presence of any thruster failure. The constraints are then handled by an adaptive Gauss pseudospectral method, in which the dynamic residuals are used as the metric to determine the distribution of collocation points. A finite-time feedback control is further employed in tracking the optimized trajectory. In particular, the stability and convergence of the controller are proved. Numerical results are given to demonstrate the effectiveness of the proposed methods.

Optimal management strategies in variable environments: Stochastic optimal control methods

USGS Publications Warehouse

Williams, B.K.

1985-01-01

Dynamic optimization was used to investigate the optimal defoliation of salt desert shrubs in north-western Utah. Management was formulated in the context of optimal stochastic control theory, with objective functions composed of discounted or time-averaged biomass yields. Climatic variability and community patterns of salt desert shrublands make the application of stochastic optimal control both feasible and necessary. A primary production model was used to simulate shrub responses and harvest yields under a variety of climatic regimes and defoliation patterns. The simulation results then were used in an optimization model to determine optimal defoliation strategies. The latter model encodes an algorithm for finite state, finite action, infinite discrete time horizon Markov decision processes. Three questions were addressed: (i) What effect do changes in weather patterns have on optimal management strategies? (ii) What effect does the discounting of future returns have? (iii) How do the optimal strategies perform relative to certain fixed defoliation strategies? An analysis was performed for the three shrub species, winterfat (Ceratoides lanata), shadscale (Atriplex confertifolia) and big sagebrush (Artemisia tridentata). In general, the results indicate substantial differences among species in optimal control strategies, which are associated with differences in physiological and morphological characteristics. Optimal policies for big sagebrush varied less with variation in climate, reserve levels and discount rates than did either shadscale or winterfat. This was attributed primarily to the overwintering of photosynthetically active tissue and to metabolic activity early in the growing season. Optimal defoliation of shadscale and winterfat generally was more responsive to differences in plant vigor and climate, reflecting the sensitivity of these species to utilization and replenishment of carbohydrate reserves. Similarities could be seen in the influence of both the discount rate and the climatic patterns on optimal harvest strategics. In general, decreases in either the discount rate or in the frequency of favorable weather patterns lcd to a more conservative defoliation policy. This did not hold, however, for plants in states of low vigor. Optimal control for shadscale and winterfat tended to stabilize on a policy of heavy defoliation stress, followed by one or more seasons of rest. Big sagebrush required a policy of heavy summer defoliation when sufficient active shoot material is present at the beginning of the growing season. The comparison of fixed and optimal strategies indicated considerable improvement in defoliation yields when optimal strategies are followed. The superior performance was attributable to increased defoliation of plants in states of high vigor. Improvements were found for both discounted and undiscounted yields.

Optimum aerodynamic design via boundary control

NASA Technical Reports Server (NTRS)

Jameson, Antony

1994-01-01

These lectures describe the implementation of optimization techniques based on control theory for airfoil and wing design. In previous studies it was shown that control theory could be used to devise an effective optimization procedure for two-dimensional profiles in which the shape is determined by a conformal transformation from a unit circle, and the control is the mapping function. Recently the method has been implemented in an alternative formulation which does not depend on conformal mapping, so that it can more easily be extended to treat general configurations. The method has also been extended to treat the Euler equations, and results are presented for both two and three dimensional cases, including the optimization of a swept wing.

Vibration suppression for large scale adaptive truss structures using direct output feedback control

NASA Technical Reports Server (NTRS)

Lu, Lyan-Ywan; Utku, Senol; Wada, Ben K.

1993-01-01

In this article, the vibration control of adaptive truss structures, where the control actuation is provided by length adjustable active members, is formulated as a direct output feedback control problem. A control method named Model Truncated Output Feedback (MTOF) is presented. The method allows the control feedback gain to be determined in a decoupled and truncated modal space in which only the critical vibration modes are retained. The on-board computation required by MTOF is minimal; thus, the method is favorable for the applications of vibration control of large scale structures. The truncation of the modal space inevitably introduces spillover effect during the control process. In this article, the effect is quantified in terms of active member locations, and it is shown that the optimal placement of active members, which minimizes the spillover effect (and thus, maximizes the control performance) can be sought. The problem of optimally selecting the locations of active members is also treated.

Design of distributed systems of hydrolithospere processes management. Selection of optimal number of extracting wells

NASA Astrophysics Data System (ADS)

Pershin, I. M.; Pervukhin, D. A.; Ilyushin, Y. V.; Afanaseva, O. V.

2017-10-01

The article considers the important issue of designing the distributed systems of hydrolithospere processes management. Control effects on the hydrolithospere processes are implemented by a set of extractive wells. The article shows how to determine the optimal number of extractive wells that provide a distributed control impact on the management object.

Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints.

PubMed

Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai

2015-07-01

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

Optimization of controllability and robustness of complex networks by edge directionality

NASA Astrophysics Data System (ADS)

Liang, Man; Jin, Suoqin; Wang, Dingjie; Zou, Xiufen

2016-09-01

Recently, controllability of complex networks has attracted enormous attention in various fields of science and engineering. How to optimize structural controllability has also become a significant issue. Previous studies have shown that an appropriate directional assignment can improve structural controllability; however, the evolution of the structural controllability of complex networks under attacks and cascading has always been ignored. To address this problem, this study proposes a new edge orientation method (NEOM) based on residual degree that changes the link direction while conserving topology and directionality. By comparing the results with those of previous methods in two random graph models and several realistic networks, our proposed approach is demonstrated to be an effective and competitive method for improving the structural controllability of complex networks. Moreover, numerical simulations show that our method is near-optimal in optimizing structural controllability. Strikingly, compared to the original network, our method maintains the structural controllability of the network under attacks and cascading, indicating that the NEOM can also enhance the robustness of controllability of networks. These results alter the view of the nature of controllability in complex networks, change the understanding of structural controllability and affect the design of network models to control such networks.

The algorithm for duration acceleration of repetitive projects considering the learning effect

NASA Astrophysics Data System (ADS)

Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan

2018-03-01

Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.

Disease and disaster: Optimal deployment of epidemic control facilities in a spatially heterogeneous population with changing behaviour.

PubMed

Gaythorpe, Katy; Adams, Ben

2016-05-21

Epidemics of water-borne infections often follow natural disasters and extreme weather events that disrupt water management processes. The impact of such epidemics may be reduced by deployment of transmission control facilities such as clinics or decontamination plants. Here we use a relatively simple mathematical model to examine how demographic and environmental heterogeneities, population behaviour, and behavioural change in response to the provision of facilities, combine to determine the optimal configurations of limited numbers of facilities to reduce epidemic size, and endemic prevalence. We show that, if the presence of control facilities does not affect behaviour, a good general rule for responsive deployment to minimise epidemic size is to place them in exactly the locations where they will directly benefit the most people. However, if infected people change their behaviour to seek out treatment then the deployment of facilities offering treatment can lead to complex effects that are difficult to foresee. So careful mathematical analysis is the only way to get a handle on the optimal deployment. Behavioural changes in response to control facilities can also lead to critical facility numbers at which there is a radical change in the optimal configuration. So sequential improvement of a control strategy by adding facilities to an existing optimal configuration does not always produce another optimal configuration. We also show that the pre-emptive deployment of control facilities has conflicting effects. The configurations that minimise endemic prevalence are very different to those that minimise epidemic size. So cost-benefit analysis of strategies to manage endemic prevalence must factor in the frequency of extreme weather events and natural disasters. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Control-Based Multidimensional Approach to the Role of Optimism in the Use of Dementia Day Care Services.

PubMed

Contador, Israel; Fernández-Calvo, Bernardino; Palenzuela, David L; Campos, Francisco Ramos; Rivera-Navarro, Jesús; de Lucena, Virginia Menezes

2015-11-01

We examined whether grounded optimism and external locus of control are associated with admission to dementia day care centers (DCCs). A total of 130 informal caregivers were recruited from the Alzheimer's Association in Salamanca (northwest Spain). All caregivers completed an assessment protocol that included the Battery of Generalized Expectancies of Control Scales (BEEGC-20, acronym in Spanish) as well as depression and burden measures. The decision of the care setting at baseline assessment (own home vs DCC) was considered the main outcome measure in the logistic regression analyses. Grounded optimism was a preventive factor for admission (odds ratio [OR]: 0.34 and confidence interval [CI]: 0.15-0.75), whereas external locus of control (OR: 2.75, CI: 1.25-6.03) increased the probabilities of using DCCs. Depression mediated the relationship between optimism and DCCs, but this effect was not consistent for burden. Grounded optimism promotes the extension of care at home for patients with dementia. © The Author(s) 2013.

The effects of Poynting-Robertson drag on solar sails

NASA Astrophysics Data System (ADS)

Abd El-Salam, F. A.

2018-06-01

In the present work, the concept of solar sailing and its developing spacecraft are presented. The effects of Poynting-Robertson drag on solar sails are considered. Some analytical control laws with some mentioned input constraints for optimizing solar sails dynamics in heliocentric orbit using Lagrange's planetary equations are obtained. Optimum force vector in a required direction is maximized by deriving optimal sail cone angle. New control laws that maximize thrust to obtain certain required maximization in some particular orbital element are obtained.

The effect of different control point sampling sequences on convergence of VMAT inverse planning

NASA Astrophysics Data System (ADS)

Pardo Montero, Juan; Fenwick, John D.

2011-04-01

A key component of some volumetric-modulated arc therapy (VMAT) optimization algorithms is the progressive addition of control points to the optimization. This idea was introduced in Otto's seminal VMAT paper, in which a coarse sampling of control points was used at the beginning of the optimization and new control points were progressively added one at a time. A different form of the methodology is also present in the RapidArc optimizer, which adds new control points in groups called 'multiresolution levels', each doubling the number of control points in the optimization. This progressive sampling accelerates convergence, improving the results obtained, and has similarities with the ordered subset algorithm used to accelerate iterative image reconstruction. In this work we have used a VMAT optimizer developed in-house to study the performance of optimization algorithms which use different control point sampling sequences, most of which fall into three different classes: doubling sequences, which add new control points in groups such that the number of control points in the optimization is (roughly) doubled; Otto-like progressive sampling which adds one control point at a time, and equi-length sequences which contain several multiresolution levels each with the same number of control points. Results are presented in this study for two clinical geometries, prostate and head-and-neck treatments. A dependence of the quality of the final solution on the number of starting control points has been observed, in agreement with previous works. We have found that some sequences, especially E20 and E30 (equi-length sequences with 20 and 30 multiresolution levels, respectively), generate better results than a 5 multiresolution level RapidArc-like sequence. The final value of the cost function is reduced up to 20%, such reductions leading to small improvements in dosimetric parameters characterizing the treatments—slightly more homogeneous target doses and better sparing of the organs at risk.

Optimal control of the power adiabatic stroke of an optomechanical heat engine.

PubMed

Bathaee, M; Bahrampour, A R

2016-08-01

We consider the power adiabatic stroke of the Otto optomechanical heat engine introduced in Phys. Rev. Lett. 112, 150602 (2014)PRLTAO0031-900710.1103/PhysRevLett.112.150602. We derive the maximum extractable work of both optomechanical normal modes in the minimum time while the system experiences quantum friction effects. We show that the total work done by the system in the power adiabatic stroke is optimized by a bang-bang control. The time duration of the power adiabatic stroke is of the order of the inverse of the effective optomechanical-coupling coefficient. The optimal phase-space trajectory of the Otto cycle for both optomechanical normal modes is also obtained.

Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices.

PubMed

Qiao, Wei; Venayagamoorthy, Ganesh K; Harley, Ronald G

2008-01-01

Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system.

The Effect of Antiretroviral Combination Treatment on Epstein-Barr Virus (EBV) Genome Load in HIV-Infected Patients

PubMed Central

Friis, Anna M. C.; Gyllensten, Katarina; Aleman, Anna; Ernberg, Ingemar; Åkerlund, Börje

2010-01-01

We evaluated the effect of combination anti-retroviral treatment (cART) on the host control of EBV infection in moderately immunosuppressed HIV-1 patients. Twenty HIV-1 infected individuals were followed for five years with repeated measurements of EBV DNA load in peripheral blood lymphocytes in relation to HIV-RNA titers and CD4+ cell counts. Individuals with optimal response, i.e. durable non-detectable HIV-RNA, showed a decline of EBV load to the level of healthy controls. Individuals with non-optimal HIV-1 control did not restore their EBV control. Long-lasting suppression of HIV-replication after early initiation of cART is a prerequisite for re-establishing the immune control of EBV. PMID:21994658

Analysis of recruitment and industrial human resources management for optimal productivity in the presence of the HIV/AIDS epidemic.

PubMed

Okosun, Kazeem O; Makinde, Oluwole D; Takaidza, Isaac

2013-01-01

The aim of this paper is to analyze the recruitment effects of susceptible and infected individuals in order to assess the productivity of an organizational labor force in the presence of HIV/AIDS with preventive and HAART treatment measures in enhancing the workforce output. We consider constant controls as well as time-dependent controls. In the constant control case, we calculate the basic reproduction number and investigate the existence and stability of equilibria. The model is found to exhibit backward and Hopf bifurcations, implying that for the disease to be eradicated, the basic reproductive number must be below a critical value of less than one. We also investigate, by calculating sensitivity indices, the sensitivity of the basic reproductive number to the model's parameters. In the time-dependent control case, we use Pontryagin's maximum principle to derive necessary conditions for the optimal control of the disease. Finally, numerical simulations are performed to illustrate the analytical results. The cost-effectiveness analysis results show that optimal efforts on recruitment (HIV screening of applicants, etc.) is not the most cost-effective strategy to enhance productivity in the organizational labor force. Hence, to enhance employees' productivity, effective education programs and strict adherence to preventive measures should be promoted.

A novel medical information management and decision model for uncertain demand optimization.

PubMed

Bi, Ya

2015-01-01

Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions. To establish a novel medical information management and decision model for uncertain demand optimization. A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm. The optimal management and decision model can effectively reduce the medicine inventory cost. The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.

Multidisciplinary Optimization Approach for Design and Operation of Constrained and Complex-shaped Space Systems

NASA Astrophysics Data System (ADS)

Lee, Dae Young

The design of a small satellite is challenging since they are constrained by mass, volume, and power. To mitigate these constraint effects, designers adopt deployable configurations on the spacecraft that result in an interesting and difficult optimization problem. The resulting optimization problem is challenging due to the computational complexity caused by the large number of design variables and the model complexity created by the deployables. Adding to these complexities, there is a lack of integration of the design optimization systems into operational optimization, and the utility maximization of spacecraft in orbit. The developed methodology enables satellite Multidisciplinary Design Optimization (MDO) that is extendable to on-orbit operation. Optimization of on-orbit operations is possible with MDO since the model predictive controller developed in this dissertation guarantees the achievement of the on-ground design behavior in orbit. To enable the design optimization of highly constrained and complex-shaped space systems, the spherical coordinate analysis technique, called the "Attitude Sphere", is extended and merged with an additional engineering tools like OpenGL. OpenGL's graphic acceleration facilitates the accurate estimation of the shadow-degraded photovoltaic cell area. This technique is applied to the design optimization of the satellite Electric Power System (EPS) and the design result shows that the amount of photovoltaic power generation can be increased more than 9%. Based on this initial methodology, the goal of this effort is extended from Single Discipline Optimization to Multidisciplinary Optimization, which includes the design and also operation of the EPS, Attitude Determination and Control System (ADCS), and communication system. The geometry optimization satisfies the conditions of the ground development phase; however, the operation optimization may not be as successful as expected in orbit due to disturbances. To address this issue, for the ADCS operations, controllers based on Model Predictive Control that are effective for constraint handling were developed and implemented. All the suggested design and operation methodologies are applied to a mission "CADRE", which is space weather mission scheduled for operation in 2016. This application demonstrates the usefulness and capability of the methodology to enhance CADRE's capabilities, and its ability to be applied to a variety of missions.

Design of clinical trials involving multiple hypothesis tests with a common control.

PubMed

Schou, I Manjula; Marschner, Ian C

2017-07-01

Randomized clinical trials comparing several treatments to a common control are often reported in the medical literature. For example, multiple experimental treatments may be compared with placebo, or in combination therapy trials, a combination therapy may be compared with each of its constituent monotherapies. Such trials are typically designed using a balanced approach in which equal numbers of individuals are randomized to each arm, however, this can result in an inefficient use of resources. We provide a unified framework and new theoretical results for optimal design of such single-control multiple-comparator studies. We consider variance optimal designs based on D-, A-, and E-optimality criteria, using a general model that allows for heteroscedasticity and a range of effect measures that include both continuous and binary outcomes. We demonstrate the sensitivity of these designs to the type of optimality criterion by showing that the optimal allocation ratios are systematically ordered according to the optimality criterion. Given this sensitivity to the optimality criterion, we argue that power optimality is a more suitable approach when designing clinical trials where testing is the objective. Weighted variance optimal designs are also discussed, which, like power optimal designs, allow the treatment difference to play a major role in determining allocation ratios. We illustrate our methods using two real clinical trial examples taken from the medical literature. Some recommendations on the use of optimal designs in single-control multiple-comparator trials are also provided. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Display/control requirements for automated VTOL aircraft

NASA Technical Reports Server (NTRS)

Hoffman, W. C.; Kleinman, D. L.; Young, L. R.

1976-01-01

A systematic design methodology for pilot displays in advanced commercial VTOL aircraft was developed and refined. The analyst is provided with a step-by-step procedure for conducting conceptual display/control configurations evaluations for simultaneous monitoring and control pilot tasks. The approach consists of three phases: formulation of information requirements, configuration evaluation, and system selection. Both the monitoring and control performance models are based upon the optimal control model of the human operator. Extensions to the conventional optimal control model required in the display design methodology include explicit optimization of control/monitoring attention; simultaneous monitoring and control performance predictions; and indifference threshold effects. The methodology was applied to NASA's experimental CH-47 helicopter in support of the VALT program. The CH-47 application examined the system performance of six flight conditions. Four candidate configurations are suggested for evaluation in pilot-in-the-loop simulations and eventual flight tests.

Analytical design and evaluation of an active control system for helicopter vibration reduction and gust response alleviation

NASA Technical Reports Server (NTRS)

Taylor, R. B.; Zwicke, P. E.; Gold, P.; Miao, W.

1980-01-01

An analytical study was conducted to define the basic configuration of an active control system for helicopter vibration and gust response alleviation. The study culminated in a control system design which has two separate systems: narrow band loop for vibration reduction and wider band loop for gust response alleviation. The narrow band vibration loop utilizes the standard swashplate control configuration to input controller for the vibration loop is based on adaptive optimal control theory and is designed to adapt to any flight condition including maneuvers and transients. The prime characteristics of the vibration control system is its real time capability. The gust alleviation control system studied consists of optimal sampled data feedback gains together with an optimal one-step-ahead prediction. The prediction permits the estimation of the gust disturbance which can then be used to minimize the gust effects on the helicopter.

Solving fuel-optimal low-thrust orbital transfers with bang-bang control using a novel continuation technique

NASA Astrophysics Data System (ADS)

Zhu, Zhengfan; Gan, Qingbo; Yang, Xin; Gao, Yang

2017-08-01

We have developed a novel continuation technique to solve optimal bang-bang control for low-thrust orbital transfers considering the first-order necessary optimality conditions derived from Lawden's primer vector theory. Continuation on the thrust amplitude is mainly described in this paper. Firstly, a finite-thrust transfer with an ;On-Off-On; thrusting sequence is modeled using a two-impulse transfer as initial solution, and then the thrust amplitude is decreased gradually to find an optimal solution with minimum thrust. Secondly, the thrust amplitude is continued from its minimum value to positive infinity to find the optimal bang-bang control, and a thrust switching principle is employed to determine the control structure by monitoring the variation of the switching function. In the continuation process, a bifurcation of bang-bang control is revealed and the concept of critical thrust is proposed to illustrate this phenomenon. The same thrust switching principle is also applicable to the continuation on other parameters, such as transfer time, orbital phase angle, etc. By this continuation technique, fuel-optimal orbital transfers with variable mission parameters can be found via an automated algorithm, and there is no need to provide an initial guess for the costate variables. Moreover, continuation is implemented in the solution space of bang-bang control that is either optimal or non-optimal, which shows that a desired solution of bang-bang control is obtained via continuation on a single parameter starting from an existing solution of bang-bang control. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed continuation technique. Specifically, this continuation technique provides an approach to find multiple solutions satisfying the first-order necessary optimality conditions to the same orbital transfer problem, and a continuation strategy is presented as a preliminary approach for solving the bang-bang control of many-revolution orbital transfers.

Optimal Fault-Tolerant Control for Discrete-Time Nonlinear Strict-Feedback Systems Based on Adaptive Critic Design.

PubMed

Wang, Zhanshan; Liu, Lei; Wu, Yanming; Zhang, Huaguang

2018-06-01

This paper investigates the problem of optimal fault-tolerant control (FTC) for a class of unknown nonlinear discrete-time systems with actuator fault in the framework of adaptive critic design (ACD). A pivotal highlight is the adaptive auxiliary signal of the actuator fault, which is designed to offset the effect of the fault. The considered systems are in strict-feedback forms and involve unknown nonlinear functions, which will result in the causal problem. To solve this problem, the original nonlinear systems are transformed into a novel system by employing the diffeomorphism theory. Besides, the action neural networks (ANNs) are utilized to approximate a predefined unknown function in the backstepping design procedure. Combined the strategic utility function and the ACD technique, a reinforcement learning algorithm is proposed to set up an optimal FTC, in which the critic neural networks (CNNs) provide an approximate structure of the cost function. In this case, it not only guarantees the stability of the systems, but also achieves the optimal control performance as well. In the end, two simulation examples are used to show the effectiveness of the proposed optimal FTC strategy.

Topology optimization of unsteady flow problems using the lattice Boltzmann method

NASA Astrophysics Data System (ADS)

Nørgaard, Sebastian; Sigmund, Ole; Lazarov, Boyan

2016-02-01

This article demonstrates and discusses topology optimization for unsteady incompressible fluid flows. The fluid flows are simulated using the lattice Boltzmann method, and a partial bounceback model is implemented to model the transition between fluid and solid phases in the optimization problems. The optimization problem is solved with a gradient based method, and the design sensitivities are computed by solving the discrete adjoint problem. For moderate Reynolds number flows, it is demonstrated that topology optimization can successfully account for unsteady effects such as vortex shedding and time-varying boundary conditions. Such effects are relevant in several engineering applications, i.e. fluid pumps and control valves.

One shot methods for optimal control of distributed parameter systems 1: Finite dimensional control

NASA Technical Reports Server (NTRS)

Taasan, Shlomo

1991-01-01

The efficient numerical treatment of optimal control problems governed by elliptic partial differential equations (PDEs) and systems of elliptic PDEs, where the control is finite dimensional is discussed. Distributed control as well as boundary control cases are discussed. The main characteristic of the new methods is that they are designed to solve the full optimization problem directly, rather than accelerating a descent method by an efficient multigrid solver for the equations involved. The methods use the adjoint state in order to achieve efficient smoother and a robust coarsening strategy. The main idea is the treatment of the control variables on appropriate scales, i.e., control variables that correspond to smooth functions are solved for on coarse grids depending on the smoothness of these functions. Solution of the control problems is achieved with the cost of solving the constraint equations about two to three times (by a multigrid solver). Numerical examples demonstrate the effectiveness of the method proposed in distributed control case, pointwise control and boundary control problems.

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimized plasma actuation on asymmetric vortex over a slender body

NASA Astrophysics Data System (ADS)

Long, Yuexiao; Li, Huaxing; Meng, Xuanshi; Hu, Haiyang

2018-01-01

Detailed particle-image-velocimetry and surface pressure measurements are conducted to study asymmetric vortex control over a slender body at high angles of attack by using a pair of optimized alternating current surface-dielectric-barrier discharge plasma actuators. The Reynolds number based on the base diameter of the model is ReD = 3.8 × 105. Steady and duty-cycle manipulations are employed. The results demonstrate the effectiveness of the optimized actuator with a thick Teflon barrier at a high free-stream speed. Perfect linear proportional control is also achieved under duty-cycle control with a reduced frequency of f+ = 0.17.

Stochastic optimal control of non-stationary response of a single-degree-of-freedom vehicle model

NASA Astrophysics Data System (ADS)

Narayanan, S.; Raju, G. V.

1990-09-01

An active suspension system to control the non-stationary response of a single-degree-of-freedom (sdf) vehicle model with variable velocity traverse over a rough road is investigated. The suspension is optimized with respect to ride comfort and road holding, using stochastic optimal control theory. The ground excitation is modelled as a spatial homogeneous random process, being the output of a linear shaping filter to white noise. The effect of the rolling contact of the tyre is considered by an additional filter in cascade. The non-stationary response with active suspension is compared with that of a passive system.

Solvability of some partial functional integrodifferential equations with finite delay and optimal controls in Banach spaces.

PubMed

Ezzinbi, Khalil; Ndambomve, Patrice

2016-01-01

In this work, we consider the control system governed by some partial functional integrodifferential equations with finite delay in Banach spaces. We assume that the undelayed part admits a resolvent operator in the sense of Grimmer. Firstly, some suitable conditions are established to guarantee the existence and uniqueness of mild solutions for a broad class of partial functional integrodifferential infinite dimensional control systems. Secondly, it is proved that, under generally mild conditions of cost functional, the associated Lagrange problem has an optimal solution, and that for each optimal solution there is a minimizing sequence of the problem that converges to the optimal solution with respect to the trajectory, the control, and the functional in appropriate topologies. Our results extend and complement many other important results in the literature. Finally, a concrete example of application is given to illustrate the effectiveness of our main results.

Design of an optimal preview controller for linear discrete-time descriptor systems with state delay

NASA Astrophysics Data System (ADS)

Cao, Mengjuan; Liao, Fucheng

2015-04-01

In this paper, the linear discrete-time descriptor system with state delay is studied, and a design method for an optimal preview controller is proposed. First, by using the discrete lifting technique, the original system is transformed into a general descriptor system without state delay in form. Then, taking advantage of the first-order forward difference operator, we construct a descriptor augmented error system, including the state vectors of the lifted system, error vectors, and desired target signals. Rigorous mathematical proofs are given for the regularity, stabilisability, causal controllability, and causal observability of the descriptor augmented error system. Based on these, the optimal preview controller with preview feedforward compensation for the original system is obtained by using the standard optimal regulator theory of the descriptor system. The effectiveness of the proposed method is shown by numerical simulation.

Order of events matter: comparing discrete models for optimal control of species augmentation.

PubMed

Bodine, Erin N; Gross, Louis J; Lenhart, Suzanne

2012-01-01

We investigate optimal timing of augmentation of an endangered/threatened species population in a target region by moving individuals from a reserve or captive population. This is formulated as a discrete-time optimal control problem in which augmentation occurs once per time period over a fixed number of time periods. The population model assumes the Allee effect growth functions in both target and reserve populations and the control objective is to maximize the target and reserve population sizes over the time horizon while accounting for costs of augmentation. Two possible orders of events are considered for different life histories of the species relative to augmentation time: move individuals either before or after population growth occurs. The control variable is the proportion of the reserve population to be moved to the target population. We develop solutions and illustrate numerical results which indicate circumstances for which optimal augmentation strategies depend upon the order of events.

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

Collective Responsibility, Academic Optimism, and Student Achievement in Taiwan Elementary Schools

ERIC Educational Resources Information Center

Wu, Hsin-Chieh

2012-01-01

Previous research indicates that collective efficacy, faculty trust in students and parents, and academic emphasis together formed a single latent school construct, called academic optimism. In the U.S., academic optimism has been proven to be a powerful construct that could effectively predict student achievement even after controlling for…

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

PubMed Central

Baygin, Mehmet; Karakose, Mehmet

2013-01-01

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods. PMID:23935433

OPUS: Optimal Projection for Uncertain Systems. Volume 1

DTIC Science & Technology

1991-09-01

unifiedI control- design methodology that directly addresses these technology issues. 1 In particular, optimal projection theory addresses the need for...effects, and limited identification accuracy in a 1-g environment. The principal contribution of OPUS is a unified design methodology that...characterizing solutions to constrained control- design problems. Transforming OPUS into a practi- cal design methodology requires the development of

Finite Energy and Bounded Actuator Attacks on Cyber-Physical Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Djouadi, Seddik M; Melin, Alexander M; Ferragut, Erik M

As control system networks are being connected to enterprise level networks for remote monitoring, operation, and system-wide performance optimization, these same connections are providing vulnerabilities that can be exploited by malicious actors for attack, financial gain, and theft of intellectual property. Much effort in cyber-physical system (CPS) protection has focused on protecting the borders of the system through traditional information security techniques. Less effort has been applied to the protection of cyber-physical systems from intelligent attacks launched after an attacker has defeated the information security protections to gain access to the control system. In this paper, attacks on actuator signalsmore » are analyzed from a system theoretic context. The threat surface is classified into finite energy and bounded attacks. These two broad classes encompass a large range of potential attacks. The effect of theses attacks on a linear quadratic (LQ) control are analyzed, and the optimal actuator attacks for both finite and infinite horizon LQ control are derived, therefore the worst case attack signals are obtained. The closed-loop system under the optimal attack signals is given and a numerical example illustrating the effect of an optimal bounded attack is provided.« less

Adaptive control for solar energy based DC microgrid system development

NASA Astrophysics Data System (ADS)

Zhang, Qinhao

During the upgrading of current electric power grid, it is expected to develop smarter, more robust and more reliable power systems integrated with distributed generations. To realize these objectives, traditional control techniques are no longer effective in either stabilizing systems or delivering optimal and robust performances. Therefore, development of advanced control methods has received increasing attention in power engineering. This work addresses two specific problems in the control of solar panel based microgrid systems. First, a new control scheme is proposed for the microgrid systems to achieve optimal energy conversion ratio in the solar panels. The control system can optimize the efficiency of the maximum power point tracking (MPPT) algorithm by implementing two layers of adaptive control. Such a hierarchical control architecture has greatly improved the system performance, which is validated through both mathematical analysis and computer simulation. Second, in the development of the microgrid transmission system, the issues related to the tele-communication delay and constant power load (CPL)'s negative incremental impedance are investigated. A reference model based method is proposed for pole and zero placements that address the challenges of the time delay and CPL in closed-loop control. The effectiveness of the proposed modeling and control design methods are demonstrated in a simulation testbed. Practical aspects of the proposed methods for general microgrid systems are also discussed.

Optimality study of a gust alleviation system for light wing-loading STOL aircraft

NASA Technical Reports Server (NTRS)

Komoda, M.

1976-01-01

An analytical study was made of an optimal gust alleviation system that employs a vertical gust sensor mounted forward of an aircraft's center of gravity. Frequency domain optimization techniques were employed to synthesize the optimal filters that process the corrective signals to the flaps and elevator actuators. Special attention was given to evaluating the effectiveness of lead time, that is, the time by which relative wind sensor information should lead the actual encounter of the gust. The resulting filter is expressed as an implicit function of the prescribed control cost. A numerical example for a light wing loading STOL aircraft is included in which the optimal trade-off between performance and control cost is systematically studied.

Self-optimizing Pitch Control for Large Scale Wind Turbine Based on ADRC

NASA Astrophysics Data System (ADS)

Xia, Anjun; Hu, Guoqing; Li, Zheng; Huang, Dongxiao; Wang, Fengxiang

2018-01-01

Since wind turbine is a complex nonlinear and strong coupling system, traditional PI control method can hardly achieve good control performance. A self-optimizing pitch control method based on the active-disturbance-rejection control theory is proposed in this paper. A linear model of the wind turbine is derived by linearizing the aerodynamic torque equation and the dynamic response of wind turbine is transformed into a first-order linear system. An expert system is designed to optimize the amplification coefficient according to the pitch rate and the speed deviation. The purpose of the proposed control method is to regulate the amplification coefficient automatically and keep the variations of pitch rate and rotor speed in proper ranges. Simulation results show that the proposed pitch control method has the ability to modify the amplification coefficient effectively, when it is not suitable, and keep the variations of pitch rate and rotor speed in proper ranges

Experimental evaluation of HJB optimal controllers for the attitude dynamics of a multirotor aerial vehicle.

PubMed

Prado, Igor Afonso Acampora; Pereira, Mateus de Freitas Virgílio; de Castro, Davi Ferreira; Dos Santos, Davi Antônio; Balthazar, Jose Manoel

2018-06-01

The present paper is concerned with the design and experimental evaluation of optimal control laws for the nonlinear attitude dynamics of a multirotor aerial vehicle. Three design methods based on Hamilton-Jacobi-Bellman equation are taken into account. The first one is a linear control with guarantee of stability for nonlinear systems. The second and third are a nonlinear suboptimal control techniques. These techniques are based on an optimal control design approach that takes into account the nonlinearities present in the vehicle dynamics. The stability Proof of the closed-loop system is presented. The performance of the control system designed is evaluated via simulations and also via an experimental scheme using the Quanser 3-DOF Hover. The experiments show the effectiveness of the linear control method over the nonlinear strategy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Active control of transient rotordynamic vibration by optimal control methods

NASA Technical Reports Server (NTRS)

Palazzolo, A. B.; Lin, R. R.; Alexander, R. M.; Kascak, A. F.

1988-01-01

Although considerable effort has been put into the study of steady state vibration control, there are few methods applicable to transient vibration control of rotorbearing systems. In this paper optimal control theory has been adopted to minimize rotor vibration due to sudden imbalance, e.g., blade loss. The system gain matrix is obtained by choosing the weighting matrices and solving the Riccati equation. Control forces are applied to the system via a feedback loop. A seven mass rotor system is simulated for illustration. A relationship between the number of sensors and the number of modes used in the optimal control model is investigated. Comparisons of responses are made for various configurations of modes, sensors, and actuators. Furthermore, spillover effect is examined by comparing results from collocated and noncollocated sensor configurations. Results show that shaft vibration is significantly attenuated in the closed loop system.

The Dilution Effect and Information Integration in Perceptual Decision Making

PubMed Central

Hotaling, Jared M.; Cohen, Andrew L.; Shiffrin, Richard M.; Busemeyer, Jerome R.

2015-01-01

In cognitive science there is a seeming paradox: On the one hand, studies of human judgment and decision making have repeatedly shown that people systematically violate optimal behavior when integrating information from multiple sources. On the other hand, optimal models, often Bayesian, have been successful at accounting for information integration in fields such as categorization, memory, and perception. This apparent conflict could be due, in part, to different materials and designs that lead to differences in the nature of processing. Stimuli that require controlled integration of information, such as the quantitative or linguistic information (commonly found in judgment studies), may lead to suboptimal performance. In contrast, perceptual stimuli may lend themselves to automatic processing, resulting in integration that is closer to optimal. We tested this hypothesis with an experiment in which participants categorized faces based on resemblance to a family patriarch. The amount of evidence contained in the top and bottom halves of each test face was independently manipulated. These data allow us to investigate a canonical example of sub-optimal information integration from the judgment and decision making literature, the dilution effect. Splitting the top and bottom halves of a face, a manipulation meant to encourage controlled integration of information, produced farther from optimal behavior and larger dilution effects. The Multi-component Information Accumulation model, a hybrid optimal/averaging model of information integration, successfully accounts for key accuracy, response time, and dilution effects. PMID:26406323

The Dilution Effect and Information Integration in Perceptual Decision Making.

PubMed

Hotaling, Jared M; Cohen, Andrew L; Shiffrin, Richard M; Busemeyer, Jerome R

2015-01-01

In cognitive science there is a seeming paradox: On the one hand, studies of human judgment and decision making have repeatedly shown that people systematically violate optimal behavior when integrating information from multiple sources. On the other hand, optimal models, often Bayesian, have been successful at accounting for information integration in fields such as categorization, memory, and perception. This apparent conflict could be due, in part, to different materials and designs that lead to differences in the nature of processing. Stimuli that require controlled integration of information, such as the quantitative or linguistic information (commonly found in judgment studies), may lead to suboptimal performance. In contrast, perceptual stimuli may lend themselves to automatic processing, resulting in integration that is closer to optimal. We tested this hypothesis with an experiment in which participants categorized faces based on resemblance to a family patriarch. The amount of evidence contained in the top and bottom halves of each test face was independently manipulated. These data allow us to investigate a canonical example of sub-optimal information integration from the judgment and decision making literature, the dilution effect. Splitting the top and bottom halves of a face, a manipulation meant to encourage controlled integration of information, produced farther from optimal behavior and larger dilution effects. The Multi-component Information Accumulation model, a hybrid optimal/averaging model of information integration, successfully accounts for key accuracy, response time, and dilution effects.

Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

NASA Astrophysics Data System (ADS)

Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

2017-10-01

Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

[Feedforward control strategy and its application in quality improvement of ethanol precipitation process of danhong injection].

PubMed

Yan, Bin-Jun; Guo, Zheng-Tai; Qu, Hai-Bin; Zhao, Bu-Chang; Zhao, Tao

2013-06-01

In this work, a feedforward control strategy basing on the concept of quality by design was established for the manufacturing process of traditional Chinese medicine to reduce the impact of the quality variation of raw materials on drug. In the research, the ethanol precipitation process of Danhong injection was taken as an application case of the method established. Box-Behnken design of experiments was conducted. Mathematical models relating the attributes of the concentrate, the process parameters and the quality of the supernatants produced were established. Then an optimization model for calculating the best process parameters basing on the attributes of the concentrate was built. The quality of the supernatants produced by ethanol precipitation with optimized and non-optimized process parameters were compared. The results showed that using the feedforward control strategy for process parameters optimization can control the quality of the supernatants effectively. The feedforward control strategy proposed can enhance the batch-to-batch consistency of the supernatants produced by ethanol precipitation.

Effect of interaction strength on robustness of controlling edge dynamics in complex networks

NASA Astrophysics Data System (ADS)

Pang, Shao-Peng; Hao, Fei

2018-05-01

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

Adaptive non-linear control for cancer therapy through a Fokker-Planck observer.

PubMed

Shakeri, Ehsan; Latif-Shabgahi, Gholamreza; Esmaeili Abharian, Amir

2018-04-01

In recent years, many efforts have been made to present optimal strategies for cancer therapy through the mathematical modelling of tumour-cell population dynamics and optimal control theory. In many cases, therapy effect is included in the drift term of the stochastic Gompertz model. By fitting the model with empirical data, the parameters of therapy function are estimated. The reported research works have not presented any algorithm to determine the optimal parameters of therapy function. In this study, a logarithmic therapy function is entered in the drift term of the Gompertz model. Using the proposed control algorithm, the therapy function parameters are predicted and adaptively adjusted. To control the growth of tumour-cell population, its moments must be manipulated. This study employs the probability density function (PDF) control approach because of its ability to control all the process moments. A Fokker-Planck-based non-linear stochastic observer will be used to determine the PDF of the process. A cost function based on the difference between a predefined desired PDF and PDF of tumour-cell population is defined. Using the proposed algorithm, the therapy function parameters are adjusted in such a manner that the cost function is minimised. The existence of an optimal therapy function is also proved. The numerical results are finally given to demonstrate the effectiveness of the proposed method.

Optimal control to modelling motorcycle rider steering: local versus global coordinate systems in rider preview

NASA Astrophysics Data System (ADS)

Rowell, S.; Popov, A. A.; Meijaard, J. P.

2010-04-01

The response of a motorcycle is heavily dependent on the rider's control actions, and consequently a means of replicating the rider's behaviour provides an important extension to motorcycle dynamics. The primary objective here is to develop effective path-following simulations and to understand how riders control motorcycles. Optimal control theory is applied to the tracking of roadway by a motorcycle, using a non-linear motorcycle model operating in free control by steering torque input. A path-following controller with road preview is designed by minimising tracking errors and control effort. Tight controls with high weightings on performance and loose controls with high weightings on control power are defined. Special attention is paid to the modelling of multipoint preview in local and global coordinate systems. The controller model is simulated over a standard single lane-change manoeuvre. It is argued that the local coordinates point of view is more representative of the way that a human rider operates and interprets information. The simulations suggest that for accurate path following, using optimal control, the problem must be solved by the local coordinates approach in order to achieve accurate results with short preview horizons. Furthermore, some weaknesses of the optimal control approach are highlighted here.

Fault-Tolerant Control of ANPC Three-Level Inverter Based on Order-Reduction Optimal Control Strategy under Multi-Device Open-Circuit Fault.

PubMed

Xu, Shi-Zhou; Wang, Chun-Jie; Lin, Fang-Li; Li, Shi-Xiang

2017-10-31

The multi-device open-circuit fault is a common fault of ANPC (Active Neutral-Point Clamped) three-level inverter and effect the operation stability of the whole system. To improve the operation stability, this paper summarized the main solutions currently firstly and analyzed all the possible states of multi-device open-circuit fault. Secondly, an order-reduction optimal control strategy was proposed under multi-device open-circuit fault to realize fault-tolerant control based on the topology and control requirement of ANPC three-level inverter and operation stability. This control strategy can solve the faults with different operation states, and can works in order-reduction state under specific open-circuit faults with specific combined devices, which sacrifices the control quality to obtain the stability priority control. Finally, the simulation and experiment proved the effectiveness of the proposed strategy.

F-18 High Alpha Research Vehicle (HARV) parameter identification flight test maneuvers for optimal input design validation and lateral control effectiveness

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1995-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for open loop parameter identification purposes, specifically for optimal input design validation at 5 degrees angle of attack, identification of individual strake effectiveness at 40 and 50 degrees angle of attack, and study of lateral dynamics and lateral control effectiveness at 40 and 50 degrees angle of attack. Each maneuver is to be realized by applying square wave inputs to specific control effectors using the On-Board Excitation System (OBES). Maneuver descriptions and complete specifications of the time/amplitude points define each input are included, along with plots of the input time histories.

The fully actuated traffic control problem solved by global optimization and complementarity

NASA Astrophysics Data System (ADS)

Ribeiro, Isabel M.; de Lurdes de Oliveira Simões, Maria

2016-02-01

Global optimization and complementarity are used to determine the signal timing for fully actuated traffic control, regarding effective green and red times on each cycle. The average values of these parameters can be used to estimate the control delay of vehicles. In this article, a two-phase queuing system for a signalized intersection is outlined, based on the principle of minimization of the total waiting time for the vehicles. The underlying model results in a linear program with linear complementarity constraints, solved by a sequential complementarity algorithm. Departure rates of vehicles during green and yellow periods were treated as deterministic, while arrival rates of vehicles were assumed to follow a Poisson distribution. Several traffic scenarios were created and solved. The numerical results reveal that it is possible to use global optimization and complementarity over a reasonable number of cycles and determine with efficiency effective green and red times for a signalized intersection.

The association of optimism and perceived discrimination with health care utilization in adults with sickle cell disease.

PubMed

Stanton, Michael V; Jonassaint, Charles R; Bartholomew, Frederick B; Edwards, Christopher; Richman, Laura; DeCastro, Laura; Williams, Redford

2010-11-01

We evaluated the effect of perceived discrimination, optimism, and their interaction on health care utilization among African American adults with sickle cell disease (SCD). Measures of optimism and perceived discrimination were obtained in 49 African American SCD patients. Multiple regression analyses controlling for sex and age tested effects of optimism and perceived discrimination on the number of emergency department visits (ED) and number and duration of hospitalizations over the past year. A perceived discrimination-optimism interaction was associated with number of emergency departments visits (b = .29, p = .052), number of hospitalizations (b = .36, p = .019), and duration of hospitalizations (b = .30, p = .045) such that those with high perceived discrimination/high optimism had the greatest health care utilization. African American SCD patients with high perceived discrimination/high optimism had greater health care utilization than patients who reported either low perceived discrimination or low optimism. This study suggests that patient personality and coping styles should be considered when evaluating the effects of stress on SCD-related outcomes.

Simultaneous multislice refocusing via time optimal control.

PubMed

Rund, Armin; Aigner, Christoph Stefan; Kunisch, Karl; Stollberger, Rudolf

2018-02-09

Joint design of minimum duration RF pulses and slice-selective gradient shapes for MRI via time optimal control with strict physical constraints, and its application to simultaneous multislice imaging. The minimization of the pulse duration is cast as a time optimal control problem with inequality constraints describing the refocusing quality and physical constraints. It is solved with a bilevel method, where the pulse length is minimized in the upper level, and the constraints are satisfied in the lower level. To address the inherent nonconvexity of the optimization problem, the upper level is enhanced with new heuristics for finding a near global optimizer based on a second optimization problem. A large set of optimized examples shows an average temporal reduction of 87.1% for double diffusion and 74% for turbo spin echo pulses compared to power independent number of slices pulses. The optimized results are validated on a 3T scanner with phantom measurements. The presented design method computes minimum duration RF pulse and slice-selective gradient shapes subject to physical constraints. The shorter pulse duration can be used to decrease the effective echo time in existing echo-planar imaging or echo spacing in turbo spin echo sequences. © 2018 International Society for Magnetic Resonance in Medicine.

Experimental validation of structural optimization methods

NASA Technical Reports Server (NTRS)

Adelman, Howard M.

1992-01-01

The topic of validating structural optimization methods by use of experimental results is addressed. The need for validating the methods as a way of effecting a greater and an accelerated acceptance of formal optimization methods by practicing engineering designers is described. The range of validation strategies is defined which includes comparison of optimization results with more traditional design approaches, establishing the accuracy of analyses used, and finally experimental validation of the optimization results. Examples of the use of experimental results to validate optimization techniques are described. The examples include experimental validation of the following: optimum design of a trussed beam; combined control-structure design of a cable-supported beam simulating an actively controlled space structure; minimum weight design of a beam with frequency constraints; minimization of the vibration response of helicopter rotor blade; minimum weight design of a turbine blade disk; aeroelastic optimization of an aircraft vertical fin; airfoil shape optimization for drag minimization; optimization of the shape of a hole in a plate for stress minimization; optimization to minimize beam dynamic response; and structural optimization of a low vibration helicopter rotor.

Approximate optimal tracking control for near-surface AUVs with wave disturbances

NASA Astrophysics Data System (ADS)

Yang, Qing; Su, Hao; Tang, Gongyou

2016-10-01

This paper considers the optimal trajectory tracking control problem for near-surface autonomous underwater vehicles (AUVs) in the presence of wave disturbances. An approximate optimal tracking control (AOTC) approach is proposed. Firstly, a six-degrees-of-freedom (six-DOF) AUV model with its body-fixed coordinate system is decoupled and simplified and then a nonlinear control model of AUVs in the vertical plane is given. Also, an exosystem model of wave disturbances is constructed based on Hirom approximation formula. Secondly, the time-parameterized desired trajectory which is tracked by the AUV's system is represented by the exosystem. Then, the coupled two-point boundary value (TPBV) problem of optimal tracking control for AUVs is derived from the theory of quadratic optimal control. By using a recently developed successive approximation approach to construct sequences, the coupled TPBV problem is transformed into a problem of solving two decoupled linear differential sequences of state vectors and adjoint vectors. By iteratively solving the two equation sequences, the AOTC law is obtained, which consists of a nonlinear optimal feedback item, an expected output tracking item, a feedforward disturbances rejection item, and a nonlinear compensatory term. Furthermore, a wave disturbances observer model is designed in order to solve the physically realizable problem. Simulation is carried out by using the Remote Environmental Unit (REMUS) AUV model to demonstrate the effectiveness of the proposed algorithm.

Active vibration control for flexible rotor by optimal direct-output feedback control

NASA Technical Reports Server (NTRS)

Nonami, Kenzou; Dirusso, Eliseo; Fleming, David P.

1989-01-01

Experimental research tests were performed to actively control the rotor vibrations of a flexible rotor mounted on flexible bearing supports. The active control method used in the tests is called optimal direct-output feedback control. This method uses four electrodynamic actuators to apply control forces directly to the bearing housings in order to achieve effective vibration control of the rotor. The force actuators are controlled by an analog controller that accepts rotor displacement as input. The controller is programmed with experimentally determined feedback coefficients; the output is a control signal to the force actuators. The tests showed that this active control method reduced the rotor resonance peaks due to unbalance from approximately 250 micrometers down to approximately 25 micrometers (essentially runout level). The tests were conducted over a speed range from 0 to 10,000 rpm; the rotor system had nine critical speeds within this speed range. The method was effective in significantly reducing the rotor vibration for all of the vibration modes and critical speeds.

Active vibration control for flexible rotor by optimal direct-output feedback control

NASA Technical Reports Server (NTRS)

Nonami, K.; Dirusso, E.; Fleming, D. P.

1989-01-01

Experimental research tests were performed to actively control the rotor vibrations of a flexible rotor mounted on flexible bearing supports. The active control method used in the tests is called optimal direct-output feedback control. This method uses four electrodynamic actuators to apply control forces directly to the bearing housings in order to achieve effective vibration control of the rotor. The force actuators are controlled by an analog controller that accepts rotor displacement as input. The controller is programmed with experimentally determined feedback coefficients; the output is a control signal to the force actuators. The tests showed that this active control method reduced the rotor resonance peaks due to unbalance from approximately 250 microns down to approximately 25 microns (essentially runout level). The tests were conducted over a speed range from 0 to 10,000 rpm; the rotor system had nine critical speeds within this speed range. The method was effective in significantly reducing the rotor vibration for all of the vibration modes and critical speeds.

Optimal Synthesis of Compliant Mechanisms using Subdivision and Commercial FEA (DETC2004-57497)

NASA Technical Reports Server (NTRS)

Hull, Patrick V.; Canfield, Stephen

2004-01-01

The field of distributed-compliance mechanisms has seen significant work in developing suitable topology optimization tools for their design. These optimal design tools have grown out of the techniques of structural optimization. This paper will build on the previous work in topology optimization and compliant mechanism design by proposing an alternative design space parameterization through control points and adding another step to the process, that of subdivision. The control points allow a specific design to be represented as a solid model during the optimization process. The process of subdivision creates an additional number of control points that help smooth the surface (for example a C(sup 2) continuous surface depending on the method of subdivision chosen) creating a manufacturable design free of some traditional numerical instabilities. Note that these additional control points do not add to the number of design parameters. This alternative parameterization and description as a solid model effectively and completely separates the design variables from the analysis variables during the optimization procedure. The motivation behind this work is to create an automated design tool from task definition to functional prototype created on a CNC or rapid-prototype machine. This paper will describe the proposed compliant mechanism design process and will demonstrate the procedure on several examples common in the literature.

Transient control for cascaded EDFAs by using a multi-objective optimization approach

NASA Astrophysics Data System (ADS)

Freitas, Marcio; Givigi, Sidney N., Jr.; Klein, Jackson; Calmon, Luiz C.; de Almeida, Ailson R.

2004-11-01

Erbium-doped fiber amplifiers (EDFA) have been used for some years now in building effective optical systems for the most diverse applications. For some applications, it is necessary to introduce some feedback control laws in order to avoid the generation of transients that could create impairments in the system. In this paper, we use a multi-objective optimization approach based on genetic algorithms, to study the introduction of proportional-derivative (PD) controllers into systems of cascaded EDFAs. We compare the use of individual controllers for each amplifier to the use of controllers to sets of amplifiers.

Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay.

PubMed

Smith, Lauren H; Hargrove, Levi J; Lock, Blair A; Kuiken, Todd A

2011-04-01

Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers.

Evolutionary Design of Controlled Structures

NASA Technical Reports Server (NTRS)

Masters, Brett P.; Crawley, Edward F.

1997-01-01

Basic physical concepts of structural delay and transmissibility are provided for simple rod and beam structures. Investigations show the sensitivity of these concepts to differing controlled-structures variables, and to rational system modeling effects. An evolutionary controls/structures design method is developed. The basis of the method is an accurate model formulation for dynamic compensator optimization and Genetic Algorithm based updating of sensor/actuator placement and structural attributes. One and three dimensional examples from the literature are used to validate the method. Frequency domain interpretation of these controlled structure systems provide physical insight as to how the objective is optimized and consequently what is important in the objective. Several disturbance rejection type controls-structures systems are optimized for a stellar interferometer spacecraft application. The interferometric designs include closed loop tracking optics. Designs are generated for differing structural aspect ratios, differing disturbance attributes, and differing sensor selections. Physical limitations in achieving performance are given in terms of average system transfer function gains and system phase loss. A spacecraft-like optical interferometry system is investigated experimentally over several different optimized controlled structures configurations. Configurations represent common and not-so-common approaches to mitigating pathlength errors induced by disturbances of two different spectra. Results show that an optimized controlled structure for low frequency broadband disturbances achieves modest performance gains over a mass equivalent regular structure, while an optimized structure for high frequency narrow band disturbances is four times better in terms of root-mean-square pathlength. These results are predictable given the nature of the physical system and the optimization design variables. Fundamental limits on controlled performance are discussed based on the measured and fit average system transfer function gains and system phase loss.

Exploring the complexity of quantum control optimization trajectories.

PubMed

Nanduri, Arun; Shir, Ofer M; Donovan, Ashley; Ho, Tak-San; Rabitz, Herschel

2015-01-07

The control of quantum system dynamics is generally performed by seeking a suitable applied field. The physical objective as a functional of the field forms the quantum control landscape, whose topology, under certain conditions, has been shown to contain no critical point suboptimal traps, thereby enabling effective searches for fields that give the global maximum of the objective. This paper addresses the structure of the landscape as a complement to topological critical point features. Recent work showed that landscape structure is highly favorable for optimization of state-to-state transition probabilities, in that gradient-based control trajectories to the global maximum value are nearly straight paths. The landscape structure is codified in the metric R ≥ 1.0, defined as the ratio of the length of the control trajectory to the Euclidean distance between the initial and optimal controls. A value of R = 1 would indicate an exactly straight trajectory to the optimal observable value. This paper extends the state-to-state transition probability results to the quantum ensemble and unitary transformation control landscapes. Again, nearly straight trajectories predominate, and we demonstrate that R can take values approaching 1.0 with high precision. However, the interplay of optimization trajectories with critical saddle submanifolds is found to influence landscape structure. A fundamental relationship necessary for perfectly straight gradient-based control trajectories is derived, wherein the gradient on the quantum control landscape must be an eigenfunction of the Hessian. This relation is an indicator of landscape structure and may provide a means to identify physical conditions when control trajectories can achieve perfect linearity. The collective favorable landscape topology and structure provide a foundation to understand why optimal quantum control can be readily achieved.

Data-based adjoint and H2 optimal control of the Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Banks, Michael; Bodony, Daniel

2017-11-01

Equation-free, reduced-order methods of control are desirable when the governing system of interest is of very high dimension or the control is to be applied to a physical experiment. Two-phase flow optimal control problems, our target application, fit these criteria. Dynamic Mode Decomposition (DMD) is a data-driven method for model reduction that can be used to resolve the dynamics of very high dimensional systems and project the dynamics onto a smaller, more manageable basis. We evaluate the effectiveness of DMD-based forward and adjoint operator estimation when applied to H2 optimal control approaches applied to the linear and nonlinear Ginzburg-Landau equation. Perspectives on applying the data-driven adjoint to two phase flow control will be given. Office of Naval Research (ONR) as part of the Multidisciplinary University Research Initiatives (MURI) Program, under Grant Number N00014-16-1-2617.

Optimizing the construction of devices to control inaccesible surfaces - case study

NASA Astrophysics Data System (ADS)

Niţu, E. L.; Costea, A.; Iordache, M. D.; Rizea, A. D.; Babă, Al

2017-10-01

The modern concept for the evolution of manufacturing systems requires multi-criteria optimization of technological processes and equipments, prioritizing associated criteria according to their importance. Technological preparation of the manufacturing can be developed, depending on the volume of production, to the limit of favourable economical effects related to the recovery of the costs for the design and execution of the technological equipment. Devices, as subsystems of the technological system, in the general context of modernization and diversification of machines, tools, semi-finished products and drives, are made in a multitude of constructive variants, which in many cases do not allow their identification, study and improvement. This paper presents a case study in which the multi-criteria analysis of some structures, based on a general optimization method, of novelty character, is used in order to determine the optimal construction variant of a control device. The rational construction of the control device confirms that the optimization method and the proposed calculation methods are correct and determine a different system configuration, new features and functions, and a specific method of working to control inaccessible surfaces.

Metabolic regulation and maximal reaction optimization in the central metabolism of a yeast cell

NASA Astrophysics Data System (ADS)

Kasbawati, Gunawan, A. Y.; Hertadi, R.; Sidarto, K. A.

2015-03-01

Regulation of fluxes in a metabolic system aims to enhance the production rates of biotechnologically important compounds. Regulation is held via modification the cellular activities of a metabolic system. In this study, we present a metabolic analysis of ethanol fermentation process of a yeast cell in terms of continuous culture scheme. The metabolic regulation is based on the kinetic formulation in combination with metabolic control analysis to indicate the key enzymes which can be modified to enhance ethanol production. The model is used to calculate the intracellular fluxes in the central metabolism of the yeast cell. Optimal control is then applied to the kinetic model to find the optimal regulation for the fermentation system. The sensitivity results show that there are external and internal control parameters which are adjusted in enhancing ethanol production. As an external control parameter, glucose supply should be chosen in appropriate way such that the optimal ethanol production can be achieved. For the internal control parameter, we find three enzymes as regulation targets namely acetaldehyde dehydrogenase, pyruvate decarboxylase, and alcohol dehydrogenase which reside in the acetaldehyde branch. Among the three enzymes, however, only acetaldehyde dehydrogenase has a significant effect to obtain optimal ethanol production efficiently.

CO 2 water-alternating-gas injection for enhanced oil recovery: Optimal well controls and half-cycle lengths

DOE PAGES

Chen, Bailian; Reynolds, Albert C.

2018-03-11

We report that CO 2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO 2 injection with the injected water to control the mobility of CO 2 and to stabilize the gas front. Optimization of CO 2 -WAG injection is widely regarded as a viable technique for controlling the CO 2 and oil miscible process. Poor recovery from CO 2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However,more » the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. Finally, the proposed framework is demonstrated with three reservoir examples.« less

Optimal control in adaptive optics modeling of nonlinear systems

NASA Astrophysics Data System (ADS)

Herrmann, J.

The problem of using an adaptive optics system to correct for nonlinear effects like thermal blooming is addressed using a model containing nonlinear lenses through which Gaussian beams are propagated. The best correction of this nonlinear system can be formulated as a deterministic open loop optimal control problem. This treatment gives a limit for the best possible correction. Aspects of adaptive control and servo systems are not included at this stage. An attempt is made to determine that control in the transmitter plane which minimizes the time averaged area or maximizes the fluence in the target plane. The standard minimization procedure leads to a two-point-boundary-value problem, which is ill-conditioned in the case. The optimal control problem was solved using an iterative gradient technique. An instantaneous correction is introduced and compared with the optimal correction. The results of the calculations show that for short times or weak nonlinearities the instantaneous correction is close to the optimal correction, but that for long times and strong nonlinearities a large difference develops between the two types of correction. For these cases the steady state correction becomes better than the instantaneous correction and approaches the optimum correction.

Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems.

PubMed

Kiumarsi, Bahare; Lewis, Frank L

2015-01-01

This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton-Jacobi-Bellman (HJB) are derived. An actor-critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.

CO 2 water-alternating-gas injection for enhanced oil recovery: Optimal well controls and half-cycle lengths

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Bailian; Reynolds, Albert C.

We report that CO 2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO 2 injection with the injected water to control the mobility of CO 2 and to stabilize the gas front. Optimization of CO 2 -WAG injection is widely regarded as a viable technique for controlling the CO 2 and oil miscible process. Poor recovery from CO 2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However,more » the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. Finally, the proposed framework is demonstrated with three reservoir examples.« less

Dynamic analysis and optimal control for a model of hepatitis C with treatment

NASA Astrophysics Data System (ADS)

Zhang, Suxia; Xu, Xiaxia

2017-05-01

A model for hepatitis C is formulated to study the effects of treatment and public concern on HCV transmission dynamics. The stability of equilibria and persistence of the model are analyzed, and an optimal control measure is performed to prevent the spread of HCV with minimal infected individuals and cost. The dynamical analysis reveals that the disease-free equilibrium of the model is asymptotically stable if the basic reproductive number R0 is less than unity. On the other hand, if R0 > 1 , the disease is uniformly persistent. Numerical simulations are conducted to investigate the influence of different vital parameters on R0. For the corresponding optimality system, the optimal solution is discussed by Pontryagin Maximum Principle, and the comparisons of model-predicted consequences with control or not are presented.

Development and application of an optimization procedure for flutter suppression using the aerodynamic energy concept

NASA Technical Reports Server (NTRS)

Nissim, E.; Abel, I.

1978-01-01

An optimization procedure is developed based on the responses of a system to continuous gust inputs. The procedure uses control law transfer functions which have been partially determined by using the relaxed aerodynamic energy approach. The optimization procedure yields a flutter suppression system which minimizes control surface activity in a gust environment. The procedure is applied to wing flutter of a drone aircraft to demonstrate a 44 percent increase in the basic wing flutter dynamic pressure. It is shown that a trailing edge control system suppresses the flutter instability over a wide range of subsonic mach numbers and flight altitudes. Results of this study confirm the effectiveness of the relaxed energy approach.

Optimal control of LQG problem with an explicit trade-off between mean and variance

NASA Astrophysics Data System (ADS)

Qian, Fucai; Xie, Guo; Liu, Ding; Xie, Wenfang

2011-12-01

For discrete-time linear-quadratic Gaussian (LQG) control problems, a utility function on the expectation and the variance of the conventional performance index is considered. The utility function is viewed as an overall objective of the system and can perform the optimal trade-off between the mean and the variance of performance index. The nonlinear utility function is first converted into an auxiliary parameters optimisation problem about the expectation and the variance. Then an optimal closed-loop feedback controller for the nonseparable mean-variance minimisation problem is designed by nonlinear mathematical programming. Finally, simulation results are given to verify the algorithm's effectiveness obtained in this article.

Stochastic optimal control of ultradiffusion processes with application to dynamic portfolio management

NASA Astrophysics Data System (ADS)

Marcozzi, Michael D.

2008-12-01

We consider theoretical and approximation aspects of the stochastic optimal control of ultradiffusion processes in the context of a prototype model for the selling price of a European call option. Within a continuous-time framework, the dynamic management of a portfolio of assets is effected through continuous or point control, activation costs, and phase delay. The performance index is derived from the unique weak variational solution to the ultraparabolic Hamilton-Jacobi equation; the value function is the optimal realization of the performance index relative to all feasible portfolios. An approximation procedure based upon a temporal box scheme/finite element method is analyzed; numerical examples are presented in order to demonstrate the viability of the approach.

Application of three controls optimally in a vector-borne disease - a mathematical study

NASA Astrophysics Data System (ADS)

Kar, T. K.; Jana, Soovoojeet

2013-10-01

We have proposed and analyzed a vector-borne disease model with three types of controls for the eradication of the disease. Four different classes for the human population namely susceptible, infected, recovered and vaccinated and two different classes for the vector populations namely susceptible and infected are considered. In the first part of our analysis the disease dynamics are described for fixed controls and some inferences have been drawn regarding the spread of the disease. Next the optimal control problem is formulated and solved considering control parameters as time dependent. Different possible combination of controls are used and their effectiveness are compared by numerical simulation.

Optimization of two-photon wave function in parametric down conversion by adaptive optics control of the pump radiation.

PubMed

Minozzi, M; Bonora, S; Sergienko, A V; Vallone, G; Villoresi, P

2013-02-15

We present an efficient method for optimizing the spatial profile of entangled-photon wave function produced in a spontaneous parametric down conversion process. A deformable mirror that modifies a wavefront of a 404 nm CW diode laser pump interacting with a nonlinear β-barium borate type-I crystal effectively controls the profile of the joint biphoton function. The use of a feedback signal extracted from the biphoton coincidence rate is used to achieve the optimal wavefront shape. The optimization of the two-photon coupling into two, single spatial modes for correlated detection is used for a practical demonstration of this physical principle.

The need to control for regression to the mean in social psychology studies.

PubMed

Yu, Rongjun; Chen, Li

2014-01-01

It is common in repeated measurements for extreme values at the first measurement to approach the mean at the subsequent measurement, a phenomenon called regression to the mean (RTM). If RTM is not fully controlled, it will lead to erroneous conclusions. The wide use of repeated measurements in social psychology creates a risk that an RTM effect will influence results. However, insufficient attention is paid to RTM in most social psychological research. Notable cases include studies on the phenomena of social conformity and unrealistic optimism (Klucharev et al., 2009, 2011; Sharot et al., 2011, 2012b; Campbell-Meiklejohn et al., 2012; Kim et al., 2012; Garrett and Sharot, 2014). In Study 1, 13 university students rated and re-rated the facial attractiveness of a series of female faces as a test of the social conformity effect (Klucharev et al., 2009). In Study 2, 15 university students estimated and re-estimated their risk of experiencing a series of adverse life events as a test of the unrealistic optimism effect (Sharot et al., 2011). Although these studies used methodologies similar to those used in earlier research, the social conformity and unrealistic optimism effects were no longer evident after controlling for RTM. Based on these findings we suggest several ways to control for the RTM effect in social psychology studies, such as adding the initial rating as a covariate in regression analysis, selecting a subset of stimuli for which the participant' initial ratings were matched across experimental conditions, and using a control group.

Final Report A Multi-Language Environment For Programmable Code Optimization and Empirical Tuning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yi, Qing; Whaley, Richard Clint; Qasem, Apan

This report summarizes our effort and results of building an integrated optimization environment to effectively combine the programmable control and the empirical tuning of source-to-source compiler optimizations within the framework of multiple existing languages, specifically C, C++, and Fortran. The environment contains two main components: the ROSE analysis engine, which is based on the ROSE C/C++/Fortran2003 source-to-source compiler developed by Co-PI Dr.Quinlan et. al at DOE/LLNL, and the POET transformation engine, which is based on an interpreted program transformation language developed by Dr. Yi at University of Texas at San Antonio (UTSA). The ROSE analysis engine performs advanced compiler analysis,more » identifies profitable code transformations, and then produces output in POET, a language designed to provide programmable control of compiler optimizations to application developers and to support the parameterization of architecture-sensitive optimizations so that their configurations can be empirically tuned later. This POET output can then be ported to different machines together with the user application, where a POET-based search engine empirically reconfigures the parameterized optimizations until satisfactory performance is found. Computational specialists can write POET scripts to directly control the optimization of their code. Application developers can interact with ROSE to obtain optimization feedback as well as provide domain-specific knowledge and high-level optimization strategies. The optimization environment is expected to support different levels of automation and programmer intervention, from fully-automated tuning to semi-automated development and to manual programmable control.« less

Application of Layered Perforation Profile Control Technique to Low Permeable Reservoir

NASA Astrophysics Data System (ADS)

Wei, Sun

2018-01-01

it is difficult to satisfy the demand of profile control of complex well section and multi-layer reservoir by adopting the conventional profile control technology, therefore, a research is conducted on adjusting the injection production profile with layered perforating parameters optimization. i.e. in the case of coproduction for multi-layer, water absorption of each layer is adjusted by adjusting the perforating parameters, thus to balance the injection production profile of the whole well section, and ultimately enhance the oil displacement efficiency of water flooding. By applying the relationship between oil-water phase percolation theory/perforating damage and capacity, a mathematic model of adjusting the injection production profile with layered perforating parameters optimization, besides, perforating parameters optimization software is programmed. Different types of optimization design work are carried out according to different geological conditions and construction purposes by using the perforating optimization design software; furthermore, an application test is done for low permeable reservoir, and the water injection profile tends to be balanced significantly after perforation with optimized parameters, thereby getting a good application effect on site.

Optimism and well-being: a prospective multi-method and multi-dimensional examination of optimism as a resilience factor following the occurrence of stressful life events.

PubMed

Kleiman, Evan M; Chiara, Alexandra M; Liu, Richard T; Jager-Hyman, Shari G; Choi, Jimmy Y; Alloy, Lauren B

2017-02-01

Optimism has been conceptualised variously as positive expectations (PE) for the future , optimistic attributions , illusion of control , and self-enhancing biases. Relatively little research has examined these multiple dimensions of optimism in relation to psychological and physical health. The current study assessed the multi-dimensional nature of optimism within a prospective vulnerability-stress framework. Initial principal component analyses revealed the following dimensions: PEs, Inferential Style (IS), Sense of Invulnerability (SI), and Overconfidence (O). Prospective follow-up analyses demonstrated that PE was associated with fewer depressive episodes and moderated the effect of stressful life events on depressive symptoms. SI also moderated the effect of life stress on anxiety symptoms. Generally, our findings indicated that optimism is a multifaceted construct and not all forms of optimism have the same effects on well-being. Specifically, our findings indicted that PE may be the most relevant to depression, whereas SI may be the most relevant to anxiety.

A mixed methods approach to assess animal vaccination programmes: The case of rabies control in Bamako, Mali.

PubMed

Mosimann, Laura; Traoré, Abdallah; Mauti, Stephanie; Léchenne, Monique; Obrist, Brigit; Véron, René; Hattendorf, Jan; Zinsstag, Jakob

2017-01-01

In the framework of the research network on integrated control of zoonoses in Africa (ICONZ) a dog rabies mass vaccination campaign was carried out in two communes of Bamako (Mali) in September 2014. A mixed method approach, combining quantitative and qualitative tools, was developed to evaluate the effectiveness of the intervention towards optimization for future scale-up. Actions to control rabies occur on one level in households when individuals take the decision to vaccinate their dogs. However, control also depends on provision of vaccination services and community participation at the intermediate level of social resilience. Mixed methods seem necessary as the problem-driven transdisciplinary project includes epidemiological components in addition to social dynamics and cultural, political and institutional issues. Adapting earlier effectiveness models for health intervention to rabies control, we propose a mixed method assessment of individual effectiveness parameters like availability, affordability, accessibility, adequacy or acceptability. Triangulation of quantitative methods (household survey, empirical coverage estimation and spatial analysis) with qualitative findings (participant observation, focus group discussions) facilitate a better understanding of the weight of each effectiveness determinant, and the underlying reasons embedded in the local understandings, cultural practices, and social and political realities of the setting. Using this method, a final effectiveness of 33% for commune Five and 28% for commune Six was estimated, with vaccination coverage of 27% and 20%, respectively. Availability was identified as the most sensitive effectiveness parameter, attributed to lack of information about the campaign. We propose a mixed methods approach to optimize intervention design, using an "intervention effectiveness optimization cycle" with the aim of maximizing effectiveness. Empirical vaccination coverage estimation is compared to the effectiveness model with its determinants. In addition, qualitative data provide an explanatory framework for deeper insight, validation and interpretation of results which should improve the intervention design while involving all stakeholders and increasing community participation. This work contributes vital information for the optimization and scale-up of future vaccination campaigns in Bamako, Mali. The proposed mixed method, although incompletely applied in this case study, should be applicable to similar rabies interventions targeting elimination in other settings. Copyright © 2016 Elsevier B.V. All rights reserved.

Toward Optimal Transport Networks

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.

2008-01-01

Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.

Optimal exponential synchronization of general chaotic delayed neural networks: an LMI approach.

PubMed

Liu, Meiqin

2009-09-01

This paper investigates the optimal exponential synchronization problem of general chaotic neural networks with or without time delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) technique. This general model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, and recurrent multilayer perceptrons (RMLPs) with or without delays. Using the drive-response concept, time-delay feedback controllers are designed to synchronize two identical chaotic neural networks as quickly as possible. The control design equations are shown to be a generalized eigenvalue problem (GEVP) which can be easily solved by various convex optimization algorithms to determine the optimal control law and the optimal exponential synchronization rate. Detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

An Optimization Framework for Driver Feedback Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malikopoulos, Andreas; Aguilar, Juan P.

2013-01-01

Modern vehicles have sophisticated electronic control units that can control engine operation with discretion to balance fuel economy, emissions, and power. These control units are designed for specific driving conditions (e.g., different speed profiles for highway and city driving). However, individual driving styles are different and rarely match the specific driving conditions for which the units were designed. In the research reported here, we investigate driving-style factors that have a major impact on fuel economy and construct an optimization framework to optimize individual driving styles with respect to these driving factors. In this context, we construct a set of polynomialmore » metamodels to reflect the responses produced in fuel economy by changing the driving factors. Then, we compare the optimized driving styles to the original driving styles and evaluate the effectiveness of the optimization framework. Finally, we use this proposed framework to develop a real-time feedback system, including visual instructions, to enable drivers to alter their driving styles in response to actual driving conditions to improve fuel efficiency.« less

Pumping strategies for management of a shallow water table: The value of the simulation-optimization approach

USGS Publications Warehouse

Barlow, P.M.; Wagner, B.J.; Belitz, K.

1996-01-01

The simulation-optimization approach is used to identify ground-water pumping strategies for control of the shallow water table in the western San Joaquin Valley, California, where shallow ground water threatens continued agricultural productivity. The approach combines the use of ground-water flow simulation with optimization techniques to build on and refine pumping strategies identified in previous research that used flow simulation alone. Use of the combined simulation-optimization model resulted in a 20 percent reduction in the area subject to a shallow water table over that identified by use of the simulation model alone. The simulation-optimization model identifies increasingly more effective pumping strategies for control of the water table as the complexity of the problem increases; that is, as the number of subareas in which pumping is to be managed increases, the simulation-optimization model is better able to discriminate areally among subareas to determine optimal pumping locations. The simulation-optimization approach provides an improved understanding of controls on the ground-water flow system and management alternatives that can be implemented in the valley. In particular, results of the simulation-optimization model indicate that optimal pumping strategies are constrained by the existing distribution of wells between the semiconfined and confined zones of the aquifer, by the distribution of sediment types (and associated hydraulic conductivities) in the western valley, and by the historical distribution of pumping throughout the western valley.

Assessment of optimal strategies in a two-patch dengue transmission model with seasonality.

PubMed

Kim, Jung Eun; Lee, Hyojung; Lee, Chang Hyeong; Lee, Sunmi

2017-01-01

Emerging and re-emerging dengue fever has posed serious problems to public health officials in many tropical and subtropical countries. Continuous traveling in seasonally varying areas makes it more difficult to control the spread of dengue fever. In this work, we consider a two-patch dengue model that can capture the movement of host individuals between and within patches using a residence-time matrix. A previous two-patch dengue model without seasonality is extended by adding host demographics and seasonal forcing in the transmission rates. We investigate the effects of human movement and seasonality on the two-patch dengue transmission dynamics. Motivated by the recent Peruvian dengue data in jungle/rural areas and coast/urban areas, our model mimics the seasonal patterns of dengue outbreaks in two patches. The roles of seasonality and residence-time configurations are highlighted in terms of the seasonal reproduction number and cumulative incidence. Moreover, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in the presence of seasonality. Our findings demonstrate that optimal patch-specific control strategies are sensitive to seasonality and residence-time scenarios. Targeting only the jungle (or endemic) is as effective as controlling both patches under weak coupling or symmetric mobility. However, focusing on intervention for the city (or high density areas) turns out to be optimal when two patches are strongly coupled with asymmetric mobility.

Data Transfer Advisor with Transport Profiling Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, Nageswara S.; Liu, Qiang; Yun, Daqing

The network infrastructures have been rapidly upgraded in many high-performance networks (HPNs). However, such infrastructure investment has not led to corresponding performance improvement in big data transfer, especially at the application layer, largely due to the complexity of optimizing transport control on end hosts. We design and implement ProbData, a PRofiling Optimization Based DAta Transfer Advisor, to help users determine the most effective data transfer method with the most appropriate control parameter values to achieve the best data transfer performance. ProbData employs a profiling optimization based approach to exploit the optimal operational zone of various data transfer methods in supportmore » of big data transfer in extreme scale scientific applications. We present a theoretical framework of the optimized profiling approach employed in ProbData as wellas its detailed design and implementation. The advising procedure and performance benefits of ProbData are illustrated and evaluated by proof-of-concept experiments in real-life networks.« less

Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

2016-07-01

This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.

Nonlinear dynamic analysis and robust controller design for Francis hydraulic turbine regulating system with a straight-tube surge tank

NASA Astrophysics Data System (ADS)

Liang, Ji; Yuan, Xiaohui; Yuan, Yanbin; Chen, Zhihuan; Li, Yuanzheng

2017-02-01

The safety and stability of hydraulic turbine regulating system (HTRS) in hydropower plants become increasingly important since the rapid development and the broad application of hydro energy technology. In this paper, a novel mathematical model of Francis hydraulic turbine regulating system with a straight-tube surge tank based on a few state-space equations is introduced to study the dynamic behaviors of the HTRS system, where the existence of possible unstable oscillations of this model is studied extensively and presented in the forms of the bifurcation diagram, time waveform plot, phase trajectories, and power spectrum. To eliminate these undesirable behaviors, a specified fuzzy sliding mode controller is designed. In this hybrid controller, the sliding mode control law makes full use of the proposed model to guarantee the robust control in the presence of system uncertainties, while the fuzzy system is applied to approximate the proper gains of the switching control in sliding mode technique to reduce the chattering effect, and particle swarm optimization is developed to search the optimal gains of the controller. Numerical simulations are presented to verify the effectiveness of the designed controller, and the results show that the performances of the nonlinear HTRS system assisted with the proposed controller is much better than that with the commonly used optimal PID controller.

Dynamic optimization case studies in DYNOPT tool

NASA Astrophysics Data System (ADS)

Ozana, Stepan; Pies, Martin; Docekal, Tomas

2016-06-01

Dynamic programming is typically applied to optimization problems. As the analytical solutions are generally very difficult, chosen software tools are used widely. These software packages are often third-party products bound for standard simulation software tools on the market. As typical examples of such tools, TOMLAB and DYNOPT could be effectively applied for solution of problems of dynamic programming. DYNOPT will be presented in this paper due to its licensing policy (free product under GPL) and simplicity of use. DYNOPT is a set of MATLAB functions for determination of optimal control trajectory by given description of the process, the cost to be minimized, subject to equality and inequality constraints, using orthogonal collocation on finite elements method. The actual optimal control problem is solved by complete parameterization both the control and the state profile vector. It is assumed, that the optimized dynamic model may be described by a set of ordinary differential equations (ODEs) or differential-algebraic equations (DAEs). This collection of functions extends the capability of the MATLAB Optimization Tool-box. The paper will introduce use of DYNOPT in the field of dynamic optimization problems by means of case studies regarding chosen laboratory physical educational models.

New numerical methods for open-loop and feedback solutions to dynamic optimization problems

NASA Astrophysics Data System (ADS)

Ghosh, Pradipto

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.

Computationally efficient control allocation

NASA Technical Reports Server (NTRS)

Durham, Wayne (Inventor)

2001-01-01

A computationally efficient method for calculating near-optimal solutions to the three-objective, linear control allocation problem is disclosed. The control allocation problem is that of distributing the effort of redundant control effectors to achieve some desired set of objectives. The problem is deemed linear if control effectiveness is affine with respect to the individual control effectors. The optimal solution is that which exploits the collective maximum capability of the effectors within their individual physical limits. Computational efficiency is measured by the number of floating-point operations required for solution. The method presented returned optimal solutions in more than 90% of the cases examined; non-optimal solutions returned by the method were typically much less than 1% different from optimal and the errors tended to become smaller than 0.01% as the number of controls was increased. The magnitude of the errors returned by the present method was much smaller than those that resulted from either pseudo inverse or cascaded generalized inverse solutions. The computational complexity of the method presented varied linearly with increasing numbers of controls; the number of required floating point operations increased from 5.5 i, to seven times faster than did the minimum-norm solution (the pseudoinverse), and at about the same rate as did the cascaded generalized inverse solution. The computational requirements of the method presented were much better than that of previously described facet-searching methods which increase in proportion to the square of the number of controls.

Optimal control of complex atomic quantum systems

PubMed Central

van Frank, S.; Bonneau, M.; Schmiedmayer, J.; Hild, S.; Gross, C.; Cheneau, M.; Bloch, I.; Pichler, T.; Negretti, A.; Calarco, T.; Montangero, S.

2016-01-01

Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing complexity. However, this control is still sub-optimal. In many scenarios, achieving a fast transformation is crucial to fight against decoherence and imperfection effects. Optimal control theory is believed to be the ideal candidate to bridge the gap between early stage proof-of-principle demonstrations and experimental protocols suitable for practical applications. Indeed, it can engineer protocols at the quantum speed limit – the fastest achievable timescale of the transformation. Here, we demonstrate such potential by computing theoretically and verifying experimentally the optimal transformations in two very different interacting systems: the coherent manipulation of motional states of an atomic Bose-Einstein condensate and the crossing of a quantum phase transition in small systems of cold atoms in optical lattices. We also show that such processes are robust with respect to perturbations, including temperature and atom number fluctuations. PMID:27725688

Optimal control of complex atomic quantum systems.

PubMed

van Frank, S; Bonneau, M; Schmiedmayer, J; Hild, S; Gross, C; Cheneau, M; Bloch, I; Pichler, T; Negretti, A; Calarco, T; Montangero, S

2016-10-11

Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing complexity. However, this control is still sub-optimal. In many scenarios, achieving a fast transformation is crucial to fight against decoherence and imperfection effects. Optimal control theory is believed to be the ideal candidate to bridge the gap between early stage proof-of-principle demonstrations and experimental protocols suitable for practical applications. Indeed, it can engineer protocols at the quantum speed limit - the fastest achievable timescale of the transformation. Here, we demonstrate such potential by computing theoretically and verifying experimentally the optimal transformations in two very different interacting systems: the coherent manipulation of motional states of an atomic Bose-Einstein condensate and the crossing of a quantum phase transition in small systems of cold atoms in optical lattices. We also show that such processes are robust with respect to perturbations, including temperature and atom number fluctuations.

An Optimal Current Observer for Predictive Current Controlled Buck DC-DC Converters

PubMed Central

Min, Run; Chen, Chen; Zhang, Xiaodong; Zou, Xuecheng; Tong, Qiaoling; Zhang, Qiao

2014-01-01

In digital current mode controlled DC-DC converters, conventional current sensors might not provide isolation at a minimized price, power loss and size. Therefore, a current observer which can be realized based on the digital circuit itself, is a possible substitute. However, the observed current may diverge due to the parasitic resistors and the forward conduction voltage of the diode. Moreover, the divergence of the observed current will cause steady state errors in the output voltage. In this paper, an optimal current observer is proposed. It achieves the highest observation accuracy by compensating for all the known parasitic parameters. By employing the optimal current observer-based predictive current controller, a buck converter is implemented. The converter has a convergently and accurately observed inductor current, and shows preferable transient response than the conventional voltage mode controlled converter. Besides, costs, power loss and size are minimized since the strategy requires no additional hardware for current sensing. The effectiveness of the proposed optimal current observer is demonstrated experimentally. PMID:24854061

Randomized Controlled Double-Blind Trial of Optimal Dose Methylphenidate in Children and Adolescents with Severe Attention Deficit Hyperactivity Disorder and Intellectual Disability

ERIC Educational Resources Information Center

Simonoff, Emily; Taylor, Eric; Baird, Gillian; Bernard, Sarah; Chadwick, Oliver; Liang, Holan; Whitwell, Susannah; Riemer, Kirsten; Sharma, Kishan; Sharma, Santvana Pandey; Wood, Nicky; Kelly, Joanna; Golaszewski, Ania; Kennedy, Juliet; Rodney, Lydia; West, Nicole; Walwyn, Rebecca; Jichi, Fatima

2013-01-01

Background: Attention deficit hyperactivity disorder is increased in children with intellectual disability. Previous research has suggested stimulants are less effective than in typically developing children but no studies have titrated medication for individual optimal dosing or tested the effects for longer than 4 weeks. Method: One hundred and…

E-Learning Optimization: The Relative and Combined Effects of Mental Practice and Modeling on Enhanced Podcast-Based Learning--A Randomized Controlled Trial

ERIC Educational Resources Information Center

Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P.; LeBlanc, Vicki R.

2016-01-01

Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced…

The controlled growth method - A tool for structural optimization

NASA Technical Reports Server (NTRS)

Hajela, P.; Sobieszczanski-Sobieski, J.

1981-01-01

An adaptive design variable linking scheme in a NLP based optimization algorithm is proposed and evaluated for feasibility of application. The present scheme, based on an intuitive effectiveness measure for each variable, differs from existing methodology in that a single dominant variable controls the growth of all others in a prescribed optimization cycle. The proposed method is implemented for truss assemblies and a wing box structure for stress, displacement and frequency constraints. Substantial reduction in computational time, even more so for structures under multiple load conditions, coupled with a minimal accompanying loss in accuracy, vindicates the algorithm.

Effects of depression, diabetes distress, diabetes self-efficacy, and diabetes self-management on glycemic control among Chinese population with type 2 diabetes mellitus.

PubMed

Lin, Keke; Park, Chang; Li, Mingzi; Wang, Xiudong; Li, Xiushu; Li, Wei; Quinn, Laurie

2017-09-01

To examine the direct and indirect effects of depression, diabetes distress, diabetes self-efficacy and diabetes self-management on glycemic control among a group of T2DM patients in China. A convenience sample of 254 participants were selected from three outpatient departments in Beijing, China. They were surveyed using a self-administered questionnaire. Diabetes-related information was retrieved from their medical records. Descriptive statistics, independent student t tests, Chi-square tests, correlation analyses and Generalized Structural Equation Modeling were used. Only 91 (35.82%) participants achieved optimal glycemic control of HbA1c<7.0% (53mmol/mol). Only diabetes self-management had a direct effect on glycemic control (OR=0.95, P<0.001). Depression and diabetes distress had only indirect effects on glycemic control through both diabetes self-efficacy and diabetes self-management. Diabetes self-efficacy only had an indirect effect on glycemic control through diabetes self-management. Glycemic control among Chinese population with T2DM was suboptimal. Future interventions should focus on decreasing depressive symptoms and diabetes distress levels, and, therefore, improve diabetes self-efficacy and self-management practices and, ultimately, reach the optimal goal of glycemic control. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

PubMed Central

Long, Yi; Du, Zhi-jiang; Wang, Wei-dong; Dong, Wei

2016-01-01

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems. PMID:27069353

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton.

PubMed

Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Dong, Wei

2016-01-01

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Effects of optimism on recovery and mental health after a tornado outbreak.

PubMed

Carbone, Eric G; Echols, Erin Thomas

2017-05-01

Dispositional optimism, a stable expectation that good things will happen, has been shown to improve health outcomes in a wide range of contexts, but very little research has explored the impact of optimism on post-disaster health and well-being. Data for this study come from the Centers for Disease Control and Prevention's Public health systems and mental health community recovery (PHSMHCR) Survey. Participants included 3216 individuals living in counties affected by the April 2011 tornado outbreak in Mississippi and Alabama. This study assesses the effect of dispositional optimism on post-disaster recovery and mental health. Dispositional optimism was found to have a positive effect on personal recovery and mental health after the disaster. Furthermore, it moderated the relationship between level of home damage and personal recovery as well as the relationship between home damage and post-traumatic stress disorder (PTSD), with stronger effects for those with increased levels of home damage. The utility of screening for optimism is discussed, along with the potential for interventions to increase optimism as a means of mitigating adverse mental health effects and improving the recovery of individuals affected by disasters and other traumatic events.

Effects of optimism on recovery and mental health after a tornado outbreak

PubMed Central

Carbone, Eric G.; Echols, Erin Thomas

2017-01-01

Objective Dispositional optimism, a stable expectation that good things will happen, has been shown to improve health outcomes in a wide range of contexts, but very little research has explored the impact of optimism on post-disaster health and well-being. Design Data for this study come from the Centers for Disease Control and Prevention’s Public health systems and mental health community recovery (PHSMHCR) Survey. Participants included 3216 individuals living in counties affected by the April 2011 tornado outbreak in Mississippi and Alabama. Main outcome measures This study assesses the effect of dispositional optimism on post-disaster recovery and mental health. Results Dispositional optimism was found to have a positive effect on personal recovery and mental health after the disaster. Furthermore, it moderated the relationship between level of home damage and personal recovery as well as the relationship between home damage and post-traumatic stress disorder (PTSD), with stronger effects for those with increased levels of home damage. Conclusions The utility of screening for optimism is discussed, along with the potential for interventions to increase optimism as a means of mitigating adverse mental health effects and improving the recovery of individuals affected by disasters and other traumatic events. PMID:28156138

Control theory based airfoil design for potential flow and a finite volume discretization

NASA Technical Reports Server (NTRS)

Reuther, J.; Jameson, A.

1994-01-01

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In previous studies it was shown that control theory could be used to devise an effective optimization procedure for two-dimensional profiles in which the shape is determined by a conformal transformation from a unit circle, and the control is the mapping function. The goal of our present work is to develop a method which does not depend on conformal mapping, so that it can be extended to treat three-dimensional problems. Therefore, we have developed a method which can address arbitrary geometric shapes through the use of a finite volume method to discretize the potential flow equation. Here the control law serves to provide computationally inexpensive gradient information to a standard numerical optimization method. Results are presented, where both target speed distributions and minimum drag are used as objective functions.

A neural network based implementation of an MPC algorithm applied in the control systems of electromechanical plants

NASA Astrophysics Data System (ADS)

Marusak, Piotr M.; Kuntanapreeda, Suwat

2018-01-01

The paper considers application of a neural network based implementation of a model predictive control (MPC) control algorithm to electromechanical plants. Properties of such control plants implicate that a relatively short sampling time should be used. However, in such a case, finding the control value numerically may be too time-consuming. Therefore, the current paper tests the solution based on transforming the MPC optimization problem into a set of differential equations whose solution is the same as that of the original optimization problem. This set of differential equations can be interpreted as a dynamic neural network. In such an approach, the constraints can be introduced into the optimization problem with relative ease. Moreover, the solution of the optimization problem can be obtained faster than when the standard numerical quadratic programming routine is used. However, a very careful tuning of the algorithm is needed to achieve this. A DC motor and an electrohydraulic actuator are taken as illustrative examples. The feasibility and effectiveness of the proposed approach are demonstrated through numerical simulations.

Towards sub-optimal stochastic control of partially observable stochastic systems

NASA Technical Reports Server (NTRS)

Ruzicka, G. J.

1980-01-01

A class of multidimensional stochastic control problems with noisy data and bounded controls encountered in aerospace design is examined. The emphasis is on suboptimal design, the optimality being taken in quadratic mean sense. To that effect the problem is viewed as a stochastic version of the Lurie problem known from nonlinear control theory. The main result is a separation theorem (involving a nonlinear Kalman-like filter) suitable for Lurie-type approximations. The theorem allows for discontinuous characteristics. As a byproduct the existence of strong solutions to a class of non-Lipschitzian stochastic differential equations in dimensions is proven.

Controlled supercontinua via spatial beam shaping

NASA Astrophysics Data System (ADS)

Zhdanova, Alexandra A.; Shen, Yujie; Thompson, Jonathan V.; Scully, Marlan O.; Yakovlev, Vladislav V.; Sokolov, Alexei V.

2018-06-01

Recently, optimization techniques have had a significant impact in a variety of fields, leading to a higher signal-to-noise and more streamlined techniques. We consider the possibility for using programmable phase-only spatial optimization of the pump beam to influence the supercontinuum generation process. Preliminary results show that significant broadening and rough control of the supercontinuum spectrum in the visible region are possible without loss of input energy. This serves as a proof-of-concept demonstration that spatial effects can controllably influence the supercontinuum spectrum, leading to possibilities for utilizing supercontinuum power more efficiently and achieving excellent spectral control.

Optimization of an Active Twist Rotor Blade Planform for Improved Active Response and Forward Flight Performance

NASA Technical Reports Server (NTRS)

Sekula, Martin K; Wilbur, Matthew L.

2014-01-01

A study was conducted to identify the optimum blade tip planform for a model-scale active twist rotor. The analysis identified blade tip design traits which simultaneously reduce rotor power of an unactuated rotor while leveraging aeromechanical couplings to tailor the active response of the blade. Optimizing the blade tip planform for minimum rotor power in forward flight provided a 5 percent improvement in performance compared to a rectangular blade tip, but reduced the vibration control authority of active twist actuation by 75 percent. Optimizing for maximum blade twist response increased the vibration control authority by 50 percent compared to the rectangular blade tip, with little effect on performance. Combined response and power optimization resulted in a blade tip design which provided similar vibration control authority to the rectangular blade tip, but with a 3.4 percent improvement in rotor performance in forward flight.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Optimal control of a rabies epidemic model with a birth pulse.

PubMed

Clayton, Tim; Duke-Sylvester, Scott; Gross, Louis J; Lenhart, Suzanne; Real, Leslie A

2010-01-01

A system of ordinary differential equations describes the population dynamics of a rabies epidemic in raccoons. The model accounts for the dynamics of a vaccine, including loss of vaccine due to animal consumption and loss from factors other than racoon uptake. A control method to reduce the spread of disease is introduced through temporal distribution of vaccine packets. This work incorporates the effect of the seasonal birth pulse in the racoon population and the attendant increase in new-borns which are susceptible to the diseases, analysing the impact of the timing and length of this pulse on the optimal distribution of vaccine packets. The optimization criterion is to minimize the number of infected raccoons while minimizing the cost of distributing the vaccine. Using an optimal control setting, numerical results illustrate strategies for distributing the vaccine depending on the timing of the infection outbreak with respect to the birth pulse.

Optimal Control of a Rabies Epidemic Model with a Birth Pulse

PubMed Central

Clayton, Tim; Duke-Sylvester, Scott; Gross, Louis J.; Lenhart, Suzanne; Real, Leslie A.

2011-01-01

A system of ordinary differential equations describes the populuation dynamics of a rabies epidemic in raccoons. The model accounts for the dynamics of vaccine, including loss of vaccine due to animal consumption and loss from factors other than racoon uptake. A control method to reduce the spread of disease is introduced through temporal distribution of vaccine packets. This work incorporates the effect of the seasonal birth pulse in the racoon population and the attendant increase in new-borns which are susceptible to the diseases, analysing the impact of the timing and length of this pulse on the optimal distribution of vaccine packets. The optimization criterion is to minimize the number of infected raccoons while minimizing the cost of distributing the vaccine. Using an optimal control setting, numerical results illustrate strategies for distributing vaccine depending on the timing of the infection outbreak with respect to the birth pulse. PMID:21423822

On stochastic control and optimal measurement strategies. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Kramer, L. C.

1971-01-01

The control of stochastic dynamic systems is studied with particular emphasis on those which influence the quality or nature of the measurements which are made to effect control. Four main areas are discussed: (1) the meaning of stochastic optimality and the means by which dynamic programming may be applied to solve a combined control/measurement problem; (2) a technique by which it is possible to apply deterministic methods, specifically the minimum principle, to the study of stochastic problems; (3) the methods described are applied to linear systems with Gaussian disturbances to study the structure of the resulting control system; and (4) several applications are considered.

A Higher Harmonic Optimal Controller to Optimise Rotorcraft Aeromechanical Behaviour

NASA Technical Reports Server (NTRS)

Leyland, Jane Anne

1996-01-01

Three methods to optimize rotorcraft aeromechanical behavior for those cases where the rotorcraft plant can be adequately represented by a linear model system matrix were identified and implemented in a stand-alone code. These methods determine the optimal control vector which minimizes the vibration metric subject to constraints at discrete time points, and differ from the commonly used non-optimal constraint penalty methods such as those employed by conventional controllers in that the constraints are handled as actual constraints to an optimization problem rather than as just additional terms in the performance index. The first method is to use a Non-linear Programming algorithm to solve the problem directly. The second method is to solve the full set of non-linear equations which define the necessary conditions for optimality. The third method is to solve each of the possible reduced sets of equations defining the necessary conditions for optimality when the constraints are pre-selected to be either active or inactive, and then to simply select the best solution. The effects of maneuvers and aeroelasticity on the systems matrix are modelled by using a pseudo-random pseudo-row-dependency scheme to define the systems matrix. Cases run to date indicate that the first method of solution is reliable, robust, and easiest to use, and that it was superior to the conventional controllers which were considered.

Beverton-Holt discrete pest management models with pulsed chemical control and evolution of pesticide resistance

NASA Astrophysics Data System (ADS)

Liang, Juhua; Tang, Sanyi; Cheke, Robert A.

2016-07-01

Pest resistance to pesticides is usually managed by switching between different types of pesticides. The optimal switching time, which depends on the dynamics of the pest population and on the evolution of the pesticide resistance, is critical. Here we address how the dynamic complexity of the pest population, the development of resistance and the spraying frequency of pulsed chemical control affect optimal switching strategies given different control aims. To do this, we developed novel discrete pest population growth models with both impulsive chemical control and the evolution of pesticide resistance. Strong and weak threshold conditions which guarantee the extinction of the pest population, based on the threshold values of the analytical formula for the optimal switching time, were derived. Further, we addressed switching strategies in the light of chosen economic injury levels. Moreover, the effects of the complex dynamical behaviour of the pest population on the pesticide switching times were also studied. The pesticide application period, the evolution of pesticide resistance and the dynamic complexity of the pest population may result in complex outbreak patterns, with consequent effects on the pesticide switching strategies.

UAV path planning using artificial potential field method updated by optimal control theory

NASA Astrophysics Data System (ADS)

Chen, Yong-bo; Luo, Guan-chen; Mei, Yue-song; Yu, Jian-qiao; Su, Xiao-long

2016-04-01

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.

Evaluation of Building Energy Saving Through the Development of Venetian Blinds' Optimal Control Algorithm According to the Orientation and Window-to-Wall Ratio

NASA Astrophysics Data System (ADS)

Kwon, Hyuk Ju; Yeon, Sang Hun; Lee, Keum Ho; Lee, Kwang Ho

2018-02-01

As various studies focusing on building energy saving have been continuously conducted, studies utilizing renewable energy sources, instead of fossil fuel, are needed. In particular, studies regarding solar energy are being carried out in the field of building science; in order to utilize such solar energy effectively, solar radiation being brought into the indoors should be acquired and blocked properly. Blinds are a typical solar radiation control device that is capable of controlling indoor thermal and light environments. However, slat-type blinds are manually controlled, giving a negative effect on building energy saving. In this regard, studies regarding the automatic control of slat-type blinds have been carried out for the last couple of decades. Therefore, this study aims to provide preliminary data for optimal control research through the controlling of slat angle in slat-type blinds by comprehensively considering various input variables. The window area ratio and orientation were selected as input variables. It was found that an optimal control algorithm was different among each window-to-wall ratio and window orientation. In addition, through comparing and analyzing the building energy saving performance for each condition by applying the developed algorithms to simulations, up to 20.7 % energy saving was shown in the cooling period and up to 12.3 % energy saving was shown in the heating period. In addition, building energy saving effect was greater as the window area ratio increased given the same orientation, and the effects of window-to-wall ratio in the cooling period were higher than those of window-to-wall ratio in the heating period.

Optimal modified tracking performance for MIMO networked control systems with communication constraints.

PubMed

Wu, Jie; Zhou, Zhu-Jun; Zhan, Xi-Sheng; Yan, Huai-Cheng; Ge, Ming-Feng

2017-05-01

This paper investigates the optimal modified tracking performance of multi-input multi-output (MIMO) networked control systems (NCSs) with packet dropouts and bandwidth constraints. Some explicit expressions are obtained by using co-prime factorization and the spectral decomposition technique. The obtained results show that the optimal modified tracking performance is related to the intrinsic properties of a given plant such as non-minimum phase (NMP) zeros, unstable poles, and their directions. Furthermore, the modified factor, packet dropouts probability and bandwidth also impact the optimal modified tracking performance of the NCSs. The optimal modified tracking performance with channel input power constraint is obtained by searching through all stabilizing two-parameter compensator. Finally, some typical examples are given to illustrate the effectiveness of the theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Two-strain Tuberculosis Transmission Model under Three Control Strategies

NASA Astrophysics Data System (ADS)

Rayhan, S. N.; Bakhtiar, T.; Jaharuddin

2017-03-01

In 1997, Castillo-Chavez and Feng developed a two-strain tuberculosis (TB) model, which is typical TB and resistant TB. Castillo-Chavez and Feng’s model was then subsequently developed by Jung et al. (2002) by adding two control variables. In this work, Jung et al.’s model was modified by introducing a new control variable so that there are three controls, namely chemoprophylaxis and two treatment strategies, with the application of three different scenarios related to the objective functional form and control application. Pontryagin maximum principle was applied to derive the differential equations system as a condition that must be satisfied by the optimal control variables. Furthermore, the fourth-order Runge-Kutta method was exploited to determine the numerical solution of the optimal control problem. In this numerical solution, it is shown that the controls treated on TB transmission model provide a good effect because latent and infected individuals are decreasing, and the number of individuals that is treated effectively is increasing.

Optimal and robust control of transition

NASA Technical Reports Server (NTRS)

Bewley, T. R.; Agarwal, R.

1996-01-01

Optimal and robust control theories are used to determine feedback control rules that effectively stabilize a linearly unstable flow in a plane channel. Wall transpiration (unsteady blowing/suction) with zero net mass flux is used as the control. Control algorithms are considered that depend both on full flowfield information and on estimates of that flowfield based on wall skin-friction measurements only. The development of these control algorithms accounts for modeling errors and measurement noise in a rigorous fashion; these disturbances are considered in both a structured (Gaussian) and unstructured ('worst case') sense. The performance of these algorithms is analyzed in terms of the eigenmodes of the resulting controlled systems, and the sensitivity of individual eigenmodes to both control and observation is quantified.

Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

NASA Astrophysics Data System (ADS)

Kmet', Tibor; Kmet'ová, Mária

2009-09-01

A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

Calcium channel blockers in hypertension. Is there still a controversy?

PubMed

Izzo, Joseph L

2005-08-01

There are several reasons why no single antihypertensive drug class is ideal in all clinical situations. First, pathophysiologic heterogeneity in hypertension and diversity of mechanisms of antihypertensive drugs dictate that no single drug class can be optimally effective in all subpopulations. Second, sustained blood pressure control generally requires combination therapy to block the reflex stimulation of physiologic mechanisms that attempt to restore blood pressure to pretreatment levels. Third, while effective blood pressure control is more important than choice of initial drug in the prevention of hypertension-related morbidity and mortality, specific drug classes are indicated for optimal treatment of complications of hypertension (e.g. heart failure, kidney disease). Fourth, although antihypertensive drug side effects are uncommon, alternative strategies are required in some patients. Given these principles, past controversies regarding whether calcium channel blockers (CCBs) should be used in the treatment of hypertension become moot. CCBs are extremely effective in lowering blood pressure and in preventing stroke and cardiovascular disease. When additional blood pressure lowering is necessary to meet strict targets, CCBs may be added, even in heart failure or chronic kidney disease, where CCBs alone may not achieve optimal outcomes. Combinations of CCBs with "anti-neurohumoral" drugs such as ACE inhibitors are particularly useful to achieve sustained blood pressure control, reduce adverse effects such as edema, and improve outcomes.

Reduced state feedback gain computation. [optimization and control theory for aircraft control

NASA Technical Reports Server (NTRS)

Kaufman, H.

1976-01-01

Because application of conventional optimal linear regulator theory to flight controller design requires the capability of measuring and/or estimating the entire state vector, it is of interest to consider procedures for computing controls which are restricted to be linear feedback functions of a lower dimensional output vector and which take into account the presence of measurement noise and process uncertainty. Therefore, a stochastic linear model that was developed is presented which accounts for aircraft parameter and initial uncertainty, measurement noise, turbulence, pilot command and a restricted number of measurable outputs. Optimization with respect to the corresponding output feedback gains was performed for both finite and infinite time performance indices without gradient computation by using Zangwill's modification of a procedure originally proposed by Powell. Results using a seventh order process show the proposed procedures to be very effective.

Effect of handling characteristics on minimum time cornering with torque vectoring

NASA Astrophysics Data System (ADS)

Smith, E. N.; Velenis, E.; Tavernini, D.; Cao, D.

2018-02-01

In this paper, the effect of both passive and actively-modified vehicle handling characteristics on minimum time manoeuvring for vehicles with 4-wheel torque vectoring (TV) capability is studied. First, a baseline optimal TV strategy is sought, independent of any causal control law. An optimal control problem (OCP) is initially formulated considering 4 independent wheel torque inputs, together with the steering angle rate, as the control variables. Using this formulation, the performance benefit using TV against an electric drive train with a fixed torque distribution, is demonstrated. The sensitivity of TV-controlled manoeuvre time to the passive understeer gradient of the vehicle is then studied. A second formulation of the OCP is introduced where a closed-loop TV controller is incorporated into the system dynamics of the OCP. This formulation allows the effect of actively modifying a vehicle's handling characteristic via TV on its minimum time cornering performance of the vehicle to be assessed. In particular, the effect of the target understeer gradient as the key tuning parameter of the literature-standard steady-state linear single-track model yaw rate reference is analysed.

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Synthesis of aircraft structures using integrated design and analysis methods

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Goetz, R. C.

1978-01-01

A systematic research is reported to develop and validate methods for structural sizing of an airframe designed with the use of composite materials and active controls. This research program includes procedures for computing aeroelastic loads, static and dynamic aeroelasticity, analysis and synthesis of active controls, and optimization techniques. Development of the methods is concerned with the most effective ways of integrating and sequencing the procedures in order to generate structural sizing and the associated active control system, which is optimal with respect to a given merit function constrained by strength and aeroelasticity requirements.

Evolutional Optimization on Material Ordering and Inventory Control of Supply Chain through Incentive Scheme

NASA Astrophysics Data System (ADS)

Prasertwattana, Kanit; Shimizu, Yoshiaki; Chiadamrong, Navee

This paper studied the material ordering and inventory control of supply chain systems. The effect of controlling policies is analyzed under three different configurations of the supply chain systems, and the formulated problem has been solved by using an evolutional optimization method known as Differential Evolution (DE). The numerical results show that the coordinating policy with the incentive scheme outperforms the other policies and can improve the performance of the overall system as well as all members under the concept of supply chain management.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

NASA Astrophysics Data System (ADS)

Wang, Zhongfeng; Zhang, Xuyang; Hu, Bo; Liu, Jun; Li, Ligang; Gu, Yongqiang; Zhou, Bowen

2018-02-01

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

Comparative study of popular objective functions for damping power system oscillations in multimachine system.

PubMed

Islam, Naz Niamul; Hannan, M A; Shareef, Hussain; Mohamed, Azah; Salam, M A

2014-01-01

Power oscillation damping controller is designed in linearized model with heuristic optimization techniques. Selection of the objective function is very crucial for damping controller design by optimization algorithms. In this research, comparative analysis has been carried out to evaluate the effectiveness of popular objective functions used in power system oscillation damping. Two-stage lead-lag damping controller by means of power system stabilizers is optimized using differential search algorithm for different objective functions. Linearized model simulations are performed to compare the dominant mode's performance and then the nonlinear model is continued to evaluate the damping performance over power system oscillations. All the simulations are conducted in two-area four-machine power system to bring a detailed analysis. Investigated results proved that multiobjective D-shaped function is an effective objective function in terms of moving unstable and lightly damped electromechanical modes into stable region. Thus, D-shape function ultimately improves overall system damping and concurrently enhances power system reliability.

Large-scale expensive black-box function optimization

NASA Astrophysics Data System (ADS)

Rashid, Kashif; Bailey, William; Couët, Benoît

2012-09-01

This paper presents the application of an adaptive radial basis function method to a computationally expensive black-box reservoir simulation model of many variables. An iterative proxy-based scheme is used to tune the control variables, distributed for finer control over a varying number of intervals covering the total simulation period, to maximize asset NPV. The method shows that large-scale simulation-based function optimization of several hundred variables is practical and effective.

Optimal integration of gravity in trajectory planning of vertical pointing movements.

PubMed

Crevecoeur, Frédéric; Thonnard, Jean-Louis; Lefèvre, Philippe

2009-08-01

The planning and control of motor actions requires knowledge of the dynamics of the controlled limb to generate the appropriate muscular commands and achieve the desired goal. Such planning and control imply that the CNS must be able to deal with forces and constraints acting on the limb, such as the omnipresent force of gravity. The present study investigates the effect of hypergravity induced by parabolic flights on the trajectory of vertical pointing movements to test the hypothesis that motor commands are optimized with respect to the effect of gravity on the limb. Subjects performed vertical pointing movements in normal gravity and hypergravity. We use a model based on optimal control to identify the role played by gravity in the optimal arm trajectory with minimal motor costs. First, the simulations in normal gravity reproduce the asymmetry in the velocity profiles (the velocity reaches its maximum before half of the movement duration), which typically characterizes the vertical pointing movements performed on Earth, whereas the horizontal movements present symmetrical velocity profiles. Second, according to the simulations, the optimal trajectory in hypergravity should present an increase in the peak acceleration and peak velocity despite the increase in the arm weight. In agreement with these predictions, the subjects performed faster movements in hypergravity with significant increases in the peak acceleration and peak velocity, which were accompanied by a significant decrease in the movement duration. This suggests that movement kinematics change in response to an increase in gravity, which is consistent with the hypothesis that motor commands are optimized and the action of gravity on the limb is taken into account. The results provide evidence for an internal representation of gravity in the central planning process and further suggest that an adaptation to altered dynamics can be understood as a reoptimization process.

Optimal therapies of a virus replication model with pharmacological delays based on reverse transcriptase inhibitors and protease inhibitors

NASA Astrophysics Data System (ADS)

Pei, Yongzhen; Li, Changguo; Liang, Xiyin

2017-11-01

A short delay in the pharmacological effect on account of the time required for drug absorption, distribution, and penetration into target cells after application of any anti-viral drug, is defined by the pharmacological delay (Herz et al 1996 Proc. Natl Acad. Sci. USA 93 7247-51). In this paper, a virus replication model with Beddington-DeAngelis incidence rate and the pharmacological and intracellular delays is presented to describe the treatment to cure the virus infection. The optimal controls represent the efficiency of reverse transcriptase inhibitors and protease inhibitors in suppressing viral production and prohibiting new infections. Due to the fact that both the control and state variables contain delays, we derive a necessary conditions for our optimal problem. Based on these results, numerical simulations are implemented not only to show the optimal therapeutic schedules for different infection and release rates, but also to compare the effective of three treatment programs. Furthermore, comparison of therapeutic effects under different maximum tolerable dosages is shown. Our research indicates that (1) the proper and specific treatment program should be determined according to the infection rates of different virus particles; (2) the optimal combined drug treatment is the most efficient; (3) the appropriate proportion of medicament must be formulated during the therapy due to the non-monotonic relationship between maximum tolerable dosages and therapeutic effects; (4) the therapeutic effect is advantageous when the pharmacological delay is considered.

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

PubMed

Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

Model-Free Optimal Tracking Control via Critic-Only Q-Learning.

PubMed

Luo, Biao; Liu, Derong; Huang, Tingwen; Wang, Ding

2016-10-01

Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.

Driver electronics design and control for a total artificial heart linear motor.

PubMed

Unthan, Kristin; Cuenca-Navalon, Elena; Pelletier, Benedikt; Finocchiaro, Thomas; Steinseifer, Ulrich

2018-01-27

For any implantable device size and efficiency are critical properties. Thus, a linear motor for a Total Artificial Heart was optimized with focus on driver electronics and control strategies. Hardware requirements were defined from power supply and motor setup. Four full bridges were chosen for the power electronics. Shunt resistors were set up for current measurement. Unipolar and bipolar switching for power electronics control were compared regarding current ripple and power losses. Here, unipolar switching showed smaller current ripple and required less power to create the necessary motor forces. Based on calculations for minimal power losses Lorentz force was distributed to the actor's four coils. The distribution was determined as ratio of effective magnetic flux through each coil, which was captured by a force test rig. Static and dynamic measurements under physiological conditions analyzed interaction of control and hardware and all efficiencies were over 89%. In conclusion, the designed electronics, optimized control strategy and applied current distribution create the required motor force and perform optimal under physiological conditions. The developed driver electronics and control offer optimized size and efficiency for any implantable or portable device with multiple independent motor coils. Graphical Abstract ᅟ.

Mitigation of epidemics in contact networks through optimal contact adaptation *

PubMed Central

Youssef, Mina; Scoglio, Caterina

2013-01-01

This paper presents an optimal control problem formulation to minimize the total number of infection cases during the spread of susceptible-infected-recovered SIR epidemics in contact networks. In the new approach, contact weighted are reduced among nodes and a global minimum contact level is preserved in the network. In addition, the infection cost and the cost associated with the contact reduction are linearly combined in a single objective function. Hence, the optimal control formulation addresses the tradeoff between minimization of total infection cases and minimization of contact weights reduction. Using Pontryagin theorem, the obtained solution is a unique candidate representing the dynamical weighted contact network. To find the near-optimal solution in a decentralized way, we propose two heuristics based on Bang-Bang control function and on a piecewise nonlinear control function, respectively. We perform extensive simulations to evaluate the two heuristics on different networks. Our results show that the piecewise nonlinear control function outperforms the well-known Bang-Bang control function in minimizing both the total number of infection cases and the reduction of contact weights. Finally, our results show awareness of the infection level at which the mitigation strategies are effectively applied to the contact weights. PMID:23906209

Mitigation of epidemics in contact networks through optimal contact adaptation.

PubMed

Youssef, Mina; Scoglio, Caterina

2013-08-01

This paper presents an optimal control problem formulation to minimize the total number of infection cases during the spread of susceptible-infected-recovered SIR epidemics in contact networks. In the new approach, contact weighted are reduced among nodes and a global minimum contact level is preserved in the network. In addition, the infection cost and the cost associated with the contact reduction are linearly combined in a single objective function. Hence, the optimal control formulation addresses the tradeoff between minimization of total infection cases and minimization of contact weights reduction. Using Pontryagin theorem, the obtained solution is a unique candidate representing the dynamical weighted contact network. To find the near-optimal solution in a decentralized way, we propose two heuristics based on Bang-Bang control function and on a piecewise nonlinear control function, respectively. We perform extensive simulations to evaluate the two heuristics on different networks. Our results show that the piecewise nonlinear control function outperforms the well-known Bang-Bang control function in minimizing both the total number of infection cases and the reduction of contact weights. Finally, our results show awareness of the infection level at which the mitigation strategies are effectively applied to the contact weights.

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

A concept for adaptive performance optimization on commercial transport aircraft

NASA Technical Reports Server (NTRS)

Jackson, Michael R.; Enns, Dale F.

1995-01-01

An adaptive control method is presented for the minimization of drag during flight for transport aircraft. The minimization of drag is achieved by taking advantage of the redundant control capability available in the pitch axis, with the horizontal tail used as the primary surface and symmetric deflection of the ailerons and cruise flaps used as additional controls. The additional control surfaces are excited with sinusoidal signals, while the altitude and velocity loops are closed with guidance and control laws. A model of the throttle response as a function of the additional control surfaces is formulated and the parameters in the model are estimated from the sensor measurements using a least squares estimation method. The estimated model is used to determine the minimum drag positions of the control surfaces. The method is presented for the optimization of one and two additional control surfaces. The adaptive control method is extended to optimize rate of climb with the throttle fixed. Simulations that include realistic disturbances are presented, as well as the results of a Monte Carlo simulation analysis that shows the effects of changing the disturbance environment and the excitation signal parameters.

Making the Most of the "Daphnia" Heart Rate Lab: Optimizing the Use of Ethanol, Nicotine & Caffeine

ERIC Educational Resources Information Center

Corotto, Frank; Ceballos, Darrel; Lee, Adam; Vinson, Lindsey

2010-01-01

Students commonly test the effects of chemical agents on the heart rate of the crustacean "Daphnia" magna, but the procedure has never been optimized. We determined the effects of three concentrations of ethanol, nicotine, and caffeine and of a control solution on heart rate in "Daphnia." Ethanol at 5% and 10% (v/v) reduced mean heart rate to…

Optimized cross-resonance gate for coupled transmon systems

NASA Astrophysics Data System (ADS)

Kirchhoff, Susanna; Keßler, Torsten; Liebermann, Per J.; Assémat, Elie; Machnes, Shai; Motzoi, Felix; Wilhelm, Frank K.

2018-04-01

The cross-resonance (CR) gate is an entangling gate for fixed-frequency superconducting qubits. While being simple and extensible, it is comparatively slow, at 160 ns, and thus of limited fidelity due to on-going incoherent processes. Using two different optimal control algorithms, we estimate the quantum speed limit for a controlled-not cnot gate in this system to be 10 ns, indicating a potential for great improvements. We show that the ability to approach this limit depends strongly on the choice of ansatz used to describe optimized control pulses and limitations placed on their complexity. Using a piecewise-constant ansatz, with a single carrier and bandwidth constraints, we identify an experimentally feasible 70-ns pulse shape. Further, an ansatz based on the two dominant frequencies involved in the optimal control problem allows for an optimal solution more than twice as fast again, at under 30 ns, with smooth features and limited complexity. This is twice as fast as gate realizations using tunable-frequency, resonantly coupled qubits. Compared to current CR-gate implementations, we project our scheme will provide a sixfold speed-up and thus a sixfold reduction in fidelity loss due to incoherent effects.

The need to control for regression to the mean in social psychology studies

PubMed Central

Yu, Rongjun; Chen, Li

2014-01-01

It is common in repeated measurements for extreme values at the first measurement to approach the mean at the subsequent measurement, a phenomenon called regression to the mean (RTM). If RTM is not fully controlled, it will lead to erroneous conclusions. The wide use of repeated measurements in social psychology creates a risk that an RTM effect will influence results. However, insufficient attention is paid to RTM in most social psychological research. Notable cases include studies on the phenomena of social conformity and unrealistic optimism (Klucharev et al., 2009, 2011; Sharot et al., 2011, 2012b; Campbell-Meiklejohn et al., 2012; Kim et al., 2012; Garrett and Sharot, 2014). In Study 1, 13 university students rated and re-rated the facial attractiveness of a series of female faces as a test of the social conformity effect (Klucharev et al., 2009). In Study 2, 15 university students estimated and re-estimated their risk of experiencing a series of adverse life events as a test of the unrealistic optimism effect (Sharot et al., 2011). Although these studies used methodologies similar to those used in earlier research, the social conformity and unrealistic optimism effects were no longer evident after controlling for RTM. Based on these findings we suggest several ways to control for the RTM effect in social psychology studies, such as adding the initial rating as a covariate in regression analysis, selecting a subset of stimuli for which the participant' initial ratings were matched across experimental conditions, and using a control group. PMID:25620951

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

PubMed

Chang, Joshua; Paydarfar, David

2014-12-01

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

Optimized PID control of depth of hypnosis in anesthesia.

PubMed

Padula, Fabrizio; Ionescu, Clara; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio; Vivacqua, Giulio

2017-06-01

This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing. In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case. Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable. Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed. Copyright © 2017 Elsevier B.V. All rights reserved.

Synchronic interval Gaussian mixed-integer programming for air quality management.

PubMed

Cheng, Guanhui; Huang, Guohe Gordon; Dong, Cong

2015-12-15

To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can help decision makers mitigate potential risks, e.g. insufficiency of pollutant treatment capabilities, exceedance of air quality standards, deficiency of pollution control fund, or imbalance of economic or environmental stress, in the process of guiding AQM. Copyright © 2015 Elsevier B.V. All rights reserved.

Unification theory of optimal life histories and linear demographic models in internal stochasticity.

PubMed

Oizumi, Ryo

2014-01-01

Life history of organisms is exposed to uncertainty generated by internal and external stochasticities. Internal stochasticity is generated by the randomness in each individual life history, such as randomness in food intake, genetic character and size growth rate, whereas external stochasticity is due to the environment. For instance, it is known that the external stochasticity tends to affect population growth rate negatively. It has been shown in a recent theoretical study using path-integral formulation in structured linear demographic models that internal stochasticity can affect population growth rate positively or negatively. However, internal stochasticity has not been the main subject of researches. Taking account of effect of internal stochasticity on the population growth rate, the fittest organism has the optimal control of life history affected by the stochasticity in the habitat. The study of this control is known as the optimal life schedule problems. In order to analyze the optimal control under internal stochasticity, we need to make use of "Stochastic Control Theory" in the optimal life schedule problem. There is, however, no such kind of theory unifying optimal life history and internal stochasticity. This study focuses on an extension of optimal life schedule problems to unify control theory of internal stochasticity into linear demographic models. First, we show the relationship between the general age-states linear demographic models and the stochastic control theory via several mathematical formulations, such as path-integral, integral equation, and transition matrix. Secondly, we apply our theory to a two-resource utilization model for two different breeding systems: semelparity and iteroparity. Finally, we show that the diversity of resources is important for species in a case. Our study shows that this unification theory can address risk hedges of life history in general age-states linear demographic models.

Unification Theory of Optimal Life Histories and Linear Demographic Models in Internal Stochasticity

PubMed Central

Oizumi, Ryo

2014-01-01

Life history of organisms is exposed to uncertainty generated by internal and external stochasticities. Internal stochasticity is generated by the randomness in each individual life history, such as randomness in food intake, genetic character and size growth rate, whereas external stochasticity is due to the environment. For instance, it is known that the external stochasticity tends to affect population growth rate negatively. It has been shown in a recent theoretical study using path-integral formulation in structured linear demographic models that internal stochasticity can affect population growth rate positively or negatively. However, internal stochasticity has not been the main subject of researches. Taking account of effect of internal stochasticity on the population growth rate, the fittest organism has the optimal control of life history affected by the stochasticity in the habitat. The study of this control is known as the optimal life schedule problems. In order to analyze the optimal control under internal stochasticity, we need to make use of “Stochastic Control Theory” in the optimal life schedule problem. There is, however, no such kind of theory unifying optimal life history and internal stochasticity. This study focuses on an extension of optimal life schedule problems to unify control theory of internal stochasticity into linear demographic models. First, we show the relationship between the general age-states linear demographic models and the stochastic control theory via several mathematical formulations, such as path–integral, integral equation, and transition matrix. Secondly, we apply our theory to a two-resource utilization model for two different breeding systems: semelparity and iteroparity. Finally, we show that the diversity of resources is important for species in a case. Our study shows that this unification theory can address risk hedges of life history in general age-states linear demographic models. PMID:24945258

Dynamic optimal strategies in transboundary pollution game under learning by doing

NASA Astrophysics Data System (ADS)

Chang, Shuhua; Qin, Weihua; Wang, Xinyu

2018-01-01

In this paper, we present a transboundary pollution game, in which emission permits trading and pollution abatement costs under learning by doing are considered. In this model, the abatement cost mainly depends on the level of pollution abatement and the experience of using pollution abatement technology. We use optimal control theory to investigate the optimal emission paths and the optimal pollution abatement strategies under cooperative and noncooperative games, respectively. Additionally, the effects of parameters on the results have been examined.

Vestibular models for design and evaluation of flight simulator motion

NASA Technical Reports Server (NTRS)

Bussolari, S. R.; Sullivan, R. B.; Young, L. R.

1986-01-01

The use of spatial orientation models in the design and evaluation of control systems for motion-base flight simulators is investigated experimentally. The development of a high-fidelity motion drive controller using an optimal control approach based on human vestibular models is described. The formulation and implementation of the optimal washout system are discussed. The effectiveness of the motion washout system was evaluated by studying the response of six motion washout systems to the NASA/AMES Vertical Motion Simulator for a single dash-quick-stop maneuver. The effects of the motion washout system on pilot performance and simulator acceptability are examined. The data reveal that human spatial orientation models are useful for the design and evaluation of flight simulator motion fidelity.

Employing static excitation control and tie line reactance to stabilize wind turbine generators

NASA Technical Reports Server (NTRS)

Hwang, H. H.; Mozeico, H. V.; Guo, T.

1978-01-01

An analytical representation of a wind turbine generator is presented which employs blade pitch angle feedback control. A mathematical model was formulated. With the functioning MOD-0 wind turbine serving as a practical case study, results of computer simulations of the model as applied to the problem of dynamic stability at rated load are also presented. The effect of the tower shadow was included in the input to the system. Different configurations of the drive train, and optimal values of the tie line reactance were used in the simulations. Computer results revealed that a static excitation control system coupled with optimal values of the tie line reactance would effectively reduce oscillations of the power output, without the use of a slip clutch.

Modeling and Control of a Delayed Hepatitis B Virus Model with Incubation Period and Combination Treatment.

PubMed

Sun, Deshun; Liu, Fei

2018-06-01

In this paper, a hepatitis B virus (HBV) model with an incubation period and delayed state and control variables is firstly proposed. Furthermore, the combination treatment is adopted to have a longer-lasting effect than mono-therapy. The equilibrium points and basic reproduction number are calculated, and then the local stability is analyzed on this model. We then present optimal control strategies based on the Pontryagin's minimum principle with an objective function not only to reduce the levels of exposed cells, infected cells and free viruses nearly to zero at the end of therapy, but also to minimize the drug side-effect and the cost of treatment. What's more, we develop a numerical simulation algorithm for solving our HBV model based on the combination of forward and backward difference approximations. The state dynamics of uninfected cells, exposed cells, infected cells, free viruses, CTL and ALT are simulated with or without optimal control, which show that HBV is reduced nearly to zero based on the time-varying optimal control strategies whereas the disease would break out without control. At last, by the simulations, we prove that strategy A is the best among the three kinds of strategies we adopt and further comparisons have been done between model (1) and model (2).

Optimizing insecticide allocation strategies based on houses and livestock shelters for visceral leishmaniasis control in Bihar, India.

PubMed

Gorahava, Kaushik K; Rosenberger, Jay M; Mubayi, Anuj

2015-07-01

Visceral leishmaniasis (VL) is the most deadly form of the leishmaniasis family of diseases, which affects numerous developing countries. The Indian state of Bihar has the highest prevalence and mortality rate of VL in the world. Insecticide spraying is believed to be an effective vector control program for controlling the spread of VL in Bihar; however, it is expensive and less effective if not implemented systematically. This study develops and analyzes a novel optimization model for VL control in Bihar that identifies an optimal (best possible) allocation of chosen insecticide (dichlorodiphenyltrichloroethane [DDT] or deltamethrin) based on the sizes of human and cattle populations in the region. The model maximizes the insecticide-induced sandfly death rate in human and cattle dwellings while staying within the current state budget for VL vector control efforts. The model results suggest that deltamethrin might not be a good replacement for DDT because the insecticide-induced sandfly deaths are 3.72 times more in case of DDT even after 90 days post spray. Different insecticide allocation strategies between the two types of sites (houses and cattle sheds) are suggested based on the state VL-control budget and have a direct implication on VL elimination efforts in a resource-limited region. © The American Society of Tropical Medicine and Hygiene.

A self-contained, automated methodology for optimal flow control validated for transition delay

NASA Technical Reports Server (NTRS)

Joslin, Ronald D.; Gunzburger, Max D.; Nicolaides, R. A.; Erlebacher, Gordon; Hussaini, M. Yousuff

1995-01-01

This paper describes a self-contained, automated methodology for flow control along with a validation of the methodology for the problem of boundary layer instability suppression. The objective of control is to match the stress vector along a portion of the boundary to a given vector; instability suppression is achieved by choosing the given vector to be that of a steady base flow, e.g., Blasius boundary layer. Control is effected through the injection or suction of fluid through a single orifice on the boundary. The present approach couples the time-dependent Navier-Stokes system with an adjoint Navier-Stokes system and optimality conditions from which optimal states, i.e., unsteady flow fields, and control, e.g., actuators, may be determined. The results demonstrate that instability suppression can be achieved without any a priori knowledge of the disturbance, which is significant because other control techniques have required some knowledge of the flow unsteadiness such as frequencies, instability type, etc.

Opinion control in complex networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki

2015-03-01

In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.

Optimal Culling and Biocontrol in a Predator-Prey Model.

PubMed

Numfor, Eric; Hilker, Frank M; Lenhart, Suzanne

2017-01-01

Invasive species cause enormous problems in ecosystems around the world. Motivated by introduced feral cats that prey on bird populations and threaten to drive them extinct on remote oceanic islands, we formulate and analyze optimal control problems. Their novelty is that they involve both scalar and time-dependent controls. They represent different forms of control, namely the initial release of infected predators on the one hand and culling as well as trapping, infecting, and returning predators on the other hand. Combinations of different control methods have been proposed to complement their respective strengths in reducing predator numbers and thus protecting endangered prey. Here, we formulate and analyze an eco-epidemiological model, provide analytical results on the optimal control problem, and use a forward-backward sweep method for numerical simulations. By taking into account different ecological scenarios, initial conditions, and control durations, our model allows to gain insight how the different methods interact and in which cases they could be effective.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE PAGES

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik; ...

2017-07-25

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

An optimal control method for fluid structure interaction systems via adjoint boundary pressure

NASA Astrophysics Data System (ADS)

Chirco, L.; Da Vià, R.; Manservisi, S.

2017-11-01

In recent year, in spite of the computational complexity, Fluid-structure interaction (FSI) problems have been widely studied due to their applicability in science and engineering. Fluid-structure interaction systems consist of one or more solid structures that deform by interacting with a surrounding fluid flow. FSI simulations evaluate the tensional state of the mechanical component and take into account the effects of the solid deformations on the motion of the interior fluids. The inverse FSI problem can be described as the achievement of a certain objective by changing some design parameters such as forces, boundary conditions and geometrical domain shapes. In this paper we would like to study the inverse FSI problem by using an optimal control approach. In particular we propose a pressure boundary optimal control method based on Lagrangian multipliers and adjoint variables. The objective is the minimization of a solid domain displacement matching functional obtained by finding the optimal pressure on the inlet boundary. The optimality system is derived from the first order necessary conditions by taking the Fréchet derivatives of the Lagrangian with respect to all the variables involved. The optimal solution is then obtained through a standard steepest descent algorithm applied to the optimality system. The approach presented in this work is general and could be used to assess other objective functionals and controls. In order to support the proposed approach we perform a few numerical tests where the fluid pressure on the domain inlet controls the displacement that occurs in a well defined region of the solid domain.

Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks

PubMed Central

Kleczkowski, Adam; Oleś, Katarzyna; Gudowska-Nowak, Ewa; Gilligan, Christopher A.

2012-01-01

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R0, does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only. PMID:21653570

Searching for the most cost-effective strategy for controlling epidemics spreading on regular and small-world networks.

PubMed

Kleczkowski, Adam; Oleś, Katarzyna; Gudowska-Nowak, Ewa; Gilligan, Christopher A

2012-01-07

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R(0), does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only.

Assessment of optimal strategies in a two-patch dengue transmission model with seasonality

PubMed Central

Lee, Chang Hyeong; Lee, Sunmi

2017-01-01

Emerging and re-emerging dengue fever has posed serious problems to public health officials in many tropical and subtropical countries. Continuous traveling in seasonally varying areas makes it more difficult to control the spread of dengue fever. In this work, we consider a two-patch dengue model that can capture the movement of host individuals between and within patches using a residence-time matrix. A previous two-patch dengue model without seasonality is extended by adding host demographics and seasonal forcing in the transmission rates. We investigate the effects of human movement and seasonality on the two-patch dengue transmission dynamics. Motivated by the recent Peruvian dengue data in jungle/rural areas and coast/urban areas, our model mimics the seasonal patterns of dengue outbreaks in two patches. The roles of seasonality and residence-time configurations are highlighted in terms of the seasonal reproduction number and cumulative incidence. Moreover, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in the presence of seasonality. Our findings demonstrate that optimal patch-specific control strategies are sensitive to seasonality and residence-time scenarios. Targeting only the jungle (or endemic) is as effective as controlling both patches under weak coupling or symmetric mobility. However, focusing on intervention for the city (or high density areas) turns out to be optimal when two patches are strongly coupled with asymmetric mobility. PMID:28301523

Improvement of cardiac function with device-based diaphragmatic stimulation in chronic heart failure patients: the randomized, open-label, crossover Epiphrenic II Pilot Trial.

PubMed

Beeler, Remo; Schoenenberger, Andreas W; Bauer, Peter; Kobza, Richard; Bergner, Michael; Mueller, Xavier; Schlaepfer, Reinhard; Zuber, Michel; Erne, Susanne; Erne, Paul

2014-03-01

Device-based pacing-induced diaphragmatic stimulation (PIDS) may have therapeutic potential for chronic heart failure (HF) patients. We studied the effects of PIDS on cardiac function and functional outcomes. In 24 chronic HF patients with CRT, an additional electrode was attached to the left diaphragm. Randomized into two groups, patients received the following PIDS modes for 3 weeks in a different sequence: (i) PIDS off (control group); (ii) PIDS 0 ms mode (PIDS simultaneously with ventricular CRT pulse); or (iii) PIDS optimized mode (PIDS with optimized delay to ventricular CRT pulse). For PIDS optimization, acoustic cardiography was used. Effects of each PIDS mode on dyspnoea, power during exercise testing, and LVEF were assessed. Dyspnoea improved with the PIDS 0 ms mode (P = 0.057) and the PIDS optimized mode (P = 0.034) as compared with the control group. Maximal power increased from median 100.5 W in the control group to 104.0 W in the PIDS 0 ms mode (P = 0.092) and 109.5 W in the PIDS optimized mode (P = 0.022). Median LVEF was 33.5% in the control group, 33.0% in the PIDS 0 ms mode, and 37.0% in the PIDS optimized mode (P = 0.763 and P = 0.009 as compared with the control group, respectively). PIDS was asymptomatic in all patients. PIDS improves dyspnoea, working capacity, and LVEF in chronic HF patients over a 3 week period in addition to CRT. This pilot study demonstrates proof of principle of an innovative technology which should be confirmed in a larger sample. NCT00769678. © 2013 The Authors. European Journal of Heart Failure © 2013 European Society of Cardiology.

Pathway optimization by re-design of untranslated regions for L-tyrosine production in Escherichia coli

PubMed Central

Cheol Kim, Seong; Eun Min, Byung; Gyu Hwang, Hyun; Woo Seo, Sang; Yeol Jung, Gyoo

2015-01-01

L-tyrosine is a commercially important compound in the food, pharmaceutical, chemical, and cosmetic industries. Although several attempts have been made to improve L-tyrosine production, translation-level expression control and carbon flux rebalancing around phosphoenolpyruvate (PEP) node still remain to be achieved for optimizing the pathway. Here, we demonstrate pathway optimization by altering gene expression levels for L-tyrosine production in Escherichia coli. To optimize the L-tyrosine biosynthetic pathway, a synthetic constitutive promoter and a synthetic 5′-untranslated region (5′-UTR) were introduced for each gene of interest to allow for control at both transcription and translation levels. Carbon flux rebalancing was achieved by controlling the expression level of PEP synthetase using UTR Designer. The L-tyrosine productivity of the engineered E. coli strain was increased through pathway optimization resulting in 3.0 g/L of L-tyrosine titer, 0.0354 g L-tyrosine/h/g DCW of productivity, and 0.102 g L-tyrosine/g glucose yield. Thus, this work demonstrates that pathway optimization by 5′-UTR redesign is an effective strategy for the development of efficient L-tyrosine-producing bacteria. PMID:26346938

Optimal shifting control strategy in inertia phase of an automatic transmission for automotive applications

NASA Astrophysics Data System (ADS)

Meng, Fei; Tao, Gang; Zhang, Tao; Hu, Yihuai; Geng, Peng

2015-08-01

Shifting quality is a crucial factor in all parts of the automobile industry. To ensure an optimal gear shifting strategy with best fuel economy for a stepped automatic transmission, the controller should be designed to meet the challenge of lacking of a feedback sensor to measure the relevant variables. This paper focuses on a new kind of automatic transmission using proportional solenoid valve to control the clutch pressure, a speed difference of the clutch based control strategy is designed for the shift control during the inertia phase. First, the mechanical system is shown and the system dynamic model is built. Second, the control strategy is designed based on the characterization analysis of models which are derived from dynamics of the drive line and electro-hydraulic actuator. Then, the controller uses conventional Proportional-Integral-Derivative control theory, and a robust two-degree-of-freedom controller is also carried out to determine the optimal control parameters to further improve the system performance. Finally, the designed control strategy with different controller is implemented on a simulation model. The compared results show that the speed difference of clutch can track the desired trajectory well and improve the shift quality effectively.

Intracavity adaptive optics. 1: Astigmatism correction performance.

PubMed

Spinhirne, J M; Anafi, D; Freeman, R H; Garcia, H R

1981-03-15

A detailed experimental study has been conducted on adaptive optical control methodologies inside a laser resonator. A comparison is presented of several optimization techniques using a multidither zonal coherent optical adaptive technique system within a laser resonator for the correction of astigmatism. A dramatic performance difference is observed when optimizing on beam quality compared with optimizing on power-in-the-bucket. Experimental data are also presented on proper selection criteria for dither frequencies when controlling phase front errors. The effects of hardware limitations and design considerations on the performance of the system are presented, and general conclusions and physical interpretations on the results are made when possible.

Self-Contained Automated Methodology for Optimal Flow Control

NASA Technical Reports Server (NTRS)

Joslin, Ronald D.; Gunzburger, Max D.; Nicolaides, Roy A.; Erlebacherl, Gordon; Hussaini, M. Yousuff

1997-01-01

This paper describes a self-contained, automated methodology for active flow control which couples the time-dependent Navier-Stokes system with an adjoint Navier-Stokes system and optimality conditions from which optimal states, i.e., unsteady flow fields and controls (e.g., actuators), may be determined. The problem of boundary layer instability suppression through wave cancellation is used as the initial validation case to test the methodology. Here, the objective of control is to match the stress vector along a portion of the boundary to a given vector; instability suppression is achieved by choosing the given vector to be that of a steady base flow. Control is effected through the injection or suction of fluid through a single orifice on the boundary. The results demonstrate that instability suppression can be achieved without any a priori knowledge of the disturbance, which is significant because other control techniques have required some knowledge of the flow unsteadiness such as frequencies, instability type, etc. The present methodology has been extended to three dimensions and may potentially be applied to separation control, re-laminarization, and turbulence control applications using one to many sensors and actuators.

Optimism and well-being: A prospective multi-method and multi-dimensional examination of optimism as a resilience factor following the occurrence of stressful life events

PubMed Central

Kleiman, Evan M.; Chiara, Alexandra M.; Liu, Richard T.; Jager-Hyman, Shari G.; Choi, Jimmy Y.; Alloy, Lauren B.

2016-01-01

Optimism has been conceptualized variously as positive expectations for the future (Scheier & Carver, 1985), optimistic attributions (Peterson & Seligman, 1984), illusion of control (Alloy & Abramson, 1979), and self-enhancing biases (Weinstein, 1980). Relatively little research has examined these multiple dimensions of optimism in relation to psychological and physical health. The current study assessed the multidimensional nature of optimism within a prospective vulnerability-stress framework. Initial principal component analyses revealed the following dimensions: Positive Expectations (PE), Inferential Style (IS), Sense of Invulnerability (SI), and Overconfidence (O). Prospective follow-up analyses demonstrated that PE was associated with fewer depressive episodes and moderated the effect of stressful life events on depressive symptoms. SI also moderated the effect of life stress on anxiety symptoms. Generally, our findings indicated that optimism is a multifaceted construct and not all forms of optimism have the same effects on well-being. Specifically, our findings indicted that PE may be the most relevant to depression, whereas SI may be the most relevant to anxiety. PMID:26558316

Optimally growing boundary layer disturbances in a convergent nozzle preceded by a circular pipe

NASA Astrophysics Data System (ADS)

Uzun, Ali; Davis, Timothy B.; Alvi, Farrukh S.; Hussaini, M. Yousuff

2017-06-01

We report the findings from a theoretical analysis of optimally growing disturbances in an initially turbulent boundary layer. The motivation behind this study originates from the desire to generate organized structures in an initially turbulent boundary layer via excitation by disturbances that are tailored to be preferentially amplified. Such optimally growing disturbances are of interest for implementation in an active flow control strategy that is investigated for effective jet noise control. Details of the optimal perturbation theory implemented in this study are discussed. The relevant stability equations are derived using both the standard decomposition and the triple decomposition. The chosen test case geometry contains a convergent nozzle, which generates a Mach 0.9 round jet, preceded by a circular pipe. Optimally growing disturbances are introduced at various stations within the circular pipe section to facilitate disturbance energy amplification upstream of the favorable pressure gradient zone within the convergent nozzle, which has a stabilizing effect on disturbance growth. Effects of temporal frequency, disturbance input and output plane locations as well as separation distance between output and input planes are investigated. The results indicate that optimally growing disturbances appear in the form of longitudinal counter-rotating vortex pairs, whose size can be on the order of several times the input plane mean boundary layer thickness. The azimuthal wavenumber, which represents the number of counter-rotating vortex pairs, is found to generally decrease with increasing separation distance. Compared to the standard decomposition, the triple decomposition analysis generally predicts relatively lower azimuthal wavenumbers and significantly reduced energy amplification ratios for the optimal disturbances.

A robust hybrid fuzzy-simulated annealing-intelligent water drops approach for tuning a distribution static compensator nonlinear controller in a distribution system

NASA Astrophysics Data System (ADS)

Bagheri Tolabi, Hajar; Hosseini, Rahil; Shakarami, Mahmoud Reza

2016-06-01

This article presents a novel hybrid optimization approach for a nonlinear controller of a distribution static compensator (DSTATCOM). The DSTATCOM is connected to a distribution system with the distributed generation units. The nonlinear control is based on partial feedback linearization. Two proportional-integral-derivative (PID) controllers regulate the voltage and track the output in this control system. In the conventional scheme, the trial-and-error method is used to determine the PID controller coefficients. This article uses a combination of a fuzzy system, simulated annealing (SA) and intelligent water drops (IWD) algorithms to optimize the parameters of the controllers. The obtained results reveal that the response of the optimized controlled system is effectively improved by finding a high-quality solution. The results confirm that using the tuning method based on the fuzzy-SA-IWD can significantly decrease the settling and rising times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM. The proposed hybrid tuning method for the partial feedback linearizing (PFL) controller achieved better regulation of the direct current voltage for the capacitor within the DSTATCOM. Furthermore, in the event of a fault the proposed controller tuned by the fuzzy-SA-IWD method showed better performance than the conventional controller or the PFL controller without optimization by the fuzzy-SA-IWD method with regard to both fault duration and clearing times.

Constraining neutron guide optimizations with phase-space considerations

NASA Astrophysics Data System (ADS)

Bertelsen, Mads; Lefmann, Kim

2016-09-01

We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

The application of neural network PID controller to control the light gasoline etherification

NASA Astrophysics Data System (ADS)

Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

2017-06-01

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

Optimal control penalty finite elements - Applications to integrodifferential equations

NASA Astrophysics Data System (ADS)

Chung, T. J.

The application of the optimal-control/penalty finite-element method to the solution of integrodifferential equations in radiative-heat-transfer problems (Chung et al.; Chung and Kim, 1982) is discussed and illustrated. The nonself-adjointness of the convective terms in the governing equations is treated by utilizing optimal-control cost functions and employing penalty functions to constrain auxiliary equations which permit the reduction of second-order derivatives to first order. The OCPFE method is applied to combined-mode heat transfer by conduction, convection, and radiation, both without and with scattering and viscous dissipation; the results are presented graphically and compared to those obtained by other methods. The OCPFE method is shown to give good results in cases where standard Galerkin FE fail, and to facilitate the investigation of scattering and dissipation effects.

Seismic design of passive tuned mass damper parameters using active control algorithm

NASA Astrophysics Data System (ADS)

Chang, Chia-Ming; Shia, Syuan; Lai, Yong-An

2018-07-01

Tuned mass dampers are a widely-accepted control method to effectively reduce the vibrations of tall buildings. A tuned mass damper employs a damped harmonic oscillator with specific dynamic characteristics, thus the response of structures can be regulated by the additive dynamics. The additive dynamics are, however, similar to the feedback control system in active control. Therefore, the objective of this study is to develop a new tuned mass damper design procedure based on the active control algorithm, i.e., the H2/LQG control. This design facilitates the similarity of feedback control in the active control algorithm to determine the spring and damper in a tuned mass damper. Given a mass ratio between the damper and structure, the stiffness and damping coefficient of the tuned mass damper are derived by minimizing the response objective function of the primary structure, where the structural properties are known. Varying a single weighting in this objective function yields the optimal TMD design when the minimum peak in the displacement transfer function of the structure with the TMD is met. This study examines various objective functions as well as derives the associated equations to compute the stiffness and damping coefficient. The relationship between the primary structure and optimal tuned mass damper is parametrically studied. Performance is evaluated by exploring the h2-and h∞-norms of displacements and accelerations of the primary structure. In time-domain analysis, the damping effectiveness of the tune mass damper controlled structures is investigated under impulse excitation. Structures with the optimal tuned mass dampers are also assessed under seismic excitation. As a result, the proposed design procedure produces an effective tuned mass damper to be employed in a structure against earthquakes.

Control theory based airfoil design using the Euler equations

NASA Technical Reports Server (NTRS)

Jameson, Antony; Reuther, James

1994-01-01

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using the potential flow equation with either a conformal mapping or a general coordinate system. The goal of our present work is to extend the development to treat the Euler equations in two-dimensions by procedures that can readily be generalized to treat complex shapes in three-dimensions. Therefore, we have developed methods which can address airfoil design through either an analytic mapping or an arbitrary grid perturbation method applied to a finite volume discretization of the Euler equations. Here the control law serves to provide computationally inexpensive gradient information to a standard numerical optimization method. Results are presented for both the inverse problem and drag minimization problem.

Zero-sum two-player game theoretic formulation of affine nonlinear discrete-time systems using neural networks.

PubMed

Mehraeen, Shahab; Dierks, Travis; Jagannathan, S; Crow, Mariesa L

2013-12-01

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.

Tire-road friction estimation and traction control strategy for motorized electric vehicle.

PubMed

Jin, Li-Qiang; Ling, Mingze; Yue, Weiqiang

2017-01-01

In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS).

Microgravity vibration isolation: An optimal control law for the one-dimensional case

NASA Technical Reports Server (NTRS)

Hampton, Richard D.; Grodsinsky, Carlos M.; Allaire, Paul E.; Lewis, David W.; Knospe, Carl R.

1991-01-01

Certain experiments contemplated for space platforms must be isolated from the accelerations of the platform. An optimal active control is developed for microgravity vibration isolation, using constant state feedback gains (identical to those obtained from the Linear Quadratic Regulator (LQR) approach) along with constant feedforward gains. The quadratic cost function for this control algorithm effectively weights external accelerations of the platform disturbances by a factor proportional to (1/omega) exp 4. Low frequency accelerations are attenuated by greater than two orders of magnitude. The control relies on the absolute position and velocity feedback of the experiment and the absolute position and velocity feedforward of the platform, and generally derives the stability robustness characteristics guaranteed by the LQR approach to optimality. The method as derived is extendable to the case in which only the relative positions and velocities and the absolute accelerations of the experiment and space platform are available.

Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming.

PubMed

Wang, Jun-Sheng; Yang, Guang-Hong

2017-07-25

This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where the terms depending on the innovation covariance matrix are available with the aid of the innovation covariance matrix identified beforehand. Therefore, by iterating the Bellman equation, the resulting value function can converge to the optimal one in the presence of the aforementioned noise, and the nearly optimal control laws are delivered. To show the effectiveness and the advantages of the proposed approach, a simulation example and a velocity control experiment on a dc machine are employed.

Analysis of torque transmitting behavior and wheel slip prevention control during regenerative braking for high speed EMU trains

NASA Astrophysics Data System (ADS)

Xu, Kun; Xu, Guo-Qing; Zheng, Chun-Hua

2016-04-01

The wheel-rail adhesion control for regenerative braking systems of high speed electric multiple unit trains is crucial to maintaining the stability, improving the adhesion utilization, and achieving deep energy recovery. There remain technical challenges mainly because of the nonlinear, uncertain, and varying features of wheel-rail contact conditions. This research analyzes the torque transmitting behavior during regenerative braking, and proposes a novel methodology to detect the wheel-rail adhesion stability. Then, applications to the wheel slip prevention during braking are investigated, and the optimal slip ratio control scheme is proposed, which is based on a novel optimal reference generation of the slip ratio and a robust sliding mode control. The proposed methodology achieves the optimal braking performance without the wheel-rail contact information. Numerical simulation results for uncertain slippery rails verify the effectiveness of the proposed methodology.

Tire-road friction estimation and traction control strategy for motorized electric vehicle

PubMed Central

Jin, Li-Qiang; Yue, Weiqiang

2017-01-01

In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS). PMID:28662053

Microgravity vibration isolation: An optimal control law for the one-dimensional case

NASA Technical Reports Server (NTRS)

Hampton, R. D.; Grodsinsky, C. M.; Allaire, P. E.; Lewis, D. W.; Knospe, C. R.

1991-01-01

Certain experiments contemplated for space platforms must be isolated from the accelerations of the platforms. An optimal active control is developed for microgravity vibration isolation, using constant state feedback gains (identical to those obtained from the Linear Quadratic Regulator (LQR) approach) along with constant feedforward (preview) gains. The quadratic cost function for this control algorithm effectively weights external accelerations of the platform disturbances by a factor proportional to (1/omega)(exp 4). Low frequency accelerations (less than 50 Hz) are attenuated by greater than two orders of magnitude. The control relies on the absolute position and velocity feedback of the experiment and the absolute position and velocity feedforward of the platform, and generally derives the stability robustness characteristics guaranteed by the LQR approach to optimality. The method as derived is extendable to the case in which only the relative positions and velocities and the absolute accelerations of the experiment and space platform are available.

Applying Strategies of Selection, Optimization, and Compensation to Maintain Work Ability-A Psychosocial Resource Complementing the Job Demand-Control Model? Results From the Representative lidA Cohort Study on Work, Age, and Health in Germany.

PubMed

Riedel, Natalie; Müller, Andreas; Ebener, Melanie

2015-05-01

To investigate whether aging employees' selection, optimization, and compensation (SOC) strategies were associated with work ability over and above job demand and control variables, as well as across professions. Multivariable linear regressions were conducted using a representative sample of German employees born in 1959 and 1965 (N = 6057). SOC was assessed to have an independent effect on work ability. Associations of job demands and control variables with work ability were more prominent. The SOC tended to enhance the positive association between decision authority and work ability. Individual strategies of selection, optimization, and compensation could be considered as psychosocial resources adding up to a better work ability and complement prevention programs. Workplace interventions should deal with job demands and control to maintain older employees' work ability in times of working population shrinkage.

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

PubMed Central

Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution. PMID:29186166

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment.

PubMed

Li, Jianjun; Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

Optimal placement of FACTS devices using optimization techniques: A review

NASA Astrophysics Data System (ADS)

Gaur, Dipesh; Mathew, Lini

2018-03-01

Modern power system is dealt with overloading problem especially transmission network which works on their maximum limit. Today’s power system network tends to become unstable and prone to collapse due to disturbances. Flexible AC Transmission system (FACTS) provides solution to problems like line overloading, voltage stability, losses, power flow etc. FACTS can play important role in improving static and dynamic performance of power system. FACTS devices need high initial investment. Therefore, FACTS location, type and their rating are vital and should be optimized to place in the network for maximum benefit. In this paper, different optimization methods like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) etc. are discussed and compared for optimal location, type and rating of devices. FACTS devices such as Thyristor Controlled Series Compensator (TCSC), Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM) are considered here. Mentioned FACTS controllers effects on different IEEE bus network parameters like generation cost, active power loss, voltage stability etc. have been analyzed and compared among the devices.

Two-dimensional optimization of free electron laser designs

DOEpatents

Prosnitz, Donald; Haas, Roger A.

1985-01-01

Off-axis, two-dimensional designs for free electron lasers that maintain correspondence of a light beam with a "synchronous electron" at an optimal transverse radius r>0 to achieve increased beam trapping efficiency and enhanced laser beam wavefront control so as to decrease optical beam diffraction and other deleterious effects.

Optimization of an efficient transcuticular delivery system for control of citrus huanglongbing

USDA-ARS?s Scientific Manuscript database

We experimentally develop and optimize a transcuticular nano-delivery system for enhancing permeation of effective compound against HLB disease through citrus cuticle into the phloem by foliar spray or bark application. The results showed that two kinds of nanoemulsions (W/O and O/W) with the smalle...

Optimization of control gain by operator adjustment

NASA Technical Reports Server (NTRS)

Kruse, W.; Rothbauer, G.

1973-01-01

An optimal gain was established by measuring errors at 5 discrete control gain settings in an experimental set-up consisting of a 2-dimensional, first-order pursuit tracking task performed by subjects (S's). No significant experience effect on optimum gain setting was found in the first experiment. During the second experiment, in which control gain was continuously adjustable, high experienced S's tended to reach the previously determined optimum gain quite accurately and quickly. Less experienced S's tended to select a marginally optimum gain either below or above the experimentally determined optimum depending on initial control gain setting, although mean settings of both groups were equal. This quick and simple method is recommended for selecting control gains for different control systems and forcing functions.

A Case Study on the Application of a Structured Experimental Method for Optimal Parameter Design of a Complex Control System

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2015-01-01

This report documents a case study on the application of Reliability Engineering techniques to achieve an optimal balance between performance and robustness by tuning the functional parameters of a complex non-linear control system. For complex systems with intricate and non-linear patterns of interaction between system components, analytical derivation of a mathematical model of system performance and robustness in terms of functional parameters may not be feasible or cost-effective. The demonstrated approach is simple, structured, effective, repeatable, and cost and time efficient. This general approach is suitable for a wide range of systems.

Strategies for global optimization in photonics design.

PubMed

Vukovic, Ana; Sewell, Phillip; Benson, Trevor M

2010-10-01

This paper reports on two important issues that arise in the context of the global optimization of photonic components where large problem spaces must be investigated. The first is the implementation of a fast simulation method and associated matrix solver for assessing particular designs and the second, the strategies that a designer can adopt to control the size of the problem design space to reduce runtimes without compromising the convergence of the global optimization tool. For this study an analytical simulation method based on Mie scattering and a fast matrix solver exploiting the fast multipole method are combined with genetic algorithms (GAs). The impact of the approximations of the simulation method on the accuracy and runtime of individual design assessments and the consequent effects on the GA are also examined. An investigation of optimization strategies for controlling the design space size is conducted on two illustrative examples, namely, 60° and 90° waveguide bends based on photonic microstructures, and their effectiveness is analyzed in terms of a GA's ability to converge to the best solution within an acceptable timeframe. Finally, the paper describes some particular optimized solutions found in the course of this work.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Adaptive Wing Camber Optimization: A Periodic Perturbation Approach

NASA Technical Reports Server (NTRS)

Espana, Martin; Gilyard, Glenn

1994-01-01

Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.

Design of Robust Adaptive Unbalance Response Controllers for Rotors with Magnetic Bearings

NASA Technical Reports Server (NTRS)

Knospe, Carl R.; Tamer, Samir M.; Fedigan, Stephen J.

1996-01-01

Experimental results have recently demonstrated that an adaptive open loop control strategy can be highly effective in the suppression of unbalance induced vibration on rotors supported in active magnetic bearings. This algorithm, however, relies upon a predetermined gain matrix. Typically, this matrix is determined by an optimal control formulation resulting in the choice of the pseudo-inverse of the nominal influence coefficient matrix as the gain matrix. This solution may result in problems with stability and performance robustness since the estimated influence coefficient matrix is not equal to the actual influence coefficient matrix. Recently, analysis tools have been developed to examine the robustness of this control algorithm with respect to structured uncertainty. Herein, these tools are extended to produce a design procedure for determining the adaptive law's gain matrix. The resulting control algorithm has a guaranteed convergence rate and steady state performance in spite of the uncertainty in the rotor system. Several examples are presented which demonstrate the effectiveness of this approach and its advantages over the standard optimal control formulation.

Differential elevon effectiveness lateral control optimization and elevon hinge moment investigation on a 0.015 scale space shuttle orbiter model 49-0 (140A/B/C modified) in the AECD VKF wind tunnel A (0A115)

NASA Technical Reports Server (NTRS)

Esparza, V.

1975-01-01

Experimental aerodynamic investigations were conducted in the Arnold Engineering Development Center (AEDC) Von Karman Facility Tunnel A on a scale model of the space shuttle orbiter. The objectives of this test were: (1) determine supersonic differential elevon/aileron lateral control optimization, (2) determine supersonic elevon hinge moments, (3) determine the supersonic effects of the new baseline 6-inch elevon/elevon and elevon/fuselage gaps, and 4) determine the supersonic effects of the new short (VL70-008410) OMS pods. Six-component aerodynamic force, moment, and elevon hinge moment data were recorded.

Establishing the ferret as a gyrencephalic animal model of traumatic brain injury: Optimization of controlled cortical impact procedures.

PubMed

Schwerin, Susan C; Hutchinson, Elizabeth B; Radomski, Kryslaine L; Ngalula, Kapinga P; Pierpaoli, Carlo M; Juliano, Sharon L

2017-06-15

Although rodent TBI studies provide valuable information regarding the effects of injury and recovery, an animal model with neuroanatomical characteristics closer to humans may provide a more meaningful basis for clinical translation. The ferret has a high white/gray matter ratio, gyrencephalic neocortex, and ventral hippocampal location. Furthermore, ferrets are amenable to behavioral training, have a body size compatible with pre-clinical MRI, and are cost-effective. We optimized the surgical procedure for controlled cortical impact (CCI) using 9 adult male ferrets. We used subject-specific brain/skull morphometric data from anatomical MRIs to overcome across-subject variability for lesion placement. We also reflected the temporalis muscle, closed the craniotomy, and used antibiotics. We then gathered MRI, behavioral, and immunohistochemical data from 6 additional animals using the optimized surgical protocol: 1 control, 3 mild, and 1 severely injured animals (surviving one week) and 1 moderately injured animal surviving sixteen weeks. The optimized surgical protocol resulted in consistent injury placement. Astrocytic reactivity increased with injury severity showing progressively greater numbers of astrocytes within the white matter. The density and morphological changes of microglia amplified with injury severity or time after injury. Motor and cognitive impairments scaled with injury severity. The optimized surgical methods differ from those used in the rodent, and are integral to success using a ferret model. We optimized ferret CCI surgery for consistent injury placement. The ferret is an excellent animal model to investigate pathophysiological and behavioral changes associated with TBI. Published by Elsevier B.V.

The cost of model reference adaptive control - Analysis, experiments, and optimization

NASA Technical Reports Server (NTRS)

Messer, R. S.; Haftka, R. T.; Cudney, H. H.

1993-01-01

In this paper the performance of Model Reference Adaptive Control (MRAC) is studied in numerical simulations and verified experimentally with the objective of understanding how differences between the plant and the reference model affect the control effort. MRAC is applied analytically and experimentally to a single degree of freedom system and analytically to a MIMO system with controlled differences between the model and the plant. It is shown that the control effort is sensitive to differences between the plant and the reference model. The effects of increased damping in the reference model are considered, and it is shown that requiring the controller to provide increased damping actually decreases the required control effort when differences between the plant and reference model exist. This result is useful because one of the first attempts to counteract the increased control effort due to differences between the plant and reference model might be to require less damping, however, this would actually increase the control effort. Optimization of weighting matrices is shown to help reduce the increase in required control effort. However, it was found that eventually the optimization resulted in a design that required an extremely high sampling rate for successful realization.

Affectionless control by the same-sex parents increases dysfunctional attitudes about achievement.

PubMed

Otani, Koichi; Suzuki, Akihito; Matsumoto, Yoshihiko; Sadahiro, Ryoichi; Enokido, Masanori

2014-08-01

The affectionless control parenting has been associated with depression in recipients. The aim of this study was to examine the effect of this parenting style on dysfunctional attitudes predisposing to depression. The subjects were 666 Japanese volunteers. Perceived parental rearing was evaluated by the Parental Bonding Instrument, which has the care and protection subscales. Parental rearing was classified into four types, i.e., optimal parenting (high care/low protection), affectionate constraint (high care/high protection), neglectful parenting (low care/low protection), and affectionless control (low care/high protection). Dysfunctional attitudes were evaluated by the 24-item Dysfunctional Attitude Scale, which has the achievement, dependency and self-control subscales. Males with paternal affectionless control had higher achievement scores than those with paternal optimal parenting (P=.016). Similarly, females with maternal affectionless control had higher achievement scores than those with maternal optimal parenting (P=.016). The present study suggests that affectionless control by the same-sex parents increases dysfunctional attitudes about achievement. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Optimization design of urban expressway ramp control

NASA Astrophysics Data System (ADS)

Xu, Hongke; Li, Peiqi; Zheng, Jinnan; Sun, Xiuzhen; Lin, Shan

2017-05-01

In this paper, various types of expressway systems are analyzed, and a variety of signal combinations are proposed to mitigate traffic congestion. And various signal combinations are used to verify the effectiveness of the multi-signal combinatorial control strategy. The simulation software VISSIM was used to simulate the system. Based on the network model of 25 kinds of road length combinations and the simulation results, an optimization scheme suitable for the practical road model is summarized. The simulation results show that the controller can reduce the travel time by 25% under the large traffic flow and improve the road capacity by about 20%.

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

Design of Simulation Product for Stability of Electric Power System Using Power System Stabilizer and Optimal Control

NASA Astrophysics Data System (ADS)

Junaidi, Agus; Hamid, K. Abdul

2018-03-01

This paper will discuss the use of optimal control and Power System Stabilizer (PSS) in improving the oscillation of electric power system. Oscillations in the electric power system can occur due to the sudden release of the load (Switcing-Off). The oscillation of an unstable system for a long time causes the equipment to work in an interruption. To overcome this problem, a control device is required that can work effectively in repairing the oscillation. The power system is modeled from the Single Machine Infinite Bus Model (SMIB). The state space equation is used to mathematically model SMIB. SMIB system which is a plant will be formed togetherness state variables (State-Space), using riccati equation then determined the optimal gain as controller plant. Plant is also controlled by Power Stabilizer System using phase compensation method. Using Matlab Software based simulation will be observed response of rotor speed change and rotor angle change for each of the two controlling methods. Simulation results using the Simulink-MATLAB 6.1 software will compare the analysis of the plant state in Open loop state and use the controller. The simulation response shows that the optimal control and PSS can improve the stability of the power system in terms of acceleration to achieve settling-time and Over Shoot improvement. From the results of both methods are able to improve system performance.

Modified independent modal space control method for active control of flexible systems

NASA Technical Reports Server (NTRS)

Baz, A.; Poh, S.

1987-01-01

A modified independent modal space control (MIMSC) method is developed for designing active vibration control systems for large flexible structures. The method accounts for the interaction between the controlled and residual modes. It incorporates also optimal placement procedures for selecting the optimal locations of the actuators in the structure in order to minimize the structural vibrations as well as the actuation energy. The MIMSC method relies on an important feature which is based on time sharing of a small number of actuators, in the modal space, to control effectively a large number of modes. Numerical examples are presented to illustrate the application of the method to generic flexible systems. The results obtained suggest the potential of the devised method in designing efficient active control systems for large flexible structures.

Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Hanquan

2016-03-01

In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

Optimal control and optimal trajectories of regional macroeconomic dynamics based on the Pontryagin maximum principle

NASA Astrophysics Data System (ADS)

Bulgakov, V. K.; Strigunov, V. V.

2009-05-01

The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Supersonic cruise research aircraft structural studies: Methods and results

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Gross, D.; Kurtze, W.; Newsom, J.; Wrenn, G.; Greene, W.

1981-01-01

NASA Langley Research Center SCAR in-house structural studies are reviewed. In methods development, advances include a new system of integrated computer programs called ISSYS, progress in determining aerodynamic loads and aerodynamically induced structural loads (including those due to gusts), flutter optimization for composite and metal airframe configurations using refined and simplified mathematical models, and synthesis of active controls. Results given address several aspects of various SCR configurations. These results include flutter penalties on composite wing, flutter suppression using active controls, roll control effectiveness, wing tip ground clearance, tail size effect on flutter, engine weight and mass distribution influence on flutter, and strength and flutter optimization of new configurations. The ISSYS system of integrated programs performed well in all the applications illustrated by the results, the diversity of which attests to ISSYS' versatility.

Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm.

PubMed

Peng, Jiansheng; Meng, Fanmei; Ai, Yuncan

2013-06-01

The artificial neural network (ANN) and genetic algorithm (GA) were combined to optimize the fermentation process for enhancing production of marine bacteriocin 1701 in a 5-L-stirred-tank. Fermentation time, pH value, dissolved oxygen level, temperature and turbidity were used to construct a "5-10-1" ANN topology to identify the nonlinear relationship between fermentation parameters and the antibiotic effects (shown as in inhibition diameters) of bacteriocin 1701. The predicted values by the trained ANN model were coincided with the observed ones (the coefficient of R(2) was greater than 0.95). As the fermentation time was brought in as one of the ANN input nodes, fermentation parameters could be optimized by stages through GA, and an optimal fermentation process control trajectory was created. The production of marine bacteriocin 1701 was significantly improved by 26% under the guidance of fermentation control trajectory that was optimized by using of combined ANN-GA method. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Framework for Modeling Emerging Diseases to Inform Management

PubMed Central

Katz, Rachel A.; Richgels, Katherine L.D.; Walsh, Daniel P.; Grant, Evan H.C.

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge. PMID:27983501

Design modification and optimisation of the perfusion system of a tri-axial bioreactor for tissue engineering.

PubMed

Hussein, Husnah; Williams, David J; Liu, Yang

2015-07-01

A systematic design of experiments (DOE) approach was used to optimize the perfusion process of a tri-axial bioreactor designed for translational tissue engineering exploiting mechanical stimuli and mechanotransduction. Four controllable design parameters affecting the perfusion process were identified in a cause-effect diagram as potential improvement opportunities. A screening process was used to separate out the factors that have the largest impact from the insignificant ones. DOE was employed to find the settings of the platen design, return tubing configuration and the elevation difference that minimise the load on the pump and variation in the perfusion process and improve the controllability of the perfusion pressures within the prescribed limits. DOE was very effective for gaining increased knowledge of the perfusion process and optimizing the process for improved functionality. It is hypothesized that the optimized perfusion system will result in improved biological performance and consistency.

A Framework for Modeling Emerging Diseases to Inform Management.

PubMed

Russell, Robin E; Katz, Rachel A; Richgels, Katherine L D; Walsh, Daniel P; Grant, Evan H C

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Optimization and Planning of Emergency Evacuation Routes Considering Traffic Control

PubMed Central

Zhang, Lijun; Wang, Zhaohua

2014-01-01

Emergencies, especially major ones, happen fast, randomly, as well as unpredictably, and generally will bring great harm to people's life and the economy. Therefore, governments and lots of professionals devote themselves to taking effective measures and providing optimal evacuation plans. This paper establishes two different emergency evacuation models on the basis of the maximum flow model (MFM) and the minimum-cost maximum flow model (MC-MFM), and proposes corresponding algorithms for the evacuation from one source node to one designated destination (one-to-one evacuation). Ulteriorly, we extend our evaluation model from one source node to many designated destinations (one-to-many evacuation). At last, we make case analysis of evacuation optimization and planning in Beijing, and obtain the desired evacuation routes and effective traffic control measures from the perspective of sufficiency and practicability. Both analytical and numerical results support that our models are feasible and practical. PMID:24991636

A framework for modeling emerging diseases to inform management

USGS Publications Warehouse

Russell, Robin E.; Katz, Rachel A.; Richgels, Katherine L. D.; Walsh, Daniel P.; Grant, Evan H. Campbell

2017-01-01

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

PubMed

Sharifi, N; Ozgoli, S; Ramezani, A

2017-06-01

Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimal actuator placement in adaptive precision trusses

NASA Technical Reports Server (NTRS)

Baycan, C. M.; Utku, S.; Das, S. K.; Wada, B. K.

1992-01-01

Actuator placement in adaptive truss structures is to cater to two needs: displacement control of precision points and preloading the elements to overcome joint slackness. Due to technological and financial considerations, the number of actuators available is much less than the degrees of freedom of precision points to be controlled and the degree of redundancy of the structure. An approach for optimal actuator location is outlined. Test cases to demonstrate the effectiveness of the scheme are applied to the Precision Segmented Reflector Truss.

Tactile Sensory Supplementation of Gravitational References to Optimize Sensorimotor Recovery

NASA Technical Reports Server (NTRS)

Black, F. O.; Paloski, W. H.; Bloomberg, J. J.; Wood, S. J.

2007-01-01

Integration of multi-sensory inputs to detect tilts relative to gravity is critical for sensorimotor control of upright orientation. Displaying body orientation using electrotactile feedback to the tongue has been developed by Bach-y- Rita and colleagues as a sensory aid to maintain upright stance with impaired vestibular feedback. This investigation has explored the effects of Tongue Elecrotactile Feedback (TEF) for control of posture and movement as a sensorimotor countermeasure, specifically addressing the optimal location of movement sensors.

Multi-objective optimal control of vibratory energy harvesting systems

NASA Astrophysics Data System (ADS)

Scruggs, J. T.

2008-03-01

This paper presents a new approach, based on H II optimal control theory, for the maximization of power generation in energy harvesting systems. The theory determines the optimal harvested power attainable through the use of power electronics to effect linear feedback control of transducer current. In contrast to most of the prior work in this area, which has assumed harmonic response, the theory proposed here applies to stochastically-excited systems in broadband response, and can be used to harvest power simultaneously from multiple significant vibratory modes. It is also applicable to coupled networks of many transducers. The theory accounts for the impact of energy harvesting on the dynamics of the vibrating system in which the transducers are embedded. It also accounts for resistive and semiconductor dissipation in the power-electronic network interfacing the transducers with energy storage. Thus, losses in the electronics are addressed in the formulation of the optimal control law. Finally, the H II-optimal control formulation of the problem naturally allows for harvested power to be systematically balanced against other response objectives. Here, this is illustrated by showing how the harvesting objective can be maximized, subject to the constraint that the transducer voltages be maintained below that of the power-electronic bus; a condition which is required for the power-electronic control system to be fully operational. Although the theory is applicable across a broad range of applications, it is presented in the context of a piezoelectric bimorph example.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Adaptive management for subsurface pressure and plume control in application to geological CO2 storage

NASA Astrophysics Data System (ADS)

Gonzalez-Nicolas, A.; Cihan, A.; Birkholzer, J. T.; Petrusak, R.; Zhou, Q.; Riestenberg, D. E.; Trautz, R. C.; Godec, M.

2016-12-01

Industrial-scale injection of CO2 into the subsurface can cause reservoir pressure increases that must be properly controlled to prevent any potential environmental impact. Excessive pressure buildup in reservoir may result in ground water contamination stemming from leakage through conductive pathways, such as improperly plugged abandoned wells or distant faults, and the potential for fault reactivation and possibly seal breaching. Brine extraction is a viable approach for managing formation pressure, effective stress, and plume movement during industrial-scale CO2 injection projects. The main objectives of this study are to investigate suitable different pressure management strategies involving active brine extraction and passive pressure relief wells. Adaptive optimized management of CO2 storage projects utilizes the advanced automated optimization algorithms and suitable process models. The adaptive management integrates monitoring, forward modeling, inversion modeling and optimization through an iterative process. In this study, we employ an adaptive framework to understand primarily the effects of initial site characterization and frequency of the model update (calibration) and optimization calculations for controlling extraction rates based on the monitoring data on the accuracy and the success of the management without violating pressure buildup constraints in the subsurface reservoir system. We will present results of applying the adaptive framework to test appropriateness of different management strategies for a realistic field injection project.

Bayesian Phase II optimization for time-to-event data based on historical information.

PubMed

Bertsche, Anja; Fleischer, Frank; Beyersmann, Jan; Nehmiz, Gerhard

2017-01-01

After exploratory drug development, companies face the decision whether to initiate confirmatory trials based on limited efficacy information. This proof-of-concept decision is typically performed after a Phase II trial studying a novel treatment versus either placebo or an active comparator. The article aims to optimize the design of such a proof-of-concept trial with respect to decision making. We incorporate historical information and develop pre-specified decision criteria accounting for the uncertainty of the observed treatment effect. We optimize these criteria based on sensitivity and specificity, given the historical information. Specifically, time-to-event data are considered in a randomized 2-arm trial with additional prior information on the control treatment. The proof-of-concept criterion uses treatment effect size, rather than significance. Criteria are defined on the posterior distribution of the hazard ratio given the Phase II data and the historical control information. Event times are exponentially modeled within groups, allowing for group-specific conjugate prior-to-posterior calculation. While a non-informative prior is placed on the investigational treatment, the control prior is constructed via the meta-analytic-predictive approach. The design parameters including sample size and allocation ratio are then optimized, maximizing the probability of taking the right decision. The approach is illustrated with an example in lung cancer.

Initial Assessment of a Variable-Camber Continuous Trailing-Edge Flap System on a Rigid Wing for Drag Reduction in Subsonic Cruise

NASA Technical Reports Server (NTRS)

Ippolito, Corey; Nguyen, Nhan; Totah, Joe; Trinh, Khanh; Ting, Eric

2013-01-01

In this paper, we describe an initial optimization study of a Variable-Camber Continuous Trailing-Edge Flap (VCCTEF) system. The VCCTEF provides a light-weight control system for aircraft with long flexible wings, providing efficient high-lift capability for takeoff and landing, and greater efficiency with reduced drag at cruising flight by considering the effects of aeroelastic wing deformations in the control law. The VCCTEF system is comprised of a large number of distributed and individually-actuatable control surfaces that are constrained in movement relative to neighboring surfaces, and are non-trivially coupled through structural aeroelastic dynamics. Minimzation of drag results in a constrained, coupled, non-linear optimization over a high-dimension search space. In this paper, we describe the modeling, analysis, and optimization of the VCCTEF system control inputs for minimum drag in cruise. The purpose of this initial study is to quantify the expected benefits of the system concept. The scope of this analysis is limited to consideration of a rigid wing without structural flexibility in a steady-state cruise condition at various fuel weights. For analysis, we developed an optimization engine that couples geometric synthesis with vortex-lattice analysis to automate the optimization procedure. In this paper, we present and describe the VCCTEF system concept, optimization approach and tools, run-time performance, and results of the optimization at 20%, 50%, and 80% fuel load. This initial limited-scope study finds the VCCTEF system can potentially gain nearly 10% reduction in cruise drag, provides greater drag savings at lower operating weight, and efficiency is negatively impacted by the severity of relative constraints between control surfaces.

Breaking down patient and physician barriers to optimize glycemic control in type 2 diabetes.

PubMed

Ross, Stuart A

2013-09-01

Approximately half of patients with type 2 diabetes (T2D) do not achieve globally recognized blood glucose targets, despite the availability of a wide range of effective glucose-lowering therapies. Failure to maintain good glycemic control increases the risk of diabetes-related complications and long-term health care costs. Patients must be brought under glycemic control to improve treatment outcomes, but existing barriers to optimizing glycemic control must first be overcome, including patient nonadherence to treatment, the failure of physicians to intensify therapy in a timely manner, and inadequacies in the health care system itself. The reasons for such barriers include treatment side effects, complex treatment regimens, needle anxiety, poor patient education, and the absence of an adequate patient care plan; however, newer therapies and devices, combined with comprehensive care plans involving adequate patient education, can help to minimize barriers and improve treatment outcomes. Copyright © 2013 Elsevier Inc. All rights reserved.

Energy Optimal Path Planning: Integrating Coastal Ocean Modelling with Optimal Control

NASA Astrophysics Data System (ADS)

Subramani, D. N.; Haley, P. J., Jr.; Lermusiaux, P. F. J.

2016-02-01

A stochastic optimization methodology is formulated for computing energy-optimal paths from among time-optimal paths of autonomous vehicles navigating in a dynamic flow field. To set up the energy optimization, the relative vehicle speed and headings are considered to be stochastic, and new stochastic Dynamically Orthogonal (DO) level-set equations that govern their stochastic time-optimal reachability fronts are derived. Their solution provides the distribution of time-optimal reachability fronts and corresponding distribution of time-optimal paths. An optimization is then performed on the vehicle's energy-time joint distribution to select the energy-optimal paths for each arrival time, among all stochastic time-optimal paths for that arrival time. The accuracy and efficiency of the DO level-set equations for solving the governing stochastic level-set reachability fronts are quantitatively assessed, including comparisons with independent semi-analytical solutions. Energy-optimal missions are studied in wind-driven barotropic quasi-geostrophic double-gyre circulations, and in realistic data-assimilative re-analyses of multiscale coastal ocean flows. The latter re-analyses are obtained from multi-resolution 2-way nested primitive-equation simulations of tidal-to-mesoscale dynamics in the Middle Atlantic Bight and Shelbreak Front region. The effects of tidal currents, strong wind events, coastal jets, and shelfbreak fronts on the energy-optimal paths are illustrated and quantified. Results showcase the opportunities for longer-duration missions that intelligently utilize the ocean environment to save energy, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Adaptive hybrid optimal quantum control for imprecisely characterized systems.

PubMed

Egger, D J; Wilhelm, F K

2014-06-20

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.

Optimal control analysis of Ebola disease with control strategies of quarantine and vaccination.

PubMed

Ahmad, Muhammad Dure; Usman, Muhammad; Khan, Adnan; Imran, Mudassar

2016-07-13

The 2014 Ebola epidemic is the largest in history, affecting multiple countries in West Africa. Some isolated cases were also observed in other regions of the world. In this paper, we introduce a deterministic SEIR type model with additional hospitalization, quarantine and vaccination components in order to understand the disease dynamics. Optimal control strategies, both in the case of hospitalization (with and without quarantine) and vaccination are used to predict the possible future outcome in terms of resource utilization for disease control and the effectiveness of vaccination on sick populations. Further, with the help of uncertainty and sensitivity analysis we also have identified the most sensitive parameters which effectively contribute to change the disease dynamics. We have performed mathematical analysis with numerical simulations and optimal control strategies on Ebola virus models. We used dynamical system tools with numerical simulations and optimal control strategies on our Ebola virus models. The original model, which allowed transmission of Ebola virus via human contact, was extended to include imperfect vaccination and quarantine. After the qualitative analysis of all three forms of Ebola model, numerical techniques, using MATLAB as a platform, were formulated and analyzed in detail. Our simulation results support the claims made in the qualitative section. Our model incorporates an important component of individuals with high risk level with exposure to disease, such as front line health care workers, family members of EVD patients and Individuals involved in burial of deceased EVD patients, rather than the general population in the affected areas. Our analysis suggests that in order for R 0 (i.e., the basic reproduction number) to be less than one, which is the basic requirement for the disease elimination, the transmission rate of isolated individuals should be less than one-fourth of that for non-isolated ones. Our analysis also predicts, we need high levels of medication and hospitalization at the beginning of an epidemic. Further, optimal control analysis of the model suggests the control strategies that may be adopted by public health authorities in order to reduce the impact of epidemics like Ebola.

Design of neural network model-based controller in a fed-batch microbial electrolysis cell reactor for bio-hydrogen gas production

NASA Astrophysics Data System (ADS)

Azwar; Hussain, M. A.; Abdul-Wahab, A. K.; Zanil, M. F.; Mukhlishien

2018-03-01

One of major challenge in bio-hydrogen production process by using MEC process is nonlinear and highly complex system. This is mainly due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. Thus, precise control is required for the MEC reactor, so that the amount of current required to produce hydrogen gas can be controlled according to the composition of the substrate in the reactor. In this work, two schemes for controlling the current and voltage of MEC were evaluated. The controllers evaluated are PID and Inverse neural network (NN) controller. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output is based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. The neural network-based controller results in fast response time and less overshoots while the offset effects are minimal. In conclusion, the Inverse neural network (NN)-based controllers provide better control performance for the MEC system compared to the PID controller.

Comparative dynamics in a health investment model.

PubMed

Eisenring, C

1999-10-01

The method of comparative dynamics fully exploits the inter-temporal structure of optimal control models. I derive comparative dynamic results in a simplified demand for health model. The effect of a change in the depreciation rate on the optimal paths for health capital and investment in health is studied by use of a phase diagram.

Moving characteristics of electrodes for vacuum circuit breaker

NASA Astrophysics Data System (ADS)

Yuan, Shun; Wang, Jimei

1994-05-01

This paper analyzes the effect of the gap of electrodes on the state of vacuum arc by experiment and theory. And the model of vacuum arc is set up. The optimal gap can be deduced from controlling the vacuum arc to be diffusion state, in order to get the optimal moving characteristics of electrodes.

Effects of nutrient optimization on intra-annual wood formation in Norway spruce.

PubMed

Kalliokoski, Tuomo; Mäkinen, Harri; Jyske, Tuula; Nöjd, Pekka; Linder, Sune

2013-11-01

In the Nordic countries, growth of Norway spruce (Picea abies (L.) Karst.) is generally limited by low availability of nutrients, especially nitrogen. Optimizing forest management requires better insight on how growth responds to the environmental conditions and their manipulation. The aim of this study was to analyse the effects of nutrient optimization on timing and the rate of tracheid formation of Norway spruce and to follow the differentiation of newly formed tracheids. The study was performed during two growing seasons in a long-term nutrient optimization experiment in northern Sweden, where all essential macro- and micronutrients were supplied in irrigation water every second day from mid-June to mid-August. The control plots were without additional nutrients and water. Tracheid formation in the stem was monitored throughout the growing season by weekly sampling of microcores at breast height. The onset of xylogenesis occurred in early June, but in early summer there were no significant between-treatment differences in the onset and relative rate of tracheid formation. In both treatments, the onset of secondary cell wall formation occurred in mid-June. The maximum rate of tracheid formation occurred close to the summer solstice and 50% of the tracheids had been accumulated in early July. Optimized nutrition resulted in the formation of ∼50% more tracheids and delayed the cessation of tracheid formation, which extended the tracheid formation period by 20-50%, compared with control trees. The increased growth was mainly an effect of enhanced tracheid formation rate during the mid- and later-part of the growing season. In the second year, the increased growth rate also resulted in 11% wider tracheids. We conclude that the onset and rate of tracheid formation and differentiation during summer is primarily controlled by photoperiod, temperature and availability of nutrients, rather than supply of carbohydrates.

Consideration of Optimal Input on Semi-Active Shock Control System

NASA Astrophysics Data System (ADS)

Kawashima, Takeshi

In press working, unidirectional transmission of mechanical energy is expected in order to maximize the life of the dies. To realize this transmission, the author has developed a shock control system based on the sliding mode control technique. The controller makes a collision-receiving object effectively deform plastically by adjusting the force of the actuator inserted between the colliding objects, while the deformation of the colliding object is held at the necessity minimum. However, the actuator has to generate a large force corresponding to the impulsive force. Therefore, development of such an actuator is a formidable challenge. The author has proposed a semi-active shock control system in which the impulsive force is adjusted by a brake mechanism, although the system exhibits inferior performance. Thus, the author has also designed an actuator using a friction device for semi-active shock control, and proposed an active seatbelt system as an application. The effectiveness has been confirmed by a numerical simulation and model experiment. In this study, the optimal deformation change of the colliding object is theoretically examined in the case that the collision-receiving object has perfect plasticity and the colliding object has perfect elasticity. As a result, the optimal input condition is obtained so that the ratio of the maximum deformation of the collision-receiving object to the maximum deformation of the colliding object becomes the maximum. Additionally, the energy balance is examined.

Optimal solution and optimality condition of the Hunter-Saxton equation

NASA Astrophysics Data System (ADS)

Shen, Chunyu

2018-02-01

This paper is devoted to the optimal distributed control problem governed by the Hunter-Saxton equation with constraints on the control. We first investigate the existence and uniqueness of weak solution for the controlled system with appropriate initial value and boundary conditions. In contrast with our previous research, the proof of solution mapping is local Lipschitz continuous, which is one big improvement. Second, based on the well-posedness result, we find a unique optimal control and optimal solution for the controlled system with the quadratic cost functional. Moreover, we establish the sufficient and necessary optimality condition of an optimal control by means of the optimal control theory, not limited to the necessary condition, which is another major novelty of this paper. We also discuss the optimality conditions corresponding to two physical meaningful distributed observation cases.

Adaptive effort investment in cognitive and physical tasks: a neurocomputational model

PubMed Central

Verguts, Tom; Vassena, Eliana; Silvetti, Massimo

2015-01-01

Despite its importance in everyday life, the computational nature of effort investment remains poorly understood. We propose an effort model obtained from optimality considerations, and a neurocomputational approximation to the optimal model. Both are couched in the framework of reinforcement learning. It is shown that choosing when or when not to exert effort can be adaptively learned, depending on rewards, costs, and task difficulty. In the neurocomputational model, the limbic loop comprising anterior cingulate cortex (ACC) and ventral striatum in the basal ganglia allocates effort to cortical stimulus-action pathways whenever this is valuable. We demonstrate that the model approximates optimality. Next, we consider two hallmark effects from the cognitive control literature, namely proportion congruency and sequential congruency effects. It is shown that the model exerts both proactive and reactive cognitive control. Then, we simulate two physical effort tasks. In line with empirical work, impairing the model's dopaminergic pathway leads to apathetic behavior. Thus, we conceptually unify the exertion of cognitive and physical effort, studied across a variety of literatures (e.g., motivation and cognitive control) and animal species. PMID:25805978

Minimum energy control for a two-compartment neuron to extracellular electric fields

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Li, Hui-Yan; Wei, Xi-Le; Deng, Bin

2016-11-01

The energy optimization of extracellular electric field (EF) stimulus for a neuron is considered in this paper. We employ the optimal control theory to design a low energy EF input for a reduced two-compartment model. It works by driving the neuron to closely track a prescriptive spike train. A cost function is introduced to balance the contradictory objectives, i.e., tracking errors and EF stimulus energy. By using the calculus of variations, we transform the minimization of cost function to a six-dimensional two-point boundary value problem (BVP). Through solving the obtained BVP in the cases of three fundamental bifurcations, it is shown that the control method is able to provide an optimal EF stimulus of reduced energy for the neuron to effectively track a prescriptive spike train. Further, the feasibility of the adopted method is interpreted from the point of view of the biophysical basis of spike initiation. These investigations are conducive to designing stimulating dose for extracellular neural stimulation, which are also helpful to interpret the effects of extracellular field on neural activity.

Research on Heat Dissipation of Electric Vehicle Based on Safety Architecture Optimization

NASA Astrophysics Data System (ADS)

Zhou, Chao; Guo, Yajuan; Huang, Wei; Jiang, Haitao; Wu, Liwei

2017-10-01

In order to solve the problem of excessive temperature in the discharge process of lithium-ion battery and the temperature difference between batteries, a heat dissipation of electric vehicle based on safety architecture optimization is designed. The simulation is used to optimize the temperature field of the heat dissipation of the battery. A reasonable heat dissipation control scheme is formulated to achieve heat dissipation requirements. The results show that the ideal working temperature range of the lithium ion battery is 20?∼45?, and the temperature difference between the batteries should be controlled within 5?. A cooling fan is arranged at the original air outlet of the battery model, and the two cooling fans work in turn to realize the reciprocating flow. The temperature difference is controlled within 5? to ensure the good temperature uniformity between the batteries of the electric vehicle. Based on the above finding, it is concluded that the heat dissipation design for electric vehicle batteries is safe and effective, which is the most effective methods to ensure battery life and vehicle safety.

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

PubMed

Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

2015-08-01

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Optimal Audiovisual Integration in the Ventriloquism Effect But Pervasive Deficits in Unisensory Spatial Localization in Amblyopia.

PubMed

Richards, Michael D; Goltz, Herbert C; Wong, Agnes M F

2018-01-01

Classically understood as a deficit in spatial vision, amblyopia is increasingly recognized to also impair audiovisual multisensory processing. Studies to date, however, have not determined whether the audiovisual abnormalities reflect a failure of multisensory integration, or an optimal strategy in the face of unisensory impairment. We use the ventriloquism effect and the maximum-likelihood estimation (MLE) model of optimal integration to investigate integration of audiovisual spatial information in amblyopia. Participants with unilateral amblyopia (n = 14; mean age 28.8 years; 7 anisometropic, 3 strabismic, 4 mixed mechanism) and visually normal controls (n = 16, mean age 29.2 years) localized brief unimodal auditory, unimodal visual, and bimodal (audiovisual) stimuli during binocular viewing using a location discrimination task. A subset of bimodal trials involved the ventriloquism effect, an illusion in which auditory and visual stimuli originating from different locations are perceived as originating from a single location. Localization precision and bias were determined by psychometric curve fitting, and the observed parameters were compared with predictions from the MLE model. Spatial localization precision was significantly reduced in the amblyopia group compared with the control group for unimodal visual, unimodal auditory, and bimodal stimuli. Analyses of localization precision and bias for bimodal stimuli showed no significant deviations from the MLE model in either the amblyopia group or the control group. Despite pervasive deficits in localization precision for visual, auditory, and audiovisual stimuli, audiovisual integration remains intact and optimal in unilateral amblyopia.

Design of multi-energy Helds coupling testing system of vertical axis wind power system

NASA Astrophysics Data System (ADS)

Chen, Q.; Yang, Z. X.; Li, G. S.; Song, L.; Ma, C.

2016-08-01

The conversion efficiency of wind energy is the focus of researches and concerns as one of the renewable energy. The present methods of enhancing the conversion efficiency are mostly improving the wind rotor structure, optimizing the generator parameters and energy storage controller and so on. Because the conversion process involves in energy conversion of multi-energy fields such as wind energy, mechanical energy and electrical energy, the coupling effect between them will influence the overall conversion efficiency. In this paper, using system integration analysis technology, a testing system based on multi-energy field coupling (MEFC) of vertical axis wind power system is proposed. When the maximum efficiency of wind rotor is satisfied, it can match to the generator function parameters according to the output performance of wind rotor. The voltage controller can transform the unstable electric power to the battery on the basis of optimizing the parameters such as charging times, charging voltage. Through the communication connection and regulation of the upper computer system (UCS), it can make the coupling parameters configure to an optimal state, and it improves the overall conversion efficiency. This method can test the whole wind turbine (WT) performance systematically and evaluate the design parameters effectively. It not only provides a testing method for system structure design and parameter optimization of wind rotor, generator and voltage controller, but also provides a new testing method for the whole performance optimization of vertical axis wind energy conversion system (WECS).

Pressure-Aware Control Layer Optimization for Flow-Based Microfluidic Biochips.

PubMed

Wang, Qin; Xu, Yue; Zuo, Shiliang; Yao, Hailong; Ho, Tsung-Yi; Li, Bing; Schlichtmann, Ulf; Cai, Yici

2017-12-01

Flow-based microfluidic biochips are attracting increasing attention with successful biomedical applications. One critical issue with flow-based microfluidic biochips is the large number of microvalves that require peripheral control pins. Even using the broadcasting addressing scheme, i.e., one control pin controls multiple microvalves simultaneously, thousands of microvalves would still require hundreds of control prins, which is unrealistic. To address this critical challenge in control scalability, the control-layer multiplexer is introduced to effectively reduce the number of control pins into log scale of the number of microvalves. There are two practical design issues with the control-layer multiplexer: (1) the reliability issue caused by the frequent control-valve switching, and (2) the pressure degradation problem caused by the control-valve switching without pressure refreshing from the pressure source. This paper addresses these two design issues by the proposed Hamming-distance-based switching sequence optimization method and the XOR-based pressure refreshing method. Simulation results demonstrate the effectiveness and efficiency of the proposed methods with an average 77.2% (maximum 89.6%) improvement in total pressure refreshing cost, and an average 88.5% (maximum 90.0%) improvement in pressure deviation.

Optimal Path Determination for Flying Vehicle to Search an Object

NASA Astrophysics Data System (ADS)

Heru Tjahjana, R.; Heri Soelistyo U, R.; Ratnasari, L.; Irawanto, B.

2018-01-01

In this paper, a method to determine optimal path for flying vehicle to search an object is proposed. Background of the paper is controlling air vehicle to search an object. Optimal path determination is one of the most popular problem in optimization. This paper describe model of control design for a flying vehicle to search an object, and focus on the optimal path that used to search an object. In this paper, optimal control model is used to control flying vehicle to make the vehicle move in optimal path. If the vehicle move in optimal path, then the path to reach the searched object also optimal. The cost Functional is one of the most important things in optimal control design, in this paper the cost functional make the air vehicle can move as soon as possible to reach the object. The axis reference of flying vehicle uses N-E-D (North-East-Down) coordinate system. The result of this paper are the theorems which say that the cost functional make the control optimal and make the vehicle move in optimal path are proved analytically. The other result of this paper also shows the cost functional which used is convex. The convexity of the cost functional is use for guarantee the existence of optimal control. This paper also expose some simulations to show an optimal path for flying vehicle to search an object. The optimization method which used to find the optimal control and optimal path vehicle in this paper is Pontryagin Minimum Principle.

Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

PubMed Central

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-01-01

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

PubMed

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-10-30

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

PubMed

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Multivariable optimization of liquid rocket engines using particle swarm algorithms

NASA Astrophysics Data System (ADS)

Jones, Daniel Ray

Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.

Fuel cells, batteries and super-capacitors stand-alone power systems management using optimal/flatness based-control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benaouadj, M.; Aboubou, A.; Bahri, M.

2016-07-25

In this work, an optimal control (under constraints) based on the Pontryagin’s maximum principle is used to optimally manage energy flows in a basic PEM (Proton Exchange Membrane) fuel cells system associated to lithium-ion batteries and supercapacitors through a common DC bus having a voltage to stabilize using the differential flatness approach. The adaptation of voltage levels between different sources and load is ensured by use of three DC-DC converters, one boost connected to the PEM fuel cells, while the two others are buck/boost and connected to the lithiumion batteries and supercapacitors. The aim of this paper is to developmore » an energy management strategy that is able to satisfy the following objectives: Impose the power requested by a habitat (representing the load) according to a proposed daily consumption profile, Keep fuel cells working at optimal power delivery conditions, Maintain constant voltage across the common DC bus, Stabilize the batteries voltage and stored quantity of charge at desired values given by the optimal control. Results obtained under MATLAB/Simulink environment prove that the cited objectives are satisfied, validating then, effectiveness and complementarity between the optimal and flatness concepts proposed for energy management. Note that this study is currently in experimentally validation within MSE Laboratory.« less

Mean Field Type Control with Congestion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Achdou, Yves, E-mail: achdou@ljll.univ-paris-diderot.fr; Laurière, Mathieu

2016-06-15

We analyze some systems of partial differential equations arising in the theory of mean field type control with congestion effects. We look for weak solutions. Our main result is the existence and uniqueness of suitably defined weak solutions, which are characterized as the optima of two optimal control problems in duality.

Optimal control of vancomycin-resistant enterococci using preventive care and treatment of infections.

PubMed

Lowden, Jonathan; Miller Neilan, Rachael; Yahdi, Mohammed

2014-03-01

The rising prevalence of vancomycin-resistant enterococci (VRE) is a major health problem in intensive care units (ICU) because of its association with increased mortality and high health care costs. We present a mathematical framework for determining cost-effective strategies for prevention and treatment of VRE in the ICU. A system of five ordinary differential equations describes the movement of ICU patients in and out of five VRE-related states. Two control variables representing the prevention and treatment of VRE are incorporated into the system. The basic reproductive number is derived and calculated for different levels of the two controls. An optimal control problem is formulated to minimize VRE-related deaths and costs associated with prevention and treatment controls over a finite time period. Numerical solutions illustrate optimal single and dual allocations of the controls for various cost values. Results show that preventive care has the greatest impact in reducing the basic reproductive number, while treatment of VRE infections has the most impact on reducing VRE-related deaths. Copyright © 2014 Elsevier Inc. All rights reserved.

Application of polynomial control to design a robust oscillation-damping controller in a multimachine power system.

PubMed

Hasanvand, Hamed; Mozafari, Babak; Arvan, Mohammad R; Amraee, Turaj

2015-11-01

This paper addresses the application of a static Var compensator (SVC) to improve the damping of interarea oscillations. Optimal location and size of SVC are defined using bifurcation and modal analysis to satisfy its primary application. Furthermore, the best-input signal for damping controller is selected using Hankel singular values and right half plane-zeros. The proposed approach is aimed to design a robust PI controller based on interval plants and Kharitonov's theorem. The objective here is to determine the stability region to attain robust stability, the desired phase margin, gain margin, and bandwidth. The intersection of the resulting stability regions yields the set of kp-ki parameters. In addition, optimal multiobjective design of PI controller using particle swarm optimization (PSO) algorithm is presented. The effectiveness of the suggested controllers in damping of local and interarea oscillation modes of a multimachine power system, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear time domain simulation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Control of rolling contact fatigue on premium rails in revenue service.

DOT National Transportation Integrated Search

2014-11-01

Effective rail maintenance strategies are : essential for controlling rolling contact fatigue : (RCF) and reducing wear of rails under heavy : axle load (HAL) operations. In an effort to : optimize rail maintenance strategies in revenue : service, Tr...

Parametric optimal control of uncertain systems under an optimistic value criterion

NASA Astrophysics Data System (ADS)

Li, Bo; Zhu, Yuanguo

2018-01-01

It is well known that the optimal control of a linear quadratic model is characterized by the solution of a Riccati differential equation. In many cases, the corresponding Riccati differential equation cannot be solved exactly such that the optimal feedback control may be a complex time-oriented function. In this article, a parametric optimal control problem of an uncertain linear quadratic model under an optimistic value criterion is considered for simplifying the expression of optimal control. Based on the equation of optimality for the uncertain optimal control problem, an approximation method is presented to solve it. As an application, a two-spool turbofan engine optimal control problem is given to show the utility of the proposed model and the efficiency of the presented approximation method.

Research on the optimization of quota design in real estate

NASA Astrophysics Data System (ADS)

Sun, Chunling; Ma, Susu; Zhong, Weichao

2017-11-01

Quota design is one of the effective methods of cost control in real estate development project and widely used in the current real estate development project to control the engineering construction cost, but quota design have many deficiencies in design process. For this purpose, this paper put forward a method to achieve investment control of real estate development project, which combine quota design and value engineering(VE) at the stage of design. Specifically, it’s an optimizing for the structure of quota design. At first, determine the design limits by investment estimate value, then using VE to carry on initial allocation of design limits and gain the functional target cost, finally, consider the whole life cycle cost (LCC) and operational problem in practical application to finish complex correction for the functional target cost. The improved process can control the project cost more effectively. It not only can control investment in a certain range, but also make the project realize maximum value within investment.

Robust Neighboring Optimal Guidance for the Advanced Launch System

NASA Technical Reports Server (NTRS)

Hull, David G.

1993-01-01

In recent years, optimization has become an engineering tool through the availability of numerous successful nonlinear programming codes. Optimal control problems are converted into parameter optimization (nonlinear programming) problems by assuming the control to be piecewise linear, making the unknowns the nodes or junction points of the linear control segments. Once the optimal piecewise linear control (suboptimal) control is known, a guidance law for operating near the suboptimal path is the neighboring optimal piecewise linear control (neighboring suboptimal control). Research conducted under this grant has been directed toward the investigation of neighboring suboptimal control as a guidance scheme for an advanced launch system.

Cartesian control of redundant robots

NASA Technical Reports Server (NTRS)

Colbaugh, R.; Glass, K.

1989-01-01

A Cartesian-space position/force controller is presented for redundant robots. The proposed control structure partitions the control problem into a nonredundant position/force trajectory tracking problem and a redundant mapping problem between Cartesian control input F is a set member of the set R(sup m) and robot actuator torque T is a set member of the set R(sup n) (for redundant robots, m is less than n). The underdetermined nature of the F yields T map is exploited so that the robot redundancy is utilized to improve the dynamic response of the robot. This dynamically optimal F yields T map is implemented locally (in time) so that it is computationally efficient for on-line control; however, it is shown that the map possesses globally optimal characteristics. Additionally, it is demonstrated that the dynamically optimal F yields T map can be modified so that the robot redundancy is used to simultaneously improve the dynamic response and realize any specified kinematic performance objective (e.g., manipulability maximization or obstacle avoidance). Computer simulation results are given for a four degree of freedom planar redundant robot under Cartesian control, and demonstrate that position/force trajectory tracking and effective redundancy utilization can be achieved simultaneously with the proposed controller.

Xylem transport models optimize effectiveness of systemic insecticide applications for controlling hemlock woolly adelgid (Adelges tsugae)

Treesearch

Chelcy R. Ford; Barbara C. Reynolds; James Vose

2010-01-01

The hemlock woolly adelgid (HWA, Adelges tsugae Annand) is causing widespread decline and mortality of eastern hemlock trees (Tsuga Canadensis (L.) Carr.). Stem injection of insecticide is widely used as a control measure, but its effectiveness depends on individual tree hydraulic characteristics. Recent work has shown that eastern...

The Effects of a Concurrent Task on Human Optimization and Self Control

ERIC Educational Resources Information Center

Reed, Phil; Thompson, Caitlin; Osborne, Lisa A.; McHugh, Louise

2011-01-01

Memory deficits have been shown to hamper decision making in a number of populations. In two experiments, participants were required to select one of three alternatives that varied in reinforcer amount and delay, and the effect of a concurrent task on a behavioral choice task that involved making either an impulsive, self-controlled, or optimal…

An efficient constraint to account for mistuning effects in the optimal design of engine rotors

NASA Technical Reports Server (NTRS)

Murthy, Durbha V.; Pierre, Christophe; Ottarsson, Gisli

1992-01-01

Blade-to-blade differences in structural properties, unavoidable in practice due to manufacturing tolerances, can have significant influence on the vibratory response of engine rotor blade. Accounting for these differences, also known as mistuning, in design and in optimization procedures is generally not possible. This note presents an easily calculated constraint that can be used in design and optimization procedures to control the sensitivity of final designs to mistuning.

Development of an external ceramic insulation for the space shuttle orbiter. Part 2: Optimization

NASA Technical Reports Server (NTRS)

Tanzilli, R. A. (Editor)

1973-01-01

The basic insulation improvement study concentrated upon evaluating variables which could result in significant near-term gains in mechanical behavior and insulation effectiveness of the baseline system. The approaches undertaken included: evaluation of small diameter fibers, optimization of binder: slurry characteristics, evaluation of techniques for controlling fiber orientation, optimization of firing cycle, and the evaluation of methods for improving insulation efficiency. A detailed discussion of these basic insulation improvement studies is presented.

Results of differential elevon/aileron deflection for lateral control optimization and elevon hinge moment investigations on an 0.015-scale model (49-0) of the space shuttle orbiter in the NASA/Langley Research Center 8 foot TPT (OA116)

NASA Technical Reports Server (NTRS)

Lindsey, A. I.; Milam, M. D.

1974-01-01

Aerodynamic investigations were conducted in a transonic pressure tunnel on an 0.015 scale model of the space shuttle orbiter. Major test objectives were to determine: (1) transonic differential elevon/aileron lateral control optimization; (2) transonic elevon hinge moments; (3) transonic effects of the baseline 6 inch elevon/elevon and elevon/fuselage gaps; and (4) transonic effects of the short OMS pods. Six-component aerodynamic force and moment, and elevon hinge moment data, were recorded over an angle-of-attack range form -2 to +22 degrees.

Using spatial information about recurrence risk for robust optimization of dose-painting prescription functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bender, Edward T.

Purpose: To develop a robust method for deriving dose-painting prescription functions using spatial information about the risk for disease recurrence. Methods: Spatial distributions of radiobiological model parameters are derived from distributions of recurrence risk after uniform irradiation. These model parameters are then used to derive optimal dose-painting prescription functions given a constant mean biologically effective dose. Results: An estimate for the optimal dose distribution can be derived based on spatial information about recurrence risk. Dose painting based on imaging markers that are moderately or poorly correlated with recurrence risk are predicted to potentially result in inferior disease control when comparedmore » the same mean biologically effective dose delivered uniformly. A robust optimization approach may partially mitigate this issue. Conclusions: The methods described here can be used to derive an estimate for a robust, patient-specific prescription function for use in dose painting. Two approximate scaling relationships were observed: First, the optimal choice for the maximum dose differential when using either a linear or two-compartment prescription function is proportional to R, where R is the Pearson correlation coefficient between a given imaging marker and recurrence risk after uniform irradiation. Second, the predicted maximum possible gain in tumor control probability for any robust optimization technique is nearly proportional to the square of R.« less

Feedback System Control Optimized Electrospinning for Fabrication of an Excellent Superhydrophobic Surface.

PubMed

Yang, Jian; Liu, Chuangui; Wang, Boqian; Ding, Xianting

2017-10-13

Superhydrophobic surface, as a promising micro/nano material, has tremendous applications in biological and artificial investigations. The electrohydrodynamics (EHD) technique is a versatile and effective method for fabricating micro- to nanoscale fibers and particles from a variety of materials. A combination of critical parameters, such as mass fraction, ratio of N, N-Dimethylformamide (DMF) to Tetrahydrofuran (THF), inner diameter of needle, feed rate, receiving distance, applied voltage as well as temperature, during electrospinning process, to determine the morphology of the electrospun membranes, which in turn determines the superhydrophobic property of the membrane. In this study, we applied a recently developed feedback system control (FSC) scheme for rapid identification of the optimal combination of these controllable parameters to fabricate superhydrophobic surface by one-step electrospinning method without any further modification. Within five rounds of experiments by testing totally forty-six data points, FSC scheme successfully identified an optimal parameter combination that generated electrospun membranes with a static water contact angle of 160 degrees or larger. Scanning electron microscope (SEM) imaging indicates that the FSC optimized surface attains unique morphology. The optimized setup introduced here therefore serves as a one-step, straightforward, and economic approach to fabricate superhydrophobic surface with electrospinning approach.

Building-to-Grid Integration through Commercial Building Portfolios Participating in Energy and Frequency Regulation Markets

NASA Astrophysics Data System (ADS)

Pavlak, Gregory S.

Building energy use is a significant contributing factor to growing worldwide energy demands. In pursuit of a sustainable energy future, commercial building operations must be intelligently integrated with the electric system to increase efficiency and enable renewable generation. Toward this end, a model-based methodology was developed to estimate the capability of commercial buildings to participate in frequency regulation ancillary service markets. This methodology was integrated into a supervisory model predictive controller to optimize building operation in consideration of energy prices, demand charges, and ancillary service revenue. The supervisory control problem was extended to building portfolios to evaluate opportunities for synergistic effect among multiple, centrally-optimized buildings. Simulation studies performed showed that the multi-market optimization was able to determine appropriate opportunities for buildings to provide frequency regulation. Total savings were increased by up to thirteen percentage points, depending on the simulation case. Furthermore, optimizing buildings as a portfolio achieved up to seven additional percentage points of savings, depending on the case. Enhanced energy and cost savings opportunities were observed by taking the novel perspective of optimizing building portfolios in multiple grid markets, motivating future pursuits of advanced control paradigms that enable a more intelligent electric grid.

Effects of external loads on balance control during upright stance: experimental results and model-based predictions.

PubMed

Qu, Xingda; Nussbaum, Maury A

2009-01-01

The purpose of this study was to identify the effects of external loads on balance control during upright stance, and to examine the ability of a new balance control model to predict these effects. External loads were applied to 12 young, healthy participants, and effects on balance control were characterized by center-of-pressure (COP) based measures. Several loading conditions were studied, involving combinations of load mass (10% and 20% of individual body mass) and height (at or 15% of stature above the whole-body COM). A balance control model based on an optimal control strategy was used to predict COP time series. It was assumed that a given individual would adopt the same neural optimal control mechanisms, identified in a no-load condition, under diverse external loading conditions. With the application of external loads, COP mean velocity in the anterior-posterior direction and RMS distance in the medial-lateral direction increased 8.1% and 10.4%, respectively. Predicted COP mean velocity and RMS distance in the anterior-posterior direction also increased with external loading, by 11.1% and 2.9%, respectively. Both experimental COP data and model-based predictions provided the same general conclusion, that application of larger external loads and loads more superior to the whole body center of mass lead to less effective postural control and perhaps a greater risk of loss of balance or falls. Thus, it can be concluded that the assumption about consistency in control mechanisms was partially supported, and it is the mechanical changes induced by external loads that primarily affect balance control.

Preserving electron spin coherence in solids by optimal dynamical decoupling.

PubMed

Du, Jiangfeng; Rong, Xing; Zhao, Nan; Wang, Ya; Yang, Jiahui; Liu, R B

2009-10-29

To exploit the quantum coherence of electron spins in solids in future technologies such as quantum computing, it is first vital to overcome the problem of spin decoherence due to their coupling to the noisy environment. Dynamical decoupling, which uses stroboscopic spin flips to give an average coupling to the environment that is effectively zero, is a particularly promising strategy for combating decoherence because it can be naturally integrated with other desired functionalities, such as quantum gates. Errors are inevitably introduced in each spin flip, so it is desirable to minimize the number of control pulses used to realize dynamical decoupling having a given level of precision. Such optimal dynamical decoupling sequences have recently been explored. The experimental realization of optimal dynamical decoupling in solid-state systems, however, remains elusive. Here we use pulsed electron paramagnetic resonance to demonstrate experimentally optimal dynamical decoupling for preserving electron spin coherence in irradiated malonic acid crystals at temperatures from 50 K to room temperature. Using a seven-pulse optimal dynamical decoupling sequence, we prolonged the spin coherence time to about 30 mus; it would otherwise be about 0.04 mus without control or 6.2 mus under one-pulse control. By comparing experiments with microscopic theories, we have identified the relevant electron spin decoherence mechanisms in the solid. Optimal dynamical decoupling may be applied to other solid-state systems, such as diamonds with nitrogen-vacancy centres, and so lay the foundation for quantum coherence control of spins in solids at room temperature.

The Effects of Optimism and Gratitude on Physical Activity, Biomarkers, and Readmissions Following an Acute Coronary Syndrome: The Gratitude Research in Acute Coronary Events (GRACE) Study

PubMed Central

Huffman, Jeff C.; Beale, Eleanor E.; Celano, Christopher M.; Beach, Scott R.; Belcher, Arianna M.; Moore, Shannon V.; Suarez, Laura; Motiwala, Shweta R.; Gandhi, Parul U.; Gaggin, Hanna; Januzzi, James L.

2015-01-01

Background Positive psychological constructs, such as optimism, are associated with beneficial health outcomes. However, no study has separately examined the effects of multiple positive psychological constructs on behavioral, biological, and clinical outcomes after an acute coronary syndrome (ACS). Accordingly, we aimed to investigate associations of baseline optimism and gratitude with subsequent physical activity, prognostic biomarkers, and cardiac rehospitalizations in post-ACS patients. Methods and Results Participants were enrolled during admission for ACS and underwent assessments at baseline (2 weeks post-ACS) and follow-up (6 months later). Associations between baseline positive psychological constructs and subsequent physical activity/biomarkers were analyzed using multivariable linear regression. Associations between baseline positive constructs and 6-month rehospitalizations were assessed via multivariable Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessments. Baseline optimism was significantly associated with greater physical activity at 6 months (n=153; β=102.5; 95% confidence interval [13.6-191.5]; p=.024), controlling for baseline activity and sociodemographic, medical, and negative psychological covariates. Baseline optimism was also associated with lower rates of cardiac readmissions at 6 months (N=164), controlling for age, gender, and medical comorbidity (hazard ratio=.92; 95% confidence interval [.86-.98]; p=.006). There were no significant relationships between optimism and biomarkers. Gratitude was minimally associated with post-ACS outcomes. Conclusions Post-ACS optimism, but not gratitude, was prospectively and independently associated with superior physical activity and fewer cardiac readmissions. Whether interventions that target optimism can successfully increase optimism or improve cardiovascular outcomes in post-ACS patients is not yet known, but can be tested in future studies. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01709669. PMID:26646818

Effects of Optimism and Gratitude on Physical Activity, Biomarkers, and Readmissions After an Acute Coronary Syndrome: The Gratitude Research in Acute Coronary Events Study.

PubMed

Huffman, Jeff C; Beale, Eleanor E; Celano, Christopher M; Beach, Scott R; Belcher, Arianna M; Moore, Shannon V; Suarez, Laura; Motiwala, Shweta R; Gandhi, Parul U; Gaggin, Hanna K; Januzzi, James L

2016-01-01

Positive psychological constructs, such as optimism, are associated with beneficial health outcomes. However, no study has separately examined the effects of multiple positive psychological constructs on behavioral, biological, and clinical outcomes after an acute coronary syndrome (ACS). Accordingly, we aimed to investigate associations of baseline optimism and gratitude with subsequent physical activity, prognostic biomarkers, and cardiac rehospitalizations in post-ACS patients. Participants were enrolled during admission for ACS and underwent assessments at baseline (2 weeks post-ACS) and follow-up (6 months later). Associations between baseline positive psychological constructs and subsequent physical activity/biomarkers were analyzed using multivariable linear regression. Associations between baseline positive constructs and 6-month rehospitalizations were assessed via multivariable Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessments. Baseline optimism was significantly associated with greater physical activity at 6 months (n=153; β=102.5; 95% confidence interval, 13.6-191.5; P=0.024), controlling for baseline activity and sociodemographic, medical, and negative psychological covariates. Baseline optimism was also associated with lower rates of cardiac readmissions at 6 months (n=164), controlling for age, sex, and medical comorbidity (hazard ratio, 0.92; 95% confidence interval, [0.86-0.98]; P=0.006). There were no significant relationships between optimism and biomarkers. Gratitude was minimally associated with post-ACS outcomes. Post-ACS optimism, but not gratitude, was prospectively and independently associated with superior physical activity and fewer cardiac readmissions. Whether interventions that target optimism can successfully increase optimism or improve cardiovascular outcomes in post-ACS patients is not yet known, but can be tested in future studies. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01709669. © 2015 American Heart Association, Inc.

An optimization-based approach for facility energy management with uncertainties, and, Power portfolio optimization in deregulated electricity markets with risk management

NASA Astrophysics Data System (ADS)

Xu, Jun

Topic 1. An Optimization-Based Approach for Facility Energy Management with Uncertainties. Effective energy management for facilities is becoming increasingly important in view of the rising energy costs, the government mandate on the reduction of energy consumption, and the human comfort requirements. This part of dissertation presents a daily energy management formulation and the corresponding solution methodology for HVAC systems. The problem is to minimize the energy and demand costs through the control of HVAC units while satisfying human comfort, system dynamics, load limit constraints, and other requirements. The problem is difficult in view of the fact that the system is nonlinear, time-varying, building-dependent, and uncertain; and that the direct control of a large number of HVAC components is difficult. In this work, HVAC setpoints are the control variables developed on top of a Direct Digital Control (DDC) system. A method that combines Lagrangian relaxation, neural networks, stochastic dynamic programming, and heuristics is developed to predict the system dynamics and uncontrollable load, and to optimize the setpoints. Numerical testing and prototype implementation results show that our method can effectively reduce total costs, manage uncertainties, and shed the load, is computationally efficient. Furthermore, it is significantly better than existing methods. Topic 2. Power Portfolio Optimization in Deregulated Electricity Markets with Risk Management. In a deregulated electric power system, multiple markets of different time scales exist with various power supply instruments. A load serving entity (LSE) has multiple choices from these instruments to meet its load obligations. In view of the large amount of power involved, the complex market structure, risks in such volatile markets, stringent constraints to be satisfied, and the long time horizon, a power portfolio optimization problem is of critical importance but difficulty for an LSE to serve the load, maximize its profit, and manage risks. In this topic, a mid-term power portfolio optimization problem with risk management is presented. Key instruments are considered, risk terms based on semi-variances of spot market transactions are introduced, and penalties on load obligation violations are added to the objective function to improve algorithm convergence and constraint satisfaction. To overcome the inseparability of the resulting problem, a surrogate optimization framework is developed enabling a decomposition and coordination approach. Numerical testing results show that our method effectively provides decisions for various instruments to maximize profit, manage risks, and is computationally efficient.

Taking side effects into account for HIV medication.

PubMed

Costanza, Vicente; Rivadeneira, Pablo S; Biafore, Federico L; D'Attellis, Carlos E

2010-09-01

A control-theoretic approach to the problem of designing "low-side-effects" therapies for HIV patients based on highly active drugs is substantiated here. The evolution of side effects during treatment is modeled by an extra differential equation coupled to the dynamics of virions, healthy T-cells, and infected ones. The new equation reflects the dependence of collateral damages on the amount of each dose administered to the patient and on the evolution of the viral load detected by periodical blood analysis. The cost objective accounts for recommended bounds on healthy cells and virions, and also penalizes the appearance of collateral morbidities caused by the medication. The optimization problem is solved by a hybrid dynamic programming scheme that adhere to discrete-time observation and control actions, but by maintaining the continuous-time setup for predicting states and side effects. The resulting optimal strategies employ less drugs than those prescribed by previous optimization studies, but maintaining high doses at the beginning and the end of each period of six months. If an inverse discount rate is applied to favor early actions, and under a mild penalization of the final viral load, then the optimal doses are found to be high at the beginning and decrease afterward, thus causing an apparent stabilization of the main variables. But in this case, the final viral load turns higher than acceptable.

Closed-Loop Optimal Control Implementations for Space Applications

DTIC Science & Technology

2016-12-01

analyses of a series of optimal control problems, several real- time optimal control algorithms are developed that continuously adapt to feedback on the...through the analyses of a series of optimal control problems, several real- time optimal control algorithms are developed that continuously adapt to...information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering

Quantum optimal control with automatic differentiation using graphics processors

NASA Astrophysics Data System (ADS)

Leung, Nelson; Abdelhafez, Mohamed; Chakram, Srivatsan; Naik, Ravi; Groszkowski, Peter; Koch, Jens; Schuster, David

We implement quantum optimal control based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them into the optimization process with ease. We will describe efficient techniques to optimally control weakly anharmonic systems that are commonly encountered in circuit QED, including coupled superconducting transmon qubits and multi-cavity circuit QED systems. These systems allow for a rich variety of control schemes that quantum optimal control is well suited to explore.

Promoter library-based module combination (PLMC) technology for optimization of threonine biosynthesis in Corynebacterium glutamicum.

PubMed

Wei, Liang; Xu, Ning; Wang, Yiran; Zhou, Wei; Han, Guoqiang; Ma, Yanhe; Liu, Jun

2018-05-01

Due to the lack of efficient control elements and tools, the fine-tuning of gene expression in the multi-gene metabolic pathways is still a great challenge for engineering microbial cell factories, especially for the important industrial microorganism Corynebacterium glutamicum. In this study, the promoter library-based module combination (PLMC) technology was developed to efficiently optimize the expression of genes in C. glutamicum. A random promoter library was designed to contain the putative - 10 (NNTANANT) and - 35 (NNGNCN) consensus motifs, and refined through a three-step screening procedure to achieve numerous genetic control elements with different strength levels, including fluorescence-activated cell sorting (FACS) screening, agar plate screening, and 96-well plate screening. Multiple conventional strategies were employed for further precise characterizations of the promoter library, such as real-time quantitative PCR, sodium dodecyl sulfate polyacrylamide gel electrophoresis, FACS analysis, and the lacZ reporter system. These results suggested that the established promoter elements effectively regulated gene expression and showed varying strengths over a wide range. Subsequently, a multi-module combination technology was created based on the efficient promoter elements for combination and optimization of modules in the multi-gene pathways. Using this technology, the threonine biosynthesis pathway was reconstructed and optimized by predictable tuning expression of five modules in C. glutamicum. The threonine titer of the optimized strain was significantly improved to 12.8 g/L, an approximate 6.1-fold higher than that of the control strain. Overall, the PLMC technology presented in this study provides a rapid and effective method for combination and optimization of multi-gene pathways in C. glutamicum.

Optimal control analysis of malaria-schistosomiasis co-infection dynamics.

PubMed

Okosun, Kazeem Oare; Smith, Robert

2017-04-01

This paper presents a mathematical model for malaria--schistosomiasis co-infection in order to investigate their synergistic relationship in the presence of treatment. We first analyse the single infection steady states, then investigate the existence and stability of equilibria and then calculate the basic reproduction numbers. Both the single-infection models and the co-infection model exhibit backward bifurcations. We carrying out a sensitivity analysis of the co-infection model and show that schistosomiasis infection may not be associated with an increased risk of malaria. Conversely, malaria infection may be associated with an increased risk of schistosomiasis. Furthermore, we found that effective treatment and prevention of schistosomiasis infection would also assist in the effective control and eradication of malaria. Finally, we apply Pontryagin's Maximum Principle to the model in order to determine optimal strategies for control of both diseases.

Sliding Mode Control of a Slewing Flexible Beam

NASA Technical Reports Server (NTRS)

Wilson, David G.; Parker, Gordon G.; Starr, Gregory P.; Robinett, Rush D., III

1997-01-01

An output feedback sliding mode controller (SMC) is proposed to minimize the effects of vibrations of slewing flexible manipulators. A spline trajectory is used to generate ideal position and velocity commands. Constrained nonlinear optimization techniques are used to both calibrate nonlinear models and determine optimized gains to produce a rest-to-rest, residual vibration-free maneuver. Vibration-free maneuvers are important for current and future NASA space missions. This study required the development of the nonlinear dynamic system equations of motion; robust control law design; numerical implementation; system identification; and verification using the Sandia National Laboratories flexible robot testbed. Results are shown for a slewing flexible beam.

Advanced in-duct sorbent injection for SO{sub 2} control. Topical report No. 2, Subtask 2.2: Design optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosenhoover, W.A.; Stouffer, M.R.; Withum, J.A.

1994-12-01

The objective of this research project is to develop second-generation duct injection technology as a cost-effective SO{sub 2} control option for the 1990 Clean Air Act Amendments. Research is focused on the Advanced Coolside process, which has shown the potential for achieving the performance targets of 90% SO{sub 2} removal and 60% sorbent utilization. In Subtask 2.2, Design Optimization, process improvement was sought by optimizing sorbent recycle and by optimizing process equipment for reduced cost. The pilot plant recycle testing showed that 90% SO{sub 2} removal could be achieved at sorbent utilizations up to 75%. This testing also showed thatmore » the Advanced Coolside process has the potential to achieve very high removal efficiency (90 to greater than 99%). Two alternative contactor designs were developed, tested and optimized through pilot plant testing; the improved designs will reduce process costs significantly, while maintaining operability and performance essential to the process. Also, sorbent recycle handling equipment was optimized to reduce cost.« less

Dynamic optimal control of homeostasis: an integrative system approach for modeling of the central nitrogen metabolism in Saccharomyces cerevisiae.

PubMed

van Riel, N A; Giuseppin, M L; Verrips, C T

2000-01-01

The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the source of the cellular amino acids and proteins, including flavors and potentially valuable biomolecules; therefore, it is also of industrial interest. In the DOMC approach the cell is regarded as an optimally controlled system. Given the metabolic genotype, the cell faces a control problem to maintain an optimal flux distribution in a changing environment. The regulation is based on strategies and balances feedback control of homeostasis and feedforward regulation for adaptation. The DOMC approach is an integrative, holistic approach, not based on mechanistic descriptions and (therefore) not biased by the variation present in biochemical and molecular biological data. It is an effective tool to structure the rapidly increasing amount of data on the function of genes and pathways. The DOMC model is used successfully to predict the responses of pulses of ammonia and glutamine to nitrogen-limited continuous cultures of a wild-type strain and a glutamine synthetase-negative mutant. The simulation results are validated with experimental data.

Chopped random-basis quantum optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caneva, Tommaso; Calarco, Tommaso; Montangero, Simone

2011-08-15

In this work, we describe in detail the chopped random basis (CRAB) optimal control technique recently introduced to optimize time-dependent density matrix renormalization group simulations [P. Doria, T. Calarco, and S. Montangero, Phys. Rev. Lett. 106, 190501 (2011)]. Here, we study the efficiency of this control technique in optimizing different quantum processes and we show that in the considered cases we obtain results equivalent to those obtained via different optimal control methods while using less resources. We propose the CRAB optimization as a general and versatile optimal control technique.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Simulation and optimal control of wind-farm boundary layers

NASA Astrophysics Data System (ADS)

Meyers, Johan; Goit, Jay

2014-05-01

In large wind farms, the effect of turbine wakes, and their interaction leads to a reduction in farm efficiency, with power generated by turbines in a farm being lower than that of a lone-standing turbine by up to 50%. In very large wind farms or `deep arrays', this efficiency loss is related to interaction of the wind farms with the planetary boundary layer, leading to lower wind speeds at turbine level. Moreover, for these cases it has been demonstrated both in simulations and wind-tunnel experiments that the wind-farm energy extraction is dominated by the vertical turbulent transport of kinetic energy from higher regions in the boundary layer towards the turbine level. In the current study, we investigate the use of optimal control techniques combined with Large-Eddy Simulations (LES) of wind-farm boundary layer interaction for the increase of total energy extraction in very large `infinite' wind farms. We consider the individual wind turbines as flow actuators, whose energy extraction can be dynamically regulated in time so as to optimally influence the turbulent flow field, maximizing the wind farm power. For the simulation of wind-farm boundary layers we use large-eddy simulations in combination with actuator-disk and actuator-line representations of wind turbines. Simulations are performed in our in-house pseudo-spectral code SP-Wind that combines Fourier-spectral discretization in horizontal directions with a fourth-order finite-volume approach in the vertical direction. For the optimal control study, we consider the dynamic control of turbine-thrust coefficients in an actuator-disk model. They represent the effect of turbine blades that can actively pitch in time, changing the lift- and drag coefficients of the turbine blades. Optimal model-predictive control (or optimal receding horizon control) is used, where the model simply consists of the full LES equations, and the time horizon is approximately 280 seconds. The optimization is performed using a nonlinear conjugate gradient method, and the gradients are calculated by solving the adjoint LES equations. We find that the extracted farm power increases by approximately 20% when using optimal model-predictive control. However, the increased power output is also responsible for an increase in turbulent dissipation, and a deceleration of the boundary layer. Further investigating the energy balances in the boundary layer, it is observed that this deceleration is mainly occurring in the outer layer as a result of higher turbulent energy fluxes towards the turbines. In a second optimization case, we penalize boundary-layer deceleration, and find an increase of energy extraction of approximately 10%. In this case, increased energy extraction is balanced by a reduction in of turbulent dissipation in the boundary layer. J.M. acknowledges support from the European Research Council (FP7-Ideas, grant no. 306471). Simulations were performed on the computing infrastructure of the VSC Flemish Supercomputer Center, funded by the Hercules Foundation and the Flemish Government.

Direct Digital Control Study.

DTIC Science & Technology

1985-02-01

Deck - Cold Deck Reset Reheat Coil Reset Steam Boiler Optimization [lot Water Outside Air Reset Chiller Optimization Chiller Water Temperature Reset...with programming techniques for each type of installed DDC in order to effect changes in operating setpoints and application programs. *Communication...can be changed without recailbration of instrumentation devices. Changes to the application software, operating setpoints and parameters require the

Optimizing Dynamical Network Structure for Pinning Control

NASA Astrophysics Data System (ADS)

Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

2016-04-01

Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

Environmental optimal control strategies based on plant canopy photosynthesis responses and greenhouse climate model

NASA Astrophysics Data System (ADS)

Deng, Lujuan; Xie, Songhe; Cui, Jiantao; Liu, Tao

2006-11-01

It is the essential goal of intelligent greenhouse environment optimal control to enhance income of cropper and energy save. There were some characteristics such as uncertainty, imprecision, nonlinear, strong coupling, bigger inertia and different time scale in greenhouse environment control system. So greenhouse environment optimal control was not easy and especially model-based optimal control method was more difficult. So the optimal control problem of plant environment in intelligent greenhouse was researched. Hierarchical greenhouse environment control system was constructed. In the first level data measuring was carried out and executive machine was controlled. Optimal setting points of climate controlled variable in greenhouse was calculated and chosen in the second level. Market analysis and planning were completed in third level. The problem of the optimal setting point was discussed in this paper. Firstly the model of plant canopy photosynthesis responses and the model of greenhouse climate model were constructed. Afterwards according to experience of the planting expert, in daytime the optimal goals were decided according to the most maximal photosynthesis rate principle. In nighttime on plant better growth conditions the optimal goals were decided by energy saving principle. Whereafter environment optimal control setting points were computed by GA. Compared the optimal result and recording data in real system, the method is reasonable and can achieve energy saving and the maximal photosynthesis rate in intelligent greenhouse

Optimal interpolation and the Kalman filter. [for analysis of numerical weather predictions

NASA Technical Reports Server (NTRS)

Cohn, S.; Isaacson, E.; Ghil, M.

1981-01-01

The estimation theory of stochastic-dynamic systems is described and used in a numerical study of optimal interpolation. The general form of data assimilation methods is reviewed. The Kalman-Bucy, KB filter, and optimal interpolation (OI) filters are examined for effectiveness in performance as gain matrices using a one-dimensional form of the shallow-water equations. Control runs in the numerical analyses were performed for a ten-day forecast in concert with the OI method. The effects of optimality, initialization, and assimilation were studied. It was found that correct initialization is necessary in order to localize errors, especially near boundary points. Also, the use of small forecast error growth rates over data-sparse areas was determined to offset inaccurate modeling of correlation functions near boundaries.

Web malware spread modelling and optimal control strategies

NASA Astrophysics Data System (ADS)

Liu, Wanping; Zhong, Shouming

2017-02-01

The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.

Web malware spread modelling and optimal control strategies.

PubMed

Liu, Wanping; Zhong, Shouming

2017-02-10

The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.

Web malware spread modelling and optimal control strategies

PubMed Central

Liu, Wanping; Zhong, Shouming

2017-01-01

The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice. PMID:28186203

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

PubMed Central

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-01-01

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm–neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS. PMID:29671822

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.

PubMed

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-04-19

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm⁻neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

A Control Model: Interpretation of Fitts' Law

NASA Technical Reports Server (NTRS)

Connelly, E. M.

1984-01-01

The analytical results for several models are given: a first order model where it is assumed that the hand velocity can be directly controlled, and a second order model where it is assumed that the hand acceleration can be directly controlled. Two different types of control-laws are investigated. One is linear function of the hand error and error rate; the other is the time-optimal control law. Results show that the first and second order models with the linear control-law produce a movement time (MT) function with the exact form of the Fitts' Law. The control-law interpretation implies that the effect of target width on MT must be a result of the vertical motion which elevates the hand from the starting point and drops it on the target at the target edge. The time optimal control law did not produce a movement-time formula simular to Fitt's Law.

Active vibration and noise control of vibro-acoustic system by using PID controller

NASA Astrophysics Data System (ADS)

Li, Yunlong; Wang, Xiaojun; Huang, Ren; Qiu, Zhiping

2015-07-01

Active control simulation of the acoustic and vibration response of a vibro-acoustic cavity of an airplane based on a PID controller is presented. A full numerical vibro-acoustic model is developed by using an Eulerian model, which is a coupled model based on the finite element formulation. The reduced order model, which is used to design the closed-loop control system, is obtained by the combination of modal expansion and variable substitution. Some physical experiments are made to validate and update the full-order and the reduced-order numerical models. Optimization of the actuator placement is employed in order to get an effective closed-loop control system. For the controller design, an iterative method is used to determine the optimal parameters of the PID controller. The process is illustrated by the design of an active noise and vibration control system for a cavity structure. The numerical and experimental results show that a PID-based active control system can effectively suppress the noise inside the cavity using a sound pressure signal as the controller input. It is also possible to control the noise by suppressing the vibration of the structure using the structural displacement signal as the controller input. For an airplane cavity structure, considering the issue of space-saving, the latter is more suitable.

Optimal intravenous infusion to decrease the haematocrit level in patient of DHF infection

NASA Astrophysics Data System (ADS)

Handayani, D.; Nuraini, N.; Saragih, R.; Wijaya, K. P.; Naiborhu, J.

2014-02-01

The optimal control of infusion model for Dengue Hemorrhagic Fever (DHF) infection is formulated here. The infusion model will be presented in form of haematocrit level. The input control aim to normalize the haematocrit level and is expressed as infusion volume on mL/day. The stability near the equilibrium points will be analyzed. Numerical simulation shows the dynamic of each infection compartments which gives a description of within-host dynamic of dengue virus. These results show particularly that infected compartments tend to be vanished in ±15days after the onset of the virus. In fact, without any control added, the haematocrit level will decrease but not up to the normal level. Therefore the effective haematocrit normalization should be done with the treatment control. Control treatment for a fixed time using a control input can bring haematocrit level to normal range 42-47%. The optimal control in this paper is divided into three cases, i.e. fixed end point, constrained input, and tracking haematocrit state. Each case shows different infection condition in human body. However, all cases require that the haematocrit level to be in normal range in fixed final time.

Long-term myocardial preservation: beneficial and additive effects of polarized arrest (Na+-channel blockade), Na+/H+-exchange inhibition, and Na+/K+/2Cl- -cotransport inhibition combined with calcium desensitization.

PubMed

Snabaitis, A K; Chambers, D

1999-11-27

Polarized arrest, induced by tetrodotoxin (TTX) at an optimal concentration of 22 micromol/L, has been shown to reduce ionic imbalance and improve myocardial preservation compared with hyperkalemic (depolarized) arrest. Additional pharmacologic manipulation of ionic changes (involving inhibition of Na+ influx by the Na+/H+ exchanger [HOE694] and Na+/K+/2Cl- cotransporter [furosemide], and calcium desensitization [BDM]) may further improve long-term preservation. In this study, we (i) established optimal concentrations of each drug, (ii) determined additive effects of optimal concentrations of each drug and (iii) compared our optimal preservation solution to an established depolarizing cardioplegia (St Thomas' Hospital solution No 2: STH2) used during long-term hypothermic storage for clinical transplantation. The isolated working rat heart, perfused with Krebs Henseleit (KH) buffer was used; cardiac function was measured after 20 min aerobic working mode perfusion. The hearts (n=6/group) were arrested with a 2 ml infusion (for 30 sec) of the polarizing (control) solution (22 micromol/L TTX in KH) or control+drug and subjected to 5 hr or 8 hr of storage at 7.5 degrees C in the arresting solution. Postischemic function during reperfusion was measured (expressed as percentage of preischemic function). Dose-response studies established optimal concentrations of HOE694 (10 micromol/L), furosemide (1.0 micromol/L) and BDM (30 mmol/L) in the polarizing (control) solution. Sequential addition to the control solution (Group I) of optimal concentrations of HOE694 (Group II), furosemide (Group III), and BDM (Group IV) were compared with STH2 (Group V); postischemic recovery of aortic flow was 29+/-7%, 49+/-6%*, 56+/-2%*, 76+/-3%*, and 25+/-6%, respectively (*P<0.05 vs. I and V). Creatine kinase leakage was lowest, and myocardial ATP content was highest in Group IV. A polarizing preservation solution (KH+TTX) containing HOE694, furosemide, and BDM significantly enhanced long-term preservation compared with an optimized depolarizing solution (STH2) used clinically for long-term donor heart preservation.

Gradient Optimization for Analytic conTrols - GOAT

NASA Astrophysics Data System (ADS)

Assémat, Elie; Machnes, Shai; Tannor, David; Wilhelm-Mauch, Frank

Quantum optimal control becomes a necessary step in a number of studies in the quantum realm. Recent experimental advances showed that superconducting qubits can be controlled with an impressive accuracy. However, most of the standard optimal control algorithms are not designed to manage such high accuracy. To tackle this issue, a novel quantum optimal control algorithm have been introduced: the Gradient Optimization for Analytic conTrols (GOAT). It avoids the piecewise constant approximation of the control pulse used by standard algorithms. This allows an efficient implementation of very high accuracy optimization. It also includes a novel method to compute the gradient that provides many advantages, e.g. the absence of backpropagation or the natural route to optimize the robustness of the control pulses. This talk will present the GOAT algorithm and a few applications to transmons systems.

On Per-Phase Topology Control and Switching in Emerging Distribution Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, Fei; Mousavi, Mirrasoul J.

This paper presents a new concept and approach for topology control and switching in distribution systems by extending the traditional circuit switching to laterals and single-phase loads. Voltage unbalance and other key performance indicators including voltage magnitudes, line loading, and energy losses are used to characterize and demonstrate the technical value of optimizing system topology on a per-phase basis in response to feeder conditions. The near-optimal per-phase topology control is defined as a series of hierarchical optimization problems. The proposed approach is respectively applied to IEEE 13-bus and 123-bus test systems for demonstration, which included the impact of integrating electricmore » vehicles (EVs) in the test circuit. It is concluded that the proposed approach can be effectively leveraged to improve voltage profiles with electric vehicles, the extent of which depends upon the performance of the base case without EVs.« less

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

PubMed

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Self-optimization and auto-stabilization of receiver in DPSK transmission system.

PubMed

Jang, Y S

2008-03-17

We propose a self-optimization and auto-stabilization method for a 1-bit DMZI in DPSK transmission. Using the characteristics of eye patterns, the optical frequency transmittance of a 1-bit DMZI is thermally controlled to maximize the power difference between the constructive and destructive output ports. Unlike other techniques, this control method can be realized without additional components, making it simple and cost effective. Experimental results show that error-free performance is maintained when the carrier optical frequency variation is approximately 10% of the data rate.

Optimal control in a model of malaria with differential susceptibility

NASA Astrophysics Data System (ADS)

Hincapié, Doracelly; Ospina, Juan

2014-06-01

A malaria model with differential susceptibility is analyzed using the optimal control technique. In the model the human population is classified as susceptible, infected and recovered. Susceptibility is assumed dependent on genetic, physiological, or social characteristics that vary between individuals. The model is described by a system of differential equations that relate the human and vector populations, so that the infection is transmitted to humans by vectors, and the infection is transmitted to vectors by humans. The model considered is analyzed using the optimal control method when the control consists in using of insecticide-treated nets and educational campaigns; and the optimality criterion is to minimize the number of infected humans, while keeping the cost as low as is possible. One first goal is to determine the effects of differential susceptibility in the proposed control mechanism; and the second goal is to determine the algebraic form of the basic reproductive number of the model. All computations are performed using computer algebra, specifically Maple. It is claimed that the analytical results obtained are important for the design and implementation of control measures for malaria. It is suggested some future investigations such as the application of the method to other vector-borne diseases such as dengue or yellow fever; and also it is suggested the possible application of free software of computer algebra like Maxima.

Finite element approximation of an optimal control problem for the von Karman equations

NASA Technical Reports Server (NTRS)

Hou, L. Steven; Turner, James C.

1994-01-01

This paper is concerned with optimal control problems for the von Karman equations with distributed controls. We first show that optimal solutions exist. We then show that Lagrange multipliers may be used to enforce the constraints and derive an optimality system from which optimal states and controls may be deduced. Finally we define finite element approximations of solutions for the optimality system and derive error estimates for the approximations.

Supercritical tests of a self-optimizing, variable-Camber wind tunnel model

NASA Technical Reports Server (NTRS)

Levinsky, E. S.; Palko, R. L.

1979-01-01

A testing procedure was used in a 16-foot Transonic Propulsion Wind Tunnel which leads to optimum wing airfoil sections without stopping the tunnel for model changes. Being experimental, the optimum shapes obtained incorporate various three-dimensional and nonlinear viscous and transonic effects not included in analytical optimization methods. The method is a closed-loop, computer-controlled, interactive procedure and employs a Self-Optimizing Flexible Technology wing semispan model that conformally adapts the airfoil section at two spanwise control stations to maximize or minimize various prescribed merit functions subject to both equality and inequality constraints. The model, which employed twelve independent hydraulic actuator systems and flexible skins, was also used for conventional testing. Although six of seven optimizations attempted were at least partially convergent, further improvements in model skin smoothness and hydraulic reliability are required to make the technique fully operational.

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. V.; Yerazunis, S. W.

1973-01-01

Problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars are reported. Problem areas include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis, terrain modeling and path selection; and chemical analysis of specimens. These tasks are summarized: vehicle model design, mathematical model of vehicle dynamics, experimental vehicle dynamics, obstacle negotiation, electrochemical controls, remote control, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, and chromatograph model evaluation and improvement.

Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input.

PubMed

Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

2017-09-01

In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimization Strategies for Sensor and Actuator Placement

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Kincaid, Rex K.

1999-01-01

This paper provides a survey of actuator and sensor placement problems from a wide range of engineering disciplines and a variety of applications. Combinatorial optimization methods are recommended as a means for identifying sets of actuators and sensors that maximize performance. Several sample applications from NASA Langley Research Center, such as active structural acoustic control, are covered in detail. Laboratory and flight tests of these applications indicate that actuator and sensor placement methods are effective and important. Lessons learned in solving these optimization problems can guide future research.

An express method for optimally tuning an analog controller with respect to integral quality criteria

NASA Astrophysics Data System (ADS)

Golinko, I. M.; Kovrigo, Yu. M.; Kubrak, A. I.

2014-03-01

An express method for optimally tuning analog PI and PID controllers is considered. An integral quality criterion with minimizing the control output is proposed for optimizing control systems. The suggested criterion differs from existing ones in that the control output applied to the technological process is taken into account in a correct manner, due to which it becomes possible to maximally reduce the expenditure of material and/or energy resources in performing control of industrial equipment sets. With control organized in such manner, smaller wear and longer service life of control devices are achieved. A unimodal nature of the proposed criterion for optimally tuning a controller is numerically demonstrated using the methods of optimization theory. A functional interrelation between the optimal controller parameters and dynamic properties of a controlled plant is numerically determined for a single-loop control system. The results obtained from simulation of transients in a control system carried out using the proposed and existing functional dependences are compared with each other. The proposed calculation formulas differ from the existing ones by a simple structure and highly accurate search for the optimal controller tuning parameters. The obtained calculation formulas are recommended for being used by specialists in automation for design and optimization of control systems.

Searching for quantum optimal controls under severe constraints

DOE PAGES

Riviello, Gregory; Tibbetts, Katharine Moore; Brif, Constantin; ...

2015-04-06

The success of quantum optimal control for both experimental and theoretical objectives is connected to the topology of the corresponding control landscapes, which are free from local traps if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is of full rank, and (3) there are no constraints on the control field. This paper investigates how the violation of assumption (3) affects gradient searches for globally optimal control fields. The satisfaction of assumptions (1) and (2) ensures that the control landscape lacks fundamental traps, butmore » certain control constraints can still prevent successful optimization of the objective. Using optimal control simulations, we show that the most severe field constraints are those that limit essential control resources, such as the number of control variables, the control duration, and the field strength. Proper management of these resources is an issue of great practical importance for optimization in the laboratory. For each resource, we show that constraints exceeding quantifiable limits can introduce artificial traps to the control landscape and prevent gradient searches from reaching a globally optimal solution. These results demonstrate that careful choice of relevant control parameters helps to eliminate artificial traps and facilitate successful optimization.« less

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

NASA Astrophysics Data System (ADS)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach are less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle

NASA Astrophysics Data System (ADS)

Cao, Haotian; Song, Xiaolin; Zhao, Song; Bao, Shan; Huang, Zhi

2017-08-01

Automated driving has received a broad of attentions from the academia and industry, since it is effective to greatly reduce the severity of potential traffic accidents and achieve the ultimate automobile safety and comfort. This paper presents an optimal model-based trajectory following architecture for highly automated vehicle in its driving tasks such as automated guidance or lane keeping, which includes a velocity-planning module, a steering controller and a velocity-tracking controller. The velocity-planning module considering the optimal time-consuming and passenger comforts simultaneously could generate a smooth velocity profile. The robust sliding mode control (SMC) steering controller with adaptive preview time strategy could not only track the target path well, but also avoid a big lateral acceleration occurred in its path-tracking progress due to a fuzzy-adaptive preview time mechanism introduced. In addition, an SMC controller with input-output linearisation method for velocity tracking is built and validated. Simulation results show this trajectory following architecture are effective and feasible for high automated driving vehicle, comparing with the Driver-in-the-Loop simulations performed by an experienced driver and novice driver, respectively. The simulation results demonstrate that the present trajectory following architecture could plan a satisfying longitudinal speed profile, track the target path well and safely when dealing with different road geometry structure, it ensures a good time efficiency and driving comfort simultaneously.

Effectiveness of cognitive behavioral therapy on health-related quality of life: An evaluation of therapies provided by trainee therapists.

PubMed

Henriksson, Sophie; Anclair, Malin; Hiltunen, Arto J

2016-06-01

The present study was carried out to examine the treatment effect of cognitive behavioral therapy provided by trainee therapists at a university clinic, focusing on health-related quality of life (HRQOL) optimism and symptoms. The study was conducted through a repeated measures design and included a treatment group (n = 21), which received cognitive behavioral therapy for an average of 10.7 therapy sessions and a control group (n = 14), that was put on a wait list for 8.6 weeks on average. After treatment, the treatment group improved significantly concerning general health (p = 0.028) and optimism (p = 0.027). In addition, clients improved in several areas within mental health and displayed some reduction in anxiety symptoms. Concurrently, the results also indicated some improvement within the control group, which may have been caused by the initial therapeutic contact, expectancy effects or spontaneous remission. The study concluded that cognitive behavioral therapy provided by trainee therapists may have a positive effect on areas within HRQOL and optimism. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

An assessment of discriminatory power of office blood pressure measurements in predicting optimal ambulatory blood pressure control in people with type 2 diabetes.

PubMed

Kengne, Andre Pascal; Libend, Christelle Nong; Dzudie, Anastase; Menanga, Alain; Dehayem, Mesmin Yefou; Kingue, Samuel; Sobngwi, Eugene

2014-01-01

Ambulatory blood pressure (BP) measurements (ABPM) predict health outcomes better than office BP, and are recommended for assessing BP control, particularly in high-risk patients. We assessed the performance of office BP in predicting optimal ambulatory BP control in sub-Saharan Africans with type 2 diabetes (T2DM). Participants were a random sample of 51 T2DM patients (25 men) drug-treated for hypertension, receiving care in a referral diabetes clinic in Yaounde, Cameroon. A quality control group included 46 non-diabetic individuals with hypertension. Targets for BP control were systolic (and diastolic) BP. Mean age of diabetic participants was 60 years (standard deviation: 10) and median duration of diabetes was 6 years (min-max: 0-29). Correlation coefficients between each office-based variable and the 24-h ABPM equivalent (diabetic vs. non-diabetic participants) were 0.571 and 0.601 for systolic (SBP), 0.520 and 0.539 for diastolic (DBP), 0.631 and 0.549 for pulse pressure (PP), and 0.522 and 0.583 for mean arterial pressure (MAP). The c-statistic for the prediction of optimal ambulatory control from office-BP in diabetic participants was 0.717 for SBP, 0.494 for DBP, 0.712 for PP, 0.582 for MAP, and 0.721 for either SBP + DBP or PP + MAP. Equivalents in diabetes-free participants were 0.805, 0.763, 0.695, 0.801 and 0.813. Office DBP was ineffective in discriminating optimal ambulatory BP control in diabetic patients, and did not improve predictions based on office SBP alone. Targeting ABPM to those T2DM patients who are already at optimal office-based SBP would likely be more cost effective in this setting.