Stable adaptive fuzzy control of nonlinear systems
Li-Xin Wang
1993-01-01
A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided,
DIRECT ADAPTIVE FUZZY CONTROL OF NONLINEAR SYSTEM CLASS WITH APPLICATIONS
H. Chekireb; M. Tadjine; D. Bouchara
2003-01-01
In this article we propose a direct adaptive fuzzy control method for MIMO nonlinear plant encountered mainly in robotics. The fuzzy adaptive law ensures the stability, convergence of the controlled outputs, and \\
Fuzzy adaptive vector control of induction motor drives
Emanuele Cerruto; Alfio Consoli; Angelo Raciti; Antonio Testa
1997-01-01
This paper deals with the design and experimental realization of a model reference adaptive control (MRAC) system for the speed control of indirect field-oriented (IFO) induction motor drives based on using fuzzy laws for the adaptive process and a neuro-fuzzy procedure to optimize the fuzzy rules. Variation of the rotor time constant is also accounted for by performing a fuzzy
NONLINEAR ADAPTIVE FUZZY CONTROL FOR HYDRAULIC ROBOTS
Chih-Fu Chang; Su-Chiun Wang; Li-Chen Fu
In this paper, a novel adaptive fuzzy controller (AFC) is proposed to deal with the control problem of a parallel robot consisting of a Stewart platform and hydraulic actuators. Specifically, only two signals are measured from the robot system, namely, the leg displacements through Linear Variable Differential Transformer (LVDT) and the current change with Hall sensor. To cope with the
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Stable adaptive fuzzy controllers with application to inverted pendulum tracking
Li-Xin Wang
1996-01-01
An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given-trajectory. In this paper, two adaptive fuzzy controllers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable
Low speed control of a DC motor driving a mechanical system with fuzzy adaptive compensation
Hyun, Dongyoon
1997-01-01
A fuzzy adaptive feedforward control scheme in conjunction with classical feedback control is proposed for the low speed control of DC motors driving mechanical systems in the presence of friction. In the fuzzy adaptive scheme, a fuzzy logic based...
Decomposed fuzzy systems and their application in direct adaptive fuzzy control.
Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang
2014-10-01
In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure. PMID:25222721
Variable universe adaptive fuzzy control on the quadruple inverted pendulum
Hongxing Li; Miao Zhihong; Wang Jiayin
2002-01-01
This paper focuses on the control problem of the quadruple inverted pendulum by variable universe adaptive fuzzy control.\\u000a First, the mathematical model on the quadruple inverted pendulum is described and its controllability is versified. Then,\\u000a an efficient controller on the quadruple inverted pendulum is designed by using variable universe adaptive fuzzy control theory.\\u000a Finally the simulation of the quadruple inverted
Adaptive neuro-fuzzy control of dynamical systems
Alok Kanti Deb; Alok Juyal
2011-01-01
In this paper, the an adaptive neuro-fuzzy control that combines the features of fuzzy sets and neural networks have been implemented and applied for the control of SISO and MIMO systems. Duffing forced oscillation system was considered as the SISO plant while the Twin Rotor laboratory set up that closely mimics helicopter dynamics was considered as the MIMO plant. The
Computation of Parametric Adaptive Fuzzy Controller for Wood Drying System
NASA Astrophysics Data System (ADS)
Situmorang, Zakarias; Wardoyo, Retantyo; Hartati, Sri; Istiyanto, Jazi Eko
2009-08-01
The paper reports the computation of parametric adaptive fuzzy controller for used to wood drying system. Parametric of adaptive fuzzy controller is control period system. Control period system is how long time need to hoist of temperature drying or humidity drying if the actuator in on-conditions. The parametric is implemented for control system of wood drying process at prototype chamber with solar is source of energy. The actuator of system is heater, damper and sprayer. From result of measurement, that data were doing to analysis statistic to have the parametric. Whenever the parametric want to implemented with mechanism adaptive. Membership Functions of variable control of system to became something is difficult to have effect to temperature and humidity drying. The result of implemented of adaptive fuzzy control is described in graphic typical. The control system is able to adapt change of humidity drying in system schedule of wood drying system.
ADAPTIVE FUZZY CONTROL FOR UNDERWATER HYDRAULIC MANIPULATORS
Leonardo Bittencourt Testi; Bruno Cardozo dos Santos; Max Suell Dutra
2004-01-01
Underwater hydraulic manipulators are usually systems hard to be modeled and present strong non-linearities in its dynamics behavior. These types of manipulators are operated, nowadays, in a master-slave configuration with simple control algorithms performing tasks in hazardous and unstructured environments. In such conditions only low accuracy simple tasks can be performed. This paper presents the application of a special fuzzy
Design of adaptive fuzzy controls based on natural control laws
M. S. Ju; D. L. Yang
1996-01-01
In the design of fuzzy controllers there is a need for standardizing the selection of rule table and the scaling factors. For example, in the design of self-organizing fuzzy controllers or the rule self-regulating fuzzy controllers, the rule table or the performance table is often derived either by trial and error or from the heuristics of an expert. Recently a
Adaptive fuzzy control for wind-diesel weak power systems
Riad B. Chedid; Sami H. Karaki; Chadi El-Chamali
2000-01-01
This work is concerned with the development of an adaptive fuzzy logic controller for a wind-diesel system composed of a stall regulated wind turbine with an induction generator connected to an AC busbar in parallel with a diesel generator set having a synchronous generator. In this work we propose to use an adaptive network based inference system (ANFIS) in order
Chia-Feng Juang; Chao-Hsin Hsu
2005-01-01
Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization
Murad Shibli
2006-01-01
This paper describes the implementation of a direct adaptive control of a nonlinear underactuated mechatronics system known as the Pendubot robot using fuzzy systems and neural networks. A PD fuzzy controller is employed to control the two links motion from the free hanging position to the vertical position (the swing-up controller). Then, an intelligent adaptive fuzzy radial Gaussian neural networks
Neural and Fuzzy Adaptive Control of Induction Motor Drives
Bensalem, Y. [Research Unit of Modelisation, Analyse, Command of Systems MACS (Tunisia); Sbita, L.; Abdelkrim, M. N. [6029 Universite High School of Engineering-Gabes-Tunisia (Tunisia)
2008-06-12
This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.
Design and Simulation of Adaptive Fuzzy Controller for Active Suspension System
Jianmin Sun; Qingmei Yang
2007-01-01
An adaptive fuzzy controller which fuzzy control rule table can be obtained with the numerical calculation is designed in order to improve vehicle comfort and road holding capability. There is no membership function choice of fuzzy subset for input and output of controller. The rectification factor is adjusted online according to adaptive method. The factor can be influenced by exciting
MIMO Indirect Adaptive Fuzzy Control of Induction Motors
Manal Wahba
2007-01-01
This paper presents a new adaptive fuzzy control technique applied to induction motors (IM). The control task of such motors is considered complicated by the fact that these motors have uncertain time-varying parameters and are subjected to unknown load disturbance. A nonlinear multi-input multi-output (MIMO) state feedback linearizing control is designed for the IM modeled in a stationary reference frame.
Rewinder tension control based on fuzzy adaptive PID algorithm
Chen Jingwen
2010-01-01
With the characteristics of non-linearity, large time delay, and strong disturbance, the web tension control is the main difficulty of the re-winder electrical control system. The conventional PID algorithm can not achieve good performance index when the re-winder is working under acceleration or deceleration. The new algorithm based on fuzzy control adaptive PID algorithm could solve this problem, this algorithm
Design of adaptive fuzzy controller for active suspension system
Jianmin Sun; Qingmei Yang
2004-01-01
With the nonlinearity of the road-vehicle system, an adjustable fuzzy control algorithm which fuzzy control rule table can be obtained with the numerical calculation is advanced. Because the algorithm can adjust the rectification factor of fuzzy controller with the least means squares (LMS) method, it not only can reflect the advantage of fuzzy logic in nonlinearity system but also can
Adaptive fuzzy sliding mode control of smart structures
NASA Astrophysics Data System (ADS)
Bessa, W. M.; de Paula, A. S.; Savi, M. A.
2013-09-01
Smart structures are usually designed with a stimulus-response mechanism to mimic the autoregulatory process of living systems. In this work, in order to simulate this natural and self-adjustable behavior, an adaptive fuzzy sliding mode controller is applied to a shape memory two-bar truss. This structural system exhibits both constitutive and geometrical nonlinearities presenting the snap-through behavior and chaotic dynamics. On this basis, a variable structure controller is employed for vibration suppression in the chaotic smart truss. The control scheme is primarily based on sliding mode methodology and enhanced by an adaptive fuzzy inference system to cope with modeling inaccuracies and external disturbances. The robustness of this approach against both structured and unstructured uncertainties enables the adoption of simple constitutive models for control purposes. The overall control system performance is evaluated by means of numerical simulations, promoting vibration reduction and avoiding snap-through behavior.
Fuzzy Adaptive Control for Four Wheels Steering of Construction Machinery
Ye Min; Jiao Shengjie; Yi Xiaogang
2010-01-01
To shorten the steer diameter and improve maneuverability flexibility during steering, four wheels steering system of construction machinery is proposed. The steering system is constructed by mechanic-electric-hydraulic assemblies. Its construction and running principle are depicted in detail. Considering the nonlinear and time-varying uncertainty of the steering system, the controller composing of a PID controller and a fuzzy adaptive supervisory tuner
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Composite adaptive fuzzy control for synchronizing generalized Lorenz systems
NASA Astrophysics Data System (ADS)
Pan, Yongping; Er, Meng Joo; Sun, Tairen
2012-06-01
This paper presents a methodology of asymptotically synchronizing two uncertain generalized Lorenz systems via a single continuous composite adaptive fuzzy controller (AFC). To facilitate controller design, the synchronization problem is transformed into the stabilization problem by feedback linearization. To achieve asymptotic tracking performance, a key property of the optimal fuzzy approximation error is exploited by the Mean Value Theorem. The composite AFC, which utilizes both tracking and modeling error feedbacks, is constructed by introducing a series-parallel identification model into an indirect AFC. It is proved that the closed-loop system achieves asymptotic stability under a sufficient gain condition. Furthermore, the proposed approach cannot only synchronize two different chaotic systems but also significantly reduce computational complexity and implemented cost. Simulation studies further demonstrate the effectiveness of the proposed approach.
Simple adaptive control by fuzzy boxes methodology
D. Stipanicev; Z. Torba
1998-01-01
“Simplicity is an advantage”, particularly in solving real-life problems. Control engineering is not the exception. Simple control algorithms capable of controlling complex systems are dreams of all control engineers. 30 years ago Michie and Chambers (1968) had introduced the control methodology with such properties. They called it the boxes methodology. On the other hand, these days we are witnessing the
Global Asymptotic Stabilization Using Adaptive Fuzzy PD Control.
Pan, Yongping; Yu, Haoyong; Sun, Tairen
2015-03-01
It is well-known that standard adaptive fuzzy control (AFC) can only guarantee uniformly ultimately bounded stability due to inherent fuzzy approximation errors (FAEs). This paper proves that standard AFC with proportional-derivative (PD) control can guarantee global asymptotic stabilization even in the presence of FAEs for a class of uncertain affine nonlinear systems. Variable-gain PD control is designed to globally stabilize the plant. An optimal FAE is shown to be bounded by the norm of the plant state vector multiplied by a globally invertible and nondecreasing function, which provides a pivotal property for stability analysis. Without discontinuous control compensation, the closed-loop system achieves global and partially asymptotic stability in the sense that all plant states converge to zero. Compared with previous adaptive approximation-based global/asymptotic stabilization approaches, the major advantage of our approach is that global stability and asymptotic stabilization are achieved concurrently by a much simpler control law. Illustrative examples have further verified the theoretical results. PMID:25122847
DIRECT FUZZY CONTROL FOR NONLINEAR SERVOMECHANISM USING ADAPTIVE TUNING ALGORITHM (INVITED PAPER)
RONG-JONG WAI; CHIH-MIN LIN; CHUN-FEI HSU
In this study an adaptive fuzzy control (AFC) system is proposed to control a nonlinear motor-mechanism coupling system, that is a toggle mechanism driven by a permanent magnet (PM) syn- chronous servo motor. In the proposed AFC system, a fuzzy controller is the main tracking controller that is used to mimic an ideal feedback linearization control law and a compensated
Suvadeep Banerjee; Ankush Chakrabarty; Sayan Maity; Amitava Chatterjee
2011-01-01
The present paper describes the development of an indirect adaptive fuzzy control scheme employing feedback linearizing technique. The scheme proposes the development of a fuzzy certainty equivalence controller for controlling non-linear plants. This controller is designed on the basis of plant parameters estimated online at each sampling instant using bacterial foraging optimization (BFO) technique, a stochastic optimization technique, popularly employed
M. B. Djukanovic; M. S. Calovic; B. V. Vesovic; D. J. Sobajic
1997-01-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients,
Torque-ripple minimization in switched reluctance motors using adaptive fuzzy control
S. Mir; M. E. Elbuluk; I. Husain
1999-01-01
An adaptive fuzzy control scheme for the torque-ripple minimization of switched reluctance machines is presented. The fuzzy parameters are initially chosen randomly and then adjusted to optimize the control. The controller produces smooth torque up to the motor base speed. The torque is generated over the maximum positive torque-producing region of a phase. This increases the torque density and avoids
NASA Astrophysics Data System (ADS)
Mousavi, Seyyed Hossein; Noroozi, Navid; Safavi, Ali Akbar; Ebadat, Afrooz
2011-09-01
This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems. The control signal is comprised of two parts. The first part arises from an adaptive fuzzy wave-net based controller that approximates the system structural uncertainties. The second part comes from a robust H? based controller that is used to attenuate the effect of function approximation error and disturbance. Moreover, a new self structuring algorithm is proposed to determine the location of basis functions. Simulation results are provided for a two DOF robot to show the effectiveness of the proposed method.
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.
Adaptive multimodal vibration suppression using fuzzy-based control with limited structural data
NASA Astrophysics Data System (ADS)
Makihara, Kanjuro; Kuroishi, Chikako; Fukunaga, Hisao
2013-07-01
We propose a novel fuzzy-based method of adaptive multimodal vibration suppression with limited structural data. The adaptive control consists of fuzzy inference and a semi-active switching approach. We demonstrate it to be applicable to multimodal vibration suppression for vibrating structures, where a single piezoelectric actuator suppresses two modal vibrations simultaneously. Our fuzzy-based semi-active control requires only the structural information of natural frequencies for real-time adaptive feedback, whereas common adaptive controls require highly precise structural models or complete equations of motion. We conduct experiments in semi-active vibration suppression using the proposed fuzzy-based control, and compare the suppression performance of our fuzzy-based approach with conventional controls. The experiments indicate that the proposed fuzzy-based control demonstrates good adaptability when experiencing sudden changes in disturbance excitation, and also demonstrates high suppression performance. The fuzzy-based control can adapt to a wide range of disturbance conditions, both within and outside the range of vibration excitations assumed when the controller is designed.
A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems
Han-Xiong Li; Shaocheng Tong
2003-01-01
A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system
Adaptive fuzzy-neural-network control for maglev transportation system.
Wai, Rong-Jong; Lee, Jeng-Dao
2008-01-01
A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. PMID:18269938
Fuzzy model reference learning control with modified adaptation mechanism
Otto Cerman; Petr Hussek
2011-01-01
In this paper different approach to adapta- tion mechanism for fuzzy model reference learning con- trol (FMRLC) method will be introduced. In compar- ison to original method the proposed procedure shows larger robustness to responses to initial conditions and various reference signals. The main idea consists in adaptation of the inverse model - an important part of FMRLC. Moreover, in
Zhang, Ying-Bi
1997-01-01
In this thesis, I propose a fuzzy adaptive connection admission control (CAC) approach for real-time applications in ATM-Based heterogeneous networks (ABHN) where ATM serves as a backbone that connects different LANs by interface devices. This type...
Fuzzy-adapted recursive sliding-mode controller design for a nuclear power plant control
Zhengyu Huang; Robert M. Edwards; Kwang Y. Lee
2004-01-01
In this paper, a multi-input multi-output fuzzy-adapted recursive sliding-mode controller (FARSMC) is designed for an advanced boiling water reactor (ABWR) nuclear power plant, to control reactor pressure, reactor water level and turbine power. The FARSMC is intended to replace the existing conventional controllers for the power range of 70% to 100% rated power. The controller has a recursive form that
Performance improvement of a microbial fuel cell based on adaptive fuzzy control.
Fan, Liping; Li, Chong; Boshnakov, Kosta
2014-05-01
Microbial fuel cells have been obtaining more and more attention with the associated abilities of continuous electrical power supply and wastewater treatment. Because of its complicated reaction mechanism and its inherent characteristics of time varying, uncertainty, strong coupling and nonlinearity, there are complex control challenges in microbial fuel cells. In this paper, an adaptive fuzzy control scheme is proposed for the microbial fuel cell system to achieve constant voltage output under different loads. A main fuzzy controller is used to track the set value, and an auxiliary fuzzy controller is applied to adjust the factors of the main controller. Simulation results show that the output voltage can track the given value well. The proposed adaptive fuzzy controller can give better steady-state behavior and faster response, and it improves the running performance of the microbial fuel cell. PMID:24816708
Adjustable Adaptive Fuzzy Attitude Control using Nonlinear SISO Structure of Satellite Dynamics
NASA Astrophysics Data System (ADS)
Moradi, Morteza; Esmaelzadeh, Reza; Ghasemi, Ali
This paper presents a method for three-dimensional attitude stabilization of a satellite. The pitch loop of the satellite is controlled by a momentum wheel; whereas the roll/yaw loops are stabilized using two magnetic torques along their respective axes. In order to design an efficient controller, the stability conditions are considered based on a nonlinear model of system. An adjustable adaptive fuzzy system is proposed as the method to design the controller. The span of membership functions are tuned using errors of fuzzy inputs with respect to their references. Results show that fuzzy sets cover all variations of fuzzy inputs and optimal fuzzy output is gained. The Lyapunov synthesis method is used to prove the stability of the closed-loop system. The efficiency of the controller in converging of the position error to close to zero is also shown using some numerical simulations.
Fuzzy control of magnetic bearings
NASA Technical Reports Server (NTRS)
Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.
1991-01-01
The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.
NASA Astrophysics Data System (ADS)
Wang, Li-Ming; Tang, Yong-Guang; Chai, Yong-Quan; Wu, Feng
2014-10-01
An adaptive fuzzy sliding mode strategy is developed for the generalized projective synchronization of a fractional-order chaotic system, where the slave system is not necessarily known in advance. Based on the designed adaptive update laws and the linear feedback method, the adaptive fuzzy sliding controllers are proposed via the fuzzy design, and the strength of the designed controllers can be adaptively adjusted according to the external disturbances. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled system are proved theoretically. Numerical simulations further support the theoretical results of the paper and demonstrate the efficiency of the proposed method. Moreover, it is revealed that the proposed method allows us to manipulate arbitrarily the response dynamics of the slave system by adjusting the desired scaling factor ?i and the desired translating factor ?i, which may be used in a channel-independent chaotic secure communication.
An Adaptive Fuzzy Algorithm for Domestic Hot Water Temperature Control of a Combi-Boiler
Christine M. Haissig; Michael Woessner
2000-01-01
This paper describes an innovative adaptive fuzzy control (AFC) algorithm for regulating the domestic hot water temperature of a combi-boiler or instantaneous hot water heater when using a domestic hot water flow rate sensor. The AFC automatically learns the feedforward relationship between the domestic hot water flow rate and the gas valve position and adapts to process changes such as
Adaptive control of a class of nonlinear systems with fuzzy logic
Chun-Yi Su; Yury Stepanenko
1994-01-01
An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm
Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system
Chaio-Shiung Chen; Wen-Liang Chen
1998-01-01
In this paper, a new robust adaptive control architecture is proposed for operation of an inverted-pendulum mechanical system. The architecture employs a fuzzy system to adaptively compensate for the plant nonlinearities and forces the inverted pendulum to track a prescribed reference model. When matching with the model occurs, the pendulum will be stabilized at an upright position and the cart
Controlling Discrete Time T-S Fuzzy Chaotic Systems via Adaptive Adjustment
NASA Astrophysics Data System (ADS)
Nian, Yibei; Zheng, Yongai
In order to overcome typical drawbacks of the OGY control, i.e. the long waiting time for control to be applied and the accessible turning system parameter in advance, this paper presents a new chaos control method based on Takagi- Sugeno (T-S) fuzzy model and adaptive adjustment. This method represents a chaotic system by linear models in different state space regions based on T-S fuzzy model and then stabilize the linear models in different state space regions by the adaptive adjustment mechanism. An example for the Henon map is given to demonstrate the effectiveness of the proposed method.
Design of sewage treatment system by applying fuzzy adaptive PID controller
NASA Astrophysics Data System (ADS)
Jin, Liang-Ping; Li, Hong-Chan
2013-03-01
In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.
Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos G.
2013-10-01
An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H? tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H? tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.
NASA Astrophysics Data System (ADS)
Phu, Do Xuan; Shah, Kruti; Choi, Seung-Bok
2014-06-01
This paper presents a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological (MR) fluid damper in order to validate the effectiveness of the control performance. An interval type 2 fuzzy model is built, and then combined with modified adaptive control to achieve the desired damping force. In the formulation of the new adaptive controller, an enhanced iterative algorithm is integrated with the fuzzy model to decrease the time of calculation (D Wu 2013 IEEE Trans. Fuzzy Syst. 21 80-99) and the control algorithm is synthesized based on the {{H}^{\\infty }} tracking technique. In addition, for the verification of good control performance of the proposed controller, a cylindrical MR damper which can be applied to the vibration control of a washing machine is designed and manufactured. For the operating fluid, a recently developed plate-like particle-based MR fluid is used instead of a conventional MR fluid featuring spherical particles. To highlight the control performance of the proposed controller, two existing adaptive fuzzy control algorithms proposed by other researchers are adopted and altered for a comparative study. It is demonstrated from both simulation and experiment that the proposed new adaptive controller shows better performance of damping force control in terms of response time and tracking accuracy than the existing approaches.
Self-adaptive fuzzy PID control for three-tank water
NASA Astrophysics Data System (ADS)
Ke, Zhao
2013-03-01
Three-tank water represents a typical plant with non-linearity and large time delay. By combining the linearization method for non-linear plant, PID control structure and fuzzy control based on T-S model, the self-adaptive fuzzy PID control of the three-tank water is devised. The strategy aims at improving the control performance of the three-tank water by weighing the membership function to produce PID parameters and making parameters vary steadily with the variation of water level . Simulation results show that the control strategy proposed in this paper is correct and effective.
R. A. F. Saleh; H. R. Bolton
2001-01-01
This paper describes the development and application of two different control schemes for stability enhancement of a superconducting generator (SCG). The findings of study of the system performance with an adaptive scheme and a fuzzy logic control scheme are presented and compared. In the first scheme, the stabilizing signal is based on the minimization of a modified version of a
Flatness-based embedded adaptive fuzzy control of spark ignited engines
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan
2014-10-01
The paper proposes a differential flatness theory-based adaptive fuzzy controller for spark-ignited (SI) engines. The system's dynamic model is considered to be completely unknown. By applying a change of variables (diffeomorphism) that is based on differential flatness theory the engine's dynamic model is written in the linear canonical (Brunovsky) form. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. These nonlinear terms are approximated with the use of neuro-fuzzy networks while a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. Moreover, using Lyapunov stability analysis it is shown that the adaptive fuzzy control scheme succeeds H? tracking performance, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.
Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems] [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering] [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control] [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)] [Electric Power Research Inst., Palo Alto, CA (United States)
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
Huang, Shi-Jing; Lin, Lih-Chang
2004-08-01
This paper presents a dynamic output feedback control with adaptive rotor-imbalance compensation based on an analytical Takagi-Sugeno fuzzy model for complex nonlinear magnetic bearing systems with rotor eccentricity. The rotor mass-imbalance effect is considered with a linear in the parameter approximator. Through the robust analysis for disturbance rejection, the control law can be synthesized in terms of linear matrix inequalities. Based on the suggested fuzzy output feedback design, the controller may be much easier to implement than conventional nonlinear controllers. Simulation validations show that the proposed robust fuzzy control law can suppress the rotor imbalance-induced vibration and has excellent capability for high-speed tracking and levitation control. PMID:15462450
Guolin Che; Hua Lai
2010-01-01
The control of humidity in process of tobacco first baking is non-linear time-variant and inertial, it is disturbed by some factors in production. It is difficult to achieve a good control effects by traditional control methods. Adaptive fuzzy control based on variable universe has advantages such as high precision, fast response and strong flexibility. Because humidity control in first baking
Patricia Melin; Oscar Castillo
2003-01-01
We describe in this paper adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks. First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A specific non-linear plant
Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan
2014-10-01
In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H? tracking performance.
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method. PMID:23924413
Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems.
Shahriari kahkeshi, Maryam; Sheikholeslam, Farid; Zekri, Maryam
2013-05-01
This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed. PMID:23453235
NASA Astrophysics Data System (ADS)
Yang, Yueneng; Wu, Jie; Zheng, Wei
2013-04-01
This paper presents a novel approach for station-keeping control of a stratospheric airship platform in the presence of parametric uncertainty and external disturbance. First, conceptual design of the stratospheric airship platform is introduced, including the target mission, configuration, energy sources, propeller and payload. Second, the dynamics model of the airship platform is presented, and the mathematical model of its horizontal motion is derived. Third, a fuzzy adaptive backstepping control approach is proposed to develop the station-keeping control system for the simplified horizontal motion. The backstepping controller is designed assuming that the airship model is accurately known, and a fuzzy adaptive algorithm is used to approximate the uncertainty of the airship model. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control approach.
Lin, F J; Wai, R J; Lin, H H
1999-01-01
In this study an adaptive fuzzy-neural-network controller (AFNNC) is proposed to control a rotary traveling wave-type ultrasonic motor (USM) drive system. The USM is derived by a newly designed, high frequency, two-phase voltage source inverter using two inductances and two capacitances (LLCC) resonant technique. Then, because the dynamic characteristics of the USM are complicated and the motor parameters are time varying, an AFNNC is proposed to control the rotor position of the USM. In the proposed controller, the USM drive system is identified by a fuzzy-neural-network identifier (FNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the FNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNI and the optimal learning rate of the adaptive controller. In addition, the effectiveness of the adaptive fuzzy-neural-network (AFNN) controlled USM drive system is demonstrated by some experimental results. PMID:18238472
Li, Yongming; Tong, Shaocheng; Li, Tieshan
2014-11-25
In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial--parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial--parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach. PMID:25438335
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
NASA Astrophysics Data System (ADS)
Wade, Robert L.; Walker, Gregory W.
1996-05-01
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.
Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks
NASA Astrophysics Data System (ADS)
Wu, Zhengping; Wu, Hao
With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.
A. Halvaei Niasar; H. Moghbelli; A. Vahedi
2006-01-01
Principle of a new adaptive neuro-fuzzy inference system (ANFIS) with supervisory learning algorithm is introduced and is used to regulate the speed of a four-switch, three-phase inverter (FSTPI) brushless DC (BLDC) drive. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of drive's performance, instead of well-known back propagation learning method, a fuzzy based supervisory learning algorithm
Combining genetic algorithms and Lyapunov-based adaptation for online design of fuzzy controllers.
Giordano, Vincenzo; Naso, David; Turchiano, Biagio
2006-10-01
This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark. PMID:17036817
Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains
Yohanes Kristianto; Petri Helo; Jianxin Jiao; Maqsood Sandhu
2012-01-01
â–º Fuzzy Vendor Managed Inventory (VMI) control as controller of supply chain inventory. â–º VMI control for mitigating the rationing and gaming and order batching. â–º Fuzzy VMI enables inventory allocation across supply chains. â–º Fuzzy VMI control is good for applying in flexible manufacturing rather than in single line manufacturing.
NASA Astrophysics Data System (ADS)
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method. PMID:25273215
Towards autonomous fuzzy control
NASA Technical Reports Server (NTRS)
Shenoi, Sujeet; Ramer, Arthur
1993-01-01
The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous self-organizing controllers employing system-independent control meta-knowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control actions give rise to adequate performance characteristics but could rapidly and autonomously initiate real-time adaptation whenever its performance degrades. Such an autonomous fuzzy controller would have immense practical value. It could accommodate individual variations in system characteristics and also compensate for degradations in system characteristics caused by wear and tear. It could also potentially deal with black-box systems and control scenarios. On-going research in autonomous fuzzy control is reported. The ultimate research objective is to develop robust and relatively inexpensive autonomous fuzzy control hardware suitable for use in real time environments.
Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods
Bisset, K.R.; Kelsey, R.L.
1992-06-01
This paper describes the graphical simulation of a traffic environment. The environment includes streets leading to an intersection, the intersection, vehicle traffic, and signal lights in the intersection controlled by different methods. The simulation allows for the study of parameters affecting traffic environments and the study of different control strategies for traffic signal lights, including conventional, fuzzy, and adaptive control methods. Realistic traffic environments are simulated including a cross intersection, with one or more lanes of traffic in each direction, with and without turn lanes. Vehicle traffic patterns are a mixture of cars going straight and making right or left turns. The free velocities of vehicles follow a normal distribution with a mean of the ``posted`` speed limit. Actual velocities depend on such factors as the proximity and velocity of surrounding traffic, approaches to intersections, and human response time. The simulation proves the be a useful tool for evaluating controller methods. Preliminary results show that larger quantities of traffic are ``handled`` by fuzzy control methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzy control versus conventional control.
Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods
Bisset, K.R.; Kelsey, R.L.
1992-01-01
This paper describes the graphical simulation of a traffic environment. The environment includes streets leading to an intersection, the intersection, vehicle traffic, and signal lights in the intersection controlled by different methods. The simulation allows for the study of parameters affecting traffic environments and the study of different control strategies for traffic signal lights, including conventional, fuzzy, and adaptive control methods. Realistic traffic environments are simulated including a cross intersection, with one or more lanes of traffic in each direction, with and without turn lanes. Vehicle traffic patterns are a mixture of cars going straight and making right or left turns. The free velocities of vehicles follow a normal distribution with a mean of the posted'' speed limit. Actual velocities depend on such factors as the proximity and velocity of surrounding traffic, approaches to intersections, and human response time. The simulation proves the be a useful tool for evaluating controller methods. Preliminary results show that larger quantities of traffic are handled'' by fuzzy control methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzy control versus conventional control.
Neuro-fuzzy modeling and control
JYH-SHING ROGER JANG; Chuen-Tsai Sun
1995-01-01
Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce
Automation of a portable extracorporeal circulatory support system with adaptive fuzzy controllers.
Mendoza García, A; Krane, M; Baumgartner, B; Sprunk, N; Schreiber, U; Eichhorn, S; Lange, R; Knoll, A
2014-08-01
The presented work relates to the procedure followed for the automation of a portable extracorporeal circulatory support system. Such a device may help increase the chances of survival after suffering from cardiogenic shock outside the hospital, additionally a controller can provide of optimal organ perfusion, while reducing the workload of the operator. Animal experiments were carried out for the acquisition of haemodynamic behaviour of the body under extracorporeal circulation. A mathematical model was constructed based on the experimental data, including a cardiovascular model, gas exchange and the administration of medication. As the base of the controller fuzzy logic was used allowing the easy integration of knowledge from trained perfusionists, an adaptive mechanism was included to adapt to the patient's individual response. Initial simulations show the effectiveness of the controller and the improvements of perfusion after adaptation. PMID:24894032
Arun Khosla; Shakti Kumar; K. K. Aggarwal
2003-01-01
ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates
Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems.
Chen, C L Philip; Liu, Yan-Jun; Wen, Guo-Xing
2014-05-01
This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm. PMID:24132033
NASA Astrophysics Data System (ADS)
Qiu, Zhi-cheng; Wang, Bin; Zhang, Xian-min; Han, Jian-da
2013-04-01
This study presents a novel translating piezoelectric flexible manipulator driven by a rodless cylinder. Simultaneous positioning control and vibration suppression of the flexible manipulator is accomplished by using a hybrid driving scheme composed of the pneumatic cylinder and a piezoelectric actuator. Pulse code modulation (PCM) method is utilized for the cylinder. First, the system dynamics model is derived, and its standard multiple input multiple output (MIMO) state-space representation is provided. Second, a composite proportional derivative (PD) control algorithms and a direct adaptive fuzzy control method are designed for the MIMO system. Also, a time delay compensation algorithm, bandstop and low-pass filters are utilized, under consideration of the control hysteresis and the caused high-frequency modal vibration due to the long stroke of the cylinder, gas compression and nonlinear factors of the pneumatic system. The convergence of the closed loop system is analyzed. Finally, experimental apparatus is constructed and experiments are conducted. The effectiveness of the designed controllers and the hybrid driving scheme is verified through simulation and experimental comparison studies. The numerical simulation and experimental results demonstrate that the proposed system scheme of employing the pneumatic drive and piezoelectric actuator can suppress the vibration and achieve the desired positioning location simultaneously. Furthermore, the adopted adaptive fuzzy control algorithms can significantly enhance the control performance.
Chang, Yeong-Hwa; Chan, Wei-Shou
2014-02-01
This paper presents a new robust adaptive control method for a class of nonlinear systems subject to uncertainties. The proposed approach is based on an adaptive dynamic surface control, where the system uncertainties are approximately modeled by interval type-2 fuzzy neural networks. In this paper, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all error signals are shown to be uniformly ultimately bounded. In addition to simulations, the proposed method is applied to a real ball-and-beam system for performance evaluations. To highlight the system robustness, different initial settings of ball-and-beam parameters are considered. Simulation and experimental results indicate that the proposed control scheme has superior responses, compared to conventional dynamic surface control. PMID:23757550
Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang
2015-01-01
This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes-the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC-were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes. PMID:25815450
Hierarchical fuzzy force control for industrial robots
Shih-Tin Lin; Ang-Kiong Huang
1998-01-01
In this paper, we present a hierarchical force control framework consisting of a high-level control system based on fuzzy logic and the existing motion control system of a manipulator in the low level. In order to adapt various contact conditions, an adaptable fuzzy force control scheme has been proposed to improve the performance. The ability of the adaptable force control
A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes.
Savran, Aydogan; Kahraman, Gokalp
2014-03-01
We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities. PMID:24140160
Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter
2012-07-01
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047
Fuzzy Neural Network Control of AMT Clutch in Starting Phase
Chen Ran; Sun Dongye
2008-01-01
A fuzzy controller of automated mechanical transmission (AMT) clutch in starting phase is developed based on driverpsilas experiences. Focused on the problem that ordinary fuzzy controller design has a lot of subjective factors and the fuzzy controller is difficult to optimize, the fuzzy control model of the AMT clutch was optimized based on adaptive-network-based fuzzy inference system (ANFIS) algorithm and
Fuzzy logic base extremum seeking control system
Lev Gurvich
2004-01-01
The paper presents a fuzzy logic base seeking control system intended for objects with an extremum (peak) point. The system includes a perturbation signal generator and a fuzzy logic controller for changing the object's input coordinates. In addition, there is a perturbation signal parameters improvement adaptation subsystem. This subsystem also contains a fuzzy logic controller. There are recommendations for choosing
Lian, Kuang-Yow; Chiu, Chian-Song; Liu, P
2002-01-01
We present a semi-decentralized adaptive fuzzy control scheme for cooperative multirobot systems to achieve H(infinity) performance in motion and internal force tracking. First, we reformulate the overall system dynamics into a fully actuated system with constraints. To cope with both parametric and nonparametric uncertainties, the controller for each robot consists of two parts: 1) model-based adaptive controller; and 2) adaptive fuzzy logic controller (FLC). The model-based adaptive controller handles the nominal dynamics which results in both zero motion and internal force errors for a pure parametric uncertain system. The FLC part handles the unstructured dynamics and external disturbances. An H(infinity) tracking problem defined by a novel performance criterion is given and solved in the sequel. Hence, a robust controller satisfying the disturbance attenuation is derived being simple and singularity-free. Asymptotic convergence is obtained when the fuzzy approximation error is bounded with finite energy. Maintaining the same results, the proposed controller is further simplified for easier implementation. Finally, the numerical simulation results for two cooperative planar robots transporting an object illustrate the expected performance. PMID:18238126
Based on interval type-2 adaptive fuzzy H? tracking controller for SISO time-delay nonlinear systems
NASA Astrophysics Data System (ADS)
Lin, Tsung-Chih; Roopaei, Mehdi
2010-12-01
In this article, based on the adaptive interval type-2 fuzzy logic, by adjusting weights, centers and widths of proposed fuzzy neural network (FNN), the modeling errors can be eliminated for a class of SISO time-delay nonlinear systems. The proposed scheme has the advantage that can guarantee the H? tracking performance to attenuate the lumped uncertainties caused by the unmodelled dynamics, the approximation error and the external disturbances. Moreover, the stability analysis of the proposed control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded and arbitrary small attenuation level. The simulation results are demonstrated to show the effectiveness of the advocated design methodology.
Fuzzy logic controller optimization
Sepe Jr., Raymond B; Miller, John Michael
2004-03-23
A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
Adaptive fuzzy system for 3-D vision
NASA Technical Reports Server (NTRS)
Mitra, Sunanda
1993-01-01
An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.
Chih-Hsien Kung; Michael J. Devaney; Chung-Ming Huang
2000-01-01
This paper describes in detail the design of an innovative fuzzy-based adaptive digital PID controller for a three-degrees-of-freedom (3-DOF) in-parallel actuated manipulator. Conventionally, tuning the gains of the PID controller requires experience and know-how, and has to be performed experimentally. The proposed innovative fuzzy-based PID automatic adjustment scheme is an adaptive approach that is capable of self-tuning the controller to
Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei
2013-01-01
This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control. PMID:23820746
Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei
2013-01-01
This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control. PMID:23820746
AutoTuning Fuzzy PID Control of a Pendubot System
Chia-Ju Wu; Tsong-Li Lee; Yu-Yi Fu; Chia-Ju Li-Chun Lai
2007-01-01
The goal of this paper is to design a fuzzy proportional-integral-derivative (PID) controller to swing up the pendubot and maintain it in an unstable inverted equilibrium position. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules such that the PID gains are adaptive and the fuzzy PID controller has more flexibility
ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-I intelligent leg
Guan-zheng Tan; Hong-quan Dou
2007-01-01
Based on the ant colony system (ACS) algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller\\u000a was proposed. In this method, the ACS algorithm was used to optimize the input\\/output scaling factors of fuzzy PID controller\\u000a to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1992-01-01
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
2009-04-01
This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level and precipitable water. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy in 90 percent of the years
Adaptive fuzzy controller for loop control in a Distributed Control system
M. Addel-Geliel; A. Khalil
2009-01-01
To simplify the control task and reduce the computation burden of control system, distributed control system (DCS) becomes the most suitable control system structure especially for medium and large size of industrial processes. In DCS system the control task is distributed among some controllers, which communicate to each other via communication network, such as PLC or\\/and industrial PC. In most
Yifan Tang; Longya Xu
1994-01-01
Fuzzy control methods are developed for a highly efficient variable speed constant frequency power generating system, with wind power generation as an example. In slip power recovery configuration, the system is composed of a doubly excited brushless reluctance-machine and a reduced rating power electronic converter unit linking the stator secondary circuit with the power line. The doubly excited brushless reluctance
NASA Astrophysics Data System (ADS)
Boudana, Djamel; Nezli, Lazhari; Tlemçani, Abdelhalim; Mahmoudi, Mohand Oulhadj; Tadjine, Mohamed
2012-05-01
The double star synchronous machine (DSSM) is widely used for high power traction drives. It possesses several advantages over the conventional three phase machine. To reduce the torque ripple the DSSM are supplied with source voltage inverter (VSI). The model of the system DSSM-VSI is high order, multivariable and nonlinear. Further, big harmonic currents are generated. The aim of this paper is to develop a new direct torque adaptive fuzzy logic control in order to control DSSM and minimize the harmonics currents. Simulations results are given to show the effectiveness of our approach.
Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip
2014-10-01
This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples. PMID:25222716
Fuzzy control in process automation
H.-P. PREUß
1993-01-01
Fuzzy logic provides an extremely practical method of incorporating empirical process knowledge and linguistically formulated control strategies appropriately into process automation applications. This potential makes fuzzy logic controllers attractive for a wide range of industrial processes. In view of its significant appeal, the obvious way of implementing practical fuzzy control solutions quickly and successfully is by integrating the fuzzy functions
Fuzzy logic microcontroller implementation for DC motor speed control
Yodyium Tipsuwan; Mo-Yuen Chow
1999-01-01
This paper describes an alternative method to implement a fuzzy logic speed controller for a DC motor using a fuzzy logic microcontroller. The design, implementation and experimental results on load and no-load conditions are presented. The controller can be implemented by using only a small amount of components and easily improved to be an adaptive fuzzy controller. The controller also
Fuzzy PD+I learning control for a pneumatic muscle
S. W. Chan; John H. Lilly; Daniel W. Repperger; James E. Berlin
2003-01-01
A fuzzy learning control technique is used for position tracking involving the vertical movement of a mass attached to a pneumatic muscle. Because the pneumatic muscle is nonlinear and time varying, conventional fixed controllers are less effective than the fuzzy controller proposed in this paper. The controller is of a PID type, with an adaptive fuzzy PD part and a
Research on fuzzy robust adaptive unscented particle filtering
NASA Astrophysics Data System (ADS)
Gao, Yi; Gao, Shesheng
2011-10-01
This paper present a new fuzzy robust adaptive Unscented particle filtering method based on the fuzzy control theory. This method absorbs the advantages of the fuzzy control theory, the robust adaptive filtering and the Unscented particle filtering. Using the influence of the gross errors in the observation vectors on the state vector parameters to obtain the robust adaptive Unscented particle filtering model. Experiment results and comparison analysis demonstrate that this proposed methodology provides an effective solution for improving the positioning accuracy in navigation system.
Fuzzy control of small servo motors
NASA Technical Reports Server (NTRS)
Maor, Ron; Jani, Yashvant
1993-01-01
To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.
Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu
2014-09-01
This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. PMID:24975565
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
Kobravi, Hamid-Reza; Erfanian, Abbas
2012-01-01
In this paper, we present a novel decentralized robust methodology for control of quiet upright posture during arm-free paraplegic standing using functional electrical stimulation (FES). Each muscle-joint complex is considered as a subsystem and individual controllers are designed for each one. Each controller operates solely on its associated subsystem, with no exchange of information between them, and the interaction between the subsystems are taken as external disturbances. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed. The method is based on the synergistic combination of an adaptive nonlinear compensator with sliding mode control (SMC). Fuzzy logic system is used to represent unknown system dynamics for implementing SMC and an adaptive updating law is designed for online estimating the system parameters such that the global stability and asymptotic convergence to zero of tracking errors is guaranteed. The proposed controller requires no prior knowledge about the dynamics of system to be controlled and no offline learning phase. The results of experiments on three paraplegic subjects show that the proposed control strategy is able to maintain the vertical standing posture using only FES control of ankle dorsiflexion and plantarflexion without using upper limbs for support and to compensate the effect of external disturbances and muscle fatigue. PMID:21764350
Fuzzy logic in control systems: fuzzy logic controller. I
C. C. Lee
1990-01-01
The fuzzy logic controller (FLC) provides a means of converting a linguistic control strategy. A survey of the FLC is presented, and a general methodology for constructing an FLC and assessing its performance is described. In particular, attention is given to fuzzification and defuzzification strategies, the derivation of the database and fuzzy control rules, the definition of fuzzy implication, and
Analysis and design of fuzzy controller and fuzzy observer
Xiao-Jun Ma; Zeng-Qi Sun; Yan-Yan He
1998-01-01
This paper addresses the analysis and design of a fuzzy controller and a fuzzy observer on the basis of the Takagi-Sugeno (T-S) fuzzy model. The main contribution of the paper is the development of the separation property; that is, the fuzzy controller and the fuzzy observer can be independently designed. A numerical simulation and an experiment on an inverted pendulum
Fuzzy adaptive control for the actuators position control and modeling of an expert system
Servet Soyguder; Hasan Alli
2010-01-01
In this paper, a heating, ventilating and air-conditioning (HVAC) system was designed and two different damper gap rates (actuators position) of the HVAC system were controlled by a conventional PID (proportional–integral–derivative) controller. One of the dampers was controlled by using the required temperature for the interested indoor volume while the other damper was controlled by using the required humidity for
A fuzzy-tuned adaptive Kalman filter
Painter, John H.; Young Hwan Lho
1993-12-01
In this paper, fuzzy processing is applied to the adaptive Kalman filter. The filter gain coefficients are adapted over a 50 dB range of unknown signal/noise dynamics, using fuzzy membership functions. Specific simulation results are shown for a...
NASA Astrophysics Data System (ADS)
Fallah-Ghalhary, Gholam Abbas; Habibi-Nokhandan, Majid; Mousavi-Baygi, Mohammad; Khoshhal, Javad; Shaemi Barzoki, Akbar
2010-07-01
This paper aims to study the relationship between large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neuro-fuzzy inference system (ANFIS) was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and the 1,000-hPa level, the temperature of the 700-hPa level, the thickness between the 500- and 1,000-hPa levels, the relative humidity at the 300-hPa level, and precipitable water content. We have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the ANFIS in the period 1970-1997 has been taught. Finally, we forecast the rainfall for the period 1998-2007. The results show that the ANFIS can predict the rainfall with reasonable accuracy.
Seungdae Kim; Hunmo Kim; Yoon-Gyeoung Sung
2001-01-01
In brake systems, a proportioning valve(P. V), which reduces the brake line pressure on each wheel cylinder for the anti-locking\\u000a of rear wheels, is closely related to the safety of vehicles. However, it is impossible for current P. V. s to completely\\u000a control brake line pressure because, mechanically, it is an open loop control system. In this paper we describe
The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller
Francesco Calvino; Maria La Gennusa; Gianfranco Rizzo; Gianluca Scaccianoce
2004-01-01
The control and the monitoring of indoor thermal conditions represents a pre-eminent task with the aim of ensuring suitable working and living spaces to people. Especially in industrialised countries, in fact, several rules and standards have been recently released in order of providing technicians with suitable design tools and effective indexes and parameters for the checking of the indoor microclimate.
Zhiliang Ding; Changde Wang; Guangming Tan; Guanghua Guan
2009-01-01
The PID controller is widely used in the automatic water conveyance system of canal. However, the conventional PID controller has poor adaptability to canal operating conditions, and along with the variation of canal operating conditions, we must continuously adjust the PID control parameters, it brings some difficulties to the practical operation. And the online self-adapting of PID parameters is also
M. Sridhar; K. Vaisakh; K. S. Linga Murthy
2009-01-01
The power system is a dynamic system. Satisfactory damping of power oscillations is an important concern when dealing with the rotor angle stability. To improve the damping of oscillations in power systems, supplementary control laws can be applied to existing devices. In this paper, a PID fuzzy controller structure and optimal PI controller of a static Var compensator (SVC) are
Ranjit Kumar Barai; Kenzo Nonami
2008-01-01
Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and\\u000a large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery\\u000a delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional\\u000a electromagnetic control valves and the delay
NASA Astrophysics Data System (ADS)
Ronilaya, Ferdian; Miyauchi, Hajime
2014-10-01
This paper presents a new implementation of a parameter adaptive PID-type fuzzy controller (PAPIDfc) for a grid-supporting inverter of battery to alleviate frequency fluctuations in a wind-diesel power system. A variable speed wind turbine that drives a permanent magnet synchronous generator is assumed for demonstrations. The PAPIDfc controller is built from a set of control rules that adopts the droop method and uses only locally measurable frequency signal. The output control signal is determined from the knowledge base and the fuzzy inference. The input-derivative gain and the output-integral gain of the PAPIDfc are tuned online. To ensure safe battery operating limits, we also propose a protection scheme called intelligent battery protection (IBP). Several simulation experiments are performed by using MATLAB®/SimPowersystems™. Next, to verify the scheme's effectiveness, the simulation results are compared with the results of conventional controllers. The results demonstrate the effectiveness of the PAPIDfc scheme to control a grid-supporting inverter of the battery in the reduction of frequency fluctuations.
Intelligent control of non-linear dynamic plants using type-2 fuzzy logic and neural networks
Patricia Melin; Oscar Castillo
2002-01-01
We describe adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks. First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A specific non-linear plant is used to
Application of Adaptive Type2 Fuzzy CMAC to Automatic Landing System
Teng-Chieh Yang; Jih-Gau Juang
2010-01-01
Computational intelligence that utilizes adaptive fuzzy cerebellar model articulation controller (FCMAC) to aircraft automatic landing system is proposed in this paper. The proposed intelligent scheme uses CMAC and type-2 fuzzy system. Current flight control law is adopted in the controller design. Lyapunov stability theory is applied to obtain adaptive learning rule and to guarantee stability of the automatic landing system.
NASA Astrophysics Data System (ADS)
Ajay Kumar, M.; Srikanth, N.
2014-03-01
In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.
Granada, Universidad de
jFuzzyLogic: a Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy@decsai.ugr.es Abstract Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems suitable for engineering a standard for fuzzy control programming in part 7 of the IEC 61131 norm in order to offer a well defined
Layered Argumentation For Fuzzy Automation Controllers
Governatori, Guido
Layered Argumentation For Fuzzy Automation Controllers Insu Song School of Business and IT James implementation of Fuzzy automation controllers. Keywords-Fuzzy logic, Fuzzy controller, Nonmonotonic logic.diederich@jcu.edu.sg Abstract--We develop a layered argumentation system (LAS) for efficient implementation of Fuzzy automation
Embedded Control Systems Research Lab Fuzzy Membership Function
Simon, Dan
Embedded Control Systems Research Lab Fuzzy Membership Function Optimization for System Conference June 4, 2006 #12;Embedded Control Systems Research Lab Overview Real system Fuzzy logic system inputs Â + Adaptation algorithm #12;Embedded Control Systems Research Lab Overview 1. Introduction 2
Multivariable Fuzzy Generalized Predictive Control
Huaguang Zhang; Lilong Cai
2002-01-01
A Takagi-Sugeno (T-S) fuzzy model is used to express non-linear dynamic systems with time-delay in this paper, and an on-line identi® cation algorithm is presented regarding its parameters and structures. A multivariable fuzzy generalized predictive control approach is proposed based on the identi® ed fuzzy model by means of the generalized predictive control principle. The closed-loop stability is analyzed in
Fuzzy logic in control systems: Fuzzy logic controller. I, II
NASA Technical Reports Server (NTRS)
Lee, Chuen Chien
1990-01-01
Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.
Active control of vibration using a fuzzy control method
NASA Astrophysics Data System (ADS)
Wenzhong, Qu; Jincai, Sun; Yang, Qiu
2004-08-01
Active control of vibration has been the subject of a lot of research in recent years. Adaptive linear filtering techniques have been extensively used for the active control of vibration. A popular adaptive filtering algorithm is the filtered-X LMS algorithm because of its simplicity and its relatively low computational load. However, the filtered-X LMS algorithm is limited to linear control problems using feedforward control techniques. A fuzzy logic system based control structure and adaptive algorithm suitable for driving non-linear feedforward active vibration control systems is presented in this paper. This kind of non-linear fuzzy control method is used in two kinds of active vibration control problems, as follows: (1) A fuzzy logic system is used to approximate a non-linear sensor path function and to suppress the primary disturbance that is a non-linear function of the reference signal. (2) A fuzzy logic system is used in active control of vibration with non-linear piezoelectric actuators. The numerical simulation results show the effectiveness of this adaptive fuzzy control method.
Current projects in Fuzzy Control
NASA Technical Reports Server (NTRS)
Sugeno, Michio
1990-01-01
Viewgraphs on current projects in fuzzy control are presented. Three projects on helicopter flight control are discussed. The projects are (1) radio control by oral instructions; (2) automatic autorotation entry in engine failure; and (3) unmanned helicopter for sea rescue.
Marcos J. Ara; M. Cano-Izquierdoc; Yannis A. Dimitriadisd
This paper addresses the automatization of a penicillin production process with the development of soft sensors as well as Internal Model Controllers (IMC) for a penicillin fermentation plant using modules based on FasArt and FasBack neuro-fuzzy systems. While soft sensors are intended to aid the human supervision of the process currently being conducted at pilot plants, the proposed controller will
Marcos J. Araúzo-Bravo; José M. Cano-Izquierdo; Eduardo Gómez-Sánchez; Manuel J. López-Nieto; Yannis A. Dimitriadis; Juan López-Coronado
2004-01-01
This paper addresses the automatization of a penicillin production process with the development of soft sensors as well as Internal Model Controllers (IMC) for a penicillin fermentation plant using modules based on FasArt and FasBack neuro-fuzzy systems. While soft sensors are intended to aid the human supervision of the process currently being conducted at pilot plants, the proposed controller will
Intelligent Speed Adaptation Using a Self-Organizing Neuro-Fuzzy Controller
Paris-Sud XI, Université de
, car following, and overtaking. This paper presents a new approach to autonomously adapt the speed Organization, estimating that 1.2 millions people are killed in road crashes each year, and as many as 50 years. The global cost of road crashed and injuries is estimated to be US$ 518 billions per year
Extending Fuzzy System Concepts for Control of a Vitrification Melter
Whitehouse, J.C. [Westinghouse Savannah River Company, AIKEN, SC (United States); Sorgel, W. [Clemson University, Clemson, SC (United States); Garrison, A. [Clemson University, Clemson, SC (United States); Schalkoff, R.J. [Clemson University, Clemson, SC (United States)
1995-08-16
Fuzzy systems provide a mathematical framework to capture uncertainty. The complete description of real, complex systems or situations often requires far more detail and information than could ever be obtained (or understood). Fuzzy approaches are an alternative technology for both system control and information processing and management. In this paper, we present the design of a fuzzy control system for a melter used in the vitrification of hazardous waste. Design issues, especially those related to melter shutdown and obtaining smooth control surfaces, are addressed. Several extensions to commonly-applied fuzzy techniques, notably adaptive defuzzification and modified rule structures are developed.
Adaptive Fuzzy Systems in Computational Intelligence
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
Fuzzy-neural control of an aircraft tracking camera platform
NASA Technical Reports Server (NTRS)
Mcgrath, Dennis
1994-01-01
A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.
Conventional fuzzy control and its enhancement
Han-Xiong Li; H. B. Gatland
1996-01-01
Conventional fuzzy control can be considered mainly composed of fuzzy two-term control and fuzzy three-term control. In this paper, more systematic analysis and design are given for the conventional fuzzy control. A general robust rule base is proposed for fuzzy two-term control, leaving the optimum tuning to the scaling gains, which greatly reduces the difficulties of design and tuning. The
Generalized predictive control with fuzzy soft constraints
Shaoyuan Li; Yugeng Xi
2000-01-01
This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Both equality and inequality constraints can be handled in a unified form, i.e., fuzzy soft constraints. Thus, the traditional
Adaptive Two-Stage Fuzzy Temperature Control for an Electroheat System
Chih-Hu Wang; Chun-Hung Lin; Bore-Kuen Lee; Chien-Nan Jimmy Liu; Chauchin Su
2009-01-01
to many temperature analyses as a result of its simplicity. In (2), a PID controller is used for temperature control of a 20 KW industrial electric resistance furnace where the plant is modeled by a first-order transfer function with a time delay. Temperature control of a heated barrel with electric heaters and water coolers was proposed in (3) which used
EMBEDDED FUZZY CONTROL FOR AUTOMATIC CHANNEL EQUALIZATION AFTER DIGITAL TRANSMISSIONS
Verleysen, Michel
EMBEDDED FUZZY CONTROL FOR AUTOMATIC CHANNEL EQUALIZATION AFTER DIGITAL TRANSMISSIONS Carlos for automatic adaptation of channels equalizers after digital data transmission is presented. Inter-line during transmission and no reference signal is required. The presented methodology was validated
Adaptive fuzzy controller for vehicle active suspensions with particle swarm optimization
NASA Astrophysics Data System (ADS)
Cao, Jiangtao; Li, Ping; Liu, Honghai; Brown, David
2008-10-01
In this study, the probability density function (PDF) control method has been developed to deal with the random tracking error for a class of robotic manipulator that are subjected to non-Gaussian noises. The control aim is that the shape of the PDF of the tracking error is made as close as possible to the desired PDF. The ILC frame about PDF control approach of manipulators system with non-Gaussian noises has been proposed and a recursive optimization solution batch-by-batch has been developed. In each batch, nonlinear closed-loop error dynamics is considered. In addition, the convergence condition of the tracking control algorithm has been analyzed. Finally, a simulation is given to illustrate the efficiency of the proposed approach.
Fuzzy logic control of telerobot manipulators
NASA Technical Reports Server (NTRS)
Franke, Ernest A.; Nedungadi, Ashok
1992-01-01
Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.
How to combine probabilistic and fuzzy uncertainties in fuzzy control
NASA Technical Reports Server (NTRS)
Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert
1991-01-01
Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.
Fuzzy scheduled RTDA controller design.
Srinivasan, K; Anbarasan, K
2013-03-01
In this paper, the design and development of fuzzy scheduled robustness, tracking, disturbance rejection and overall aggressiveness (RTDA) controller design for non-linear processes are discussed. pH process is highly non-linear and the design of good controller for this process is always a challenging one due to large gain variation. Fuzzy scheduled RTDA controller design based on normalized integral square error (N_ISE) performance criteria for pH neutralization process is developed. The applicability of the proposed controller is tested for other different non-linear processes like type I diabetic process and conical tank process. The servo and regulatory performance of fuzzy scheduled RTDA controller design is compared with well-tuned internal model control (IMC) and dynamic matrix control (DMC)-based control schemes. PMID:23317662
Fuzzy coordinator in control problems
NASA Technical Reports Server (NTRS)
Rueda, A.; Pedrycz, W.
1992-01-01
In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant.
Fuzzy support vector machines for adaptive Morse code recognition.
Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh
2006-11-01
Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature. PMID:16807054
A. G. B. P. Jayasekara; K. Watanabe; K. Kiguchi; K. Izumi
2009-01-01
This paper proposes a method to adapt robot behaviors toward user's perception by human teaching. Human-friendly robotic system should be able to understand the fuzzy linguistic information based on the user's guidance and the environmental conditions. The contextual meaning of fuzzy linguistic information depends on the conditions of the environment. Therefore, user's perception is acquired to evaluate the fuzzy linguistic
Temperature control with a neural fuzzy inference network
Chin-Teng Lin; Chia-Feng Juang; Chung-Ping Li
1996-01-01
We propose a neural fuzzy inference network (NFIN) suitable for adaptive temperature control of a water bath system. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. The performance of the NFIN is compared to that of
Fuzzy control of clutch for automatic mechanical transmission vehicle starting
Xianping Xie; Xudong Wang; Xun Zhang; Tengwei Yu
2008-01-01
As for clutch control during automatic mechanical transmission (AMT) starting, it is difficult to design an effective and adaptive control strategy. Aiming at it, an improved constant engine speed control strategy is adopted as a total control principle, and on basis of it a new fuzzy control strategy using throttle opening, relative deviation between engine speed and its target and
Fuzzy controller for inverter fed induction machines
Sayeed A. Mir; Donald S. Zinger; Malik E. Elbuluk
1994-01-01
An induction machine operated with a direct self controller (DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic is used in conjunction with direct self control to minimize these problems. A fuzzy logic controller chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in
Learning fuzzy logic control system
NASA Technical Reports Server (NTRS)
Lung, Leung Kam
1994-01-01
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore, the LFLC has better performance in rise time, settling time and steady state error than to the conventional PI controller. This abstract accurately represents the content of the candidate's thesis. I recommend its publication.
13 Intelligent Control of the Pendubot with Interval Type2 Fuzzy Logic
Oscar Castillo; Patricia Melin
We describe in this chapter adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks.\\u000a First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive\\u000a control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A\\u000a specific non-linear plant
A fuzzy PLC with gain-scheduling control resolution for a thermal process – a case study
H.-X. Li; S. K. Tso
1999-01-01
This paper presents a case study on the practical implementation of a fuzzy-PLC system for a thermal process. The theoretical study indicates that the inferior performance of fuzzy-controlled processes around a reference point is often caused by insufficient resolution of the fuzzy inference. The limitations of ladder logic cannot support complex algorithms for resolution improvement. A simple gain adaptation method
Fuzzy predictive control of nonlinear systems
Widien Dhouib; Mohamed Djemel; Mohamed Chtourou
2011-01-01
This paper presents two strategies of nonlinear predictive control based on a Takagi- Sugeno fuzzy model. The first one introduces a fuzzy logic-based modeling methodology, where a nonlinear system is divided into a number of linear subsystems. So the linear model based predictive control (MPC) technique is used for each subsystem.In the second one, the fuzzy model is considered as
ANFIS: adaptive-network-based fuzzy inference system
Jyh-Shing Roger Jang
1993-01-01
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation,
Water bath temperature control with a neural fuzzy inference network
Chin-Teng Lin; Chia-Feng Juang; Chung-Ping Li
2000-01-01
Although multilayered backpropagation neural networks (BPNN) have demonstrated high potential in the nonconventional branch of adaptive control, its long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the overtuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) in this
Temperature control with a neural fuzzy inference network
Chin-teng Lin; Chia-feng Juang; Chung-ping Li
1999-01-01
Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network
Multisensor integration and image recognition using Fuzzy Adaptive Resonance Theory
NASA Astrophysics Data System (ADS)
Singer, Steven M.
1997-04-01
The main objective of this work was to investigate the use of 'sensor based real time decision and control technology' applied to actively control the arrestment of aircraft (manned or unmanned). The proposed method is to develop an adaptively controlled system that would locate the aircraft's extended tailhook, predict its position and speed at the time of arrestment, adjust an arresting end effector to actively mate with the arresting hook and remove the aircraft's kinetic energy, thus minimizing the arresting distance and impact stresses. The focus of the work presented in this paper was to explore the use of fuzzy adaptive resonance theorem (fuzzy art) neural network to form a MSI scheme which reduces image data to recognize incoming aircraft and extended tailhook. Using inputs from several image sources a single fused image was generated to give details about range and tailhook characteristics for an F18 naval aircraft. The idea is to partition an image into cells and evaluate each using fuzzy art. Once the incoming aircraft is located in a cell that subimage is again divided into smaller cells. This image is evaluated to locate various parts of the aircraft (i.e., wings, tail, tailhook, etc.). the cell that contains the tailhook provides resolved position information. Multiple images from separate sensors provides opportunity to generate range details overtime.
Study On Sunlight Greenhouse Temperature And Humidity Fuzzy Control System
Lishu Wang; Guanglin Yang; Qiang Fu; Xiangfeng Xu
2005-01-01
Through establishing fuzzy control system model, designing on fuzzy controller, controlling sunlight greenhouse temperature and humidity, we designed temperature and humidity fuzzy control system, and then studied on input and output parameter in fuzzy controller, analyzed membership function of inputting and outputting parameter, then designed fuzzy control operation. The greenhouse has the best entironment for crop growing. (Nature and Science.
An Elevator Group Control System With a Self-Tuning Fuzzy Logic Group Controller
Jafferi Jamaludin; N. Abd Rahim; Wooi Ping Hew
2010-01-01
This paper presents a new group controller's adaptation mechanism with fuzzy logic for elevator group control system (EGCS) applications. Instead of depending heavily on the predicted passenger traffic pattern for adaptation, the fuzzy logic group controller (FLGC) adjusts itself to suit the system's environment through a self-tuning scheme. The average-waiting-time data that reflect the measured performance results of the EGCS
Hani Hagras; Victor Callaghan; Martin Colley; Graham Clarke
2003-01-01
In this paper, we describe a new application domain for intelligent autonomous systems - Intelligent Buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. The
Design of Fuzzy PID Controller and Application in Glass Furnace
Qi Jianling; Deng Zhenjie; Li Yezi
2007-01-01
In this paper, the structure and feature of fuzzy logic controllers are described first, then a new type of the fuzzy PID (proportional-integral-derivative) controller is presented. The fuzzy PID controller has the simplest structure, but it has the nonlinear properties, which provide the fuzzy PID control system with a superior performance over the conventional PID control system. The fuzzy PID
Fuzzy logic control and optimization system
Lou, Xinsheng (West Hartford, CT)
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Fuzzy control of an unmanned helicopter
NASA Technical Reports Server (NTRS)
Sugeno, M.; Nishino, J.; Miwa, H.
1993-01-01
This paper discusses an application of fuzzy control to an unmanned helicopter. The authors design a fuzzy controller to achieve semi-autonomous flight of a helicopter by giving macroscopic flight commands from the ground. The fuzzy controller proposed in this study consists of two layers: the upper layer for navigation supervising the lower layer and the lower layer for ordinary rule based control. The performance of the fuzzy controller is evaluated in experiments where an industrial helicopter Yamaha R-50 is used. At present an operator can wirelessly control the helicopter through a flight computer with eight commands such as 'hover', 'fly forward', 'turn left', 'stop', etc.
D Kolokotsa; D Tsiavos; G. S Stavrakakis; K Kalaitzakis; E Antonidakis
2001-01-01
The aim of this paper is to present and evaluate control strategies for adjustment and preservation of air quality, thermal and visual comfort for buildings’ occupants while, simultaneously, energy consumption reduction is achieved. Fuzzy PID, fuzzy PD and adaptive fuzzy PD control methods are applied. The inputs to any controller are: the PMV index affecting thermal comfort, the CO2 concentration
NASA Astrophysics Data System (ADS)
Kim, Hunmo
In the brake systems, it is important to reduce the rear brake pressure in order to secure the safety of the vehicle in braking. So, there was some research that reduced and controlled the rear brake pressure exactly like a L. S. P. V and a E. L. S. P. V. However, the previous research has some weaknesses: the L. S. P. V is a mechanical system and its brake efficiency is lower than the efficiency of E. L. S. P. V. But, the cost of E. L. S. P. V is very higher so its application to the vehicle is very difficult. Additionally, when a fail appears in the circuit which controls the valves, the fail results in some wrong operation of the valves. But, the previous researchers didn't take the effect of fail into account. Hence, the efficiency of them is low and the safety of the vehicle is not confirmed. So, in this paper we develop a new economical pressure modulator that exactly controls brake pressure and confirms the safety of the vehicle in any case using a direct adaptive fuzzy controller.
Designing fuzzy net controllers using genetic algorithms
Jinwoo Kim; Yoonkeon Moon; Bernard P. Zeigler
1995-01-01
As control system tasks become more demanding, more robust controller design methodologies are needed. A genetic algorithm (GA) optimizer, which utilizes natural evolution strategies, offers a promising technology that supports optimization of the parameters of fuzzy logic and other parameterized nonlinear controllers. This article shows how GAs can effectively and efficiently optimize the performance of fuzzy net controllers employing high
Fuzzy expert system for automatic transmission control
H.-G. Weil; G. Probst; F. Graf
1992-01-01
An intelligent electronic transmission control system using both fuzzy logic and conventional methods is presented. The controller is part of the drive train, so that special attention must be paid to the system's security. Two different methods for prioritizing the controller outputs are proposed, and it is shown how these methods can be realized within fuzzy control theory. Next, it
Optimal fuzzy control of autonomous robot car
Ovid Farhi; Yordan Chervenkov
2008-01-01
The paper describes the design of the fuzzy control system for an autonomous robot car which operates in unknown, unpredictable, and dynamic environment. The fuzzy control system must provide the fusing of data from multiple sensors and must ensure navigation of the autonomous robot car. Both - an obstacle avoidance control strategy and a target tracking control strategy - are
Fuzzy decision and control, the Bayes context
Painter, John H.
1993-12-15
This paper shows how it is that fuzzy control may be viewed as a particular kind of stochastic (Bayesian) control. With the Bayes approach, fuzzy control may be viewed as an ensembled-average control, where the average is taken over a set...
Determining limit cycles in fuzzy control systems
F. Gordillo; J. Aracil; T. Alamo
1997-01-01
We consider nonlinear control systems including fuzzy logic controllers. The dynamical behavior of such systems may be much richer and more complex than that of linear systems. This paper deals with the application of classical control techniques of system analysis, including frequency domain methods, which allow one to gain a better understanding the behavior of systems controlled by fuzzy logic.
Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech Prediction
Dimitris G. Stavrakoudis; John B. Theocharis
2007-01-01
A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number
Fuzzy regulator design for wind turbine yaw control.
Theodoropoulos, Stefanos; Kandris, Dionisis; Samarakou, Maria; Koulouras, Grigorios
2014-01-01
This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237
Fuzzy Regulator Design for Wind Turbine Yaw Control
Koulouras, Grigorios
2014-01-01
This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237
The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor
NASA Astrophysics Data System (ADS)
Wu, Ying; Jiang, Hang; Zou, Min
Based on analyzing mathematical mode of the permanent magnet linear synchronous motor (PMLSM), three-closed-loop control system is presented in this paper. Combined the advantages of traditional PID control algorithm and fuzzy control algorithm, according to the characteristics of linear motor and the possible factors of uncertainty, a set of adaptive fuzzy PID control system is designed for the speed loop of the proposed control system, moreover, fuzzy inference rules is established to realize the Fuzzy PID controlling of the speed loop. In the end, the simulation models of the motor and the whole control system are built on Matlab/Simulink platform to compare and analyze the fuzzy PID control and conventional PID control. Simulation results show that the designed fuzzy PID speed loop controller can significantly improve the response performance of linear motor.
Fuzzy EMG classification for prosthesis control
Francis H. Y. Chan; Yong-Sheng Yang; F. K. Lam; Yuan-Ting Zhang; Philip A. Parker
2000-01-01
Proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain
Fuzzy logic control for camera tracking system
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant
1992-01-01
A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.
Jianchang Liu; Yiyang Liu
2007-01-01
To improve the performance of elevator group control systems (EGCS), an intelligent dispatching method based on ant colony algorithm and fuzzy neural network is presented. An elevator group control system based on fuzzy neural network adapts to various traffic flow modes. Using ant colony algorithm to optimize the weights of fuzzy neural network before training with BP algorithm can solve
Adaptive neuro-fuzzy and fuzzy decision tree classifiers as applied to seafloor characterization
NASA Astrophysics Data System (ADS)
Stepnowski, A.; Moszy?ski, M.; van Dung, Tran
2003-03-01
This paper investigates the influence of various backscattered echo parameters collected at an echosounder frequency of 200 kHz on the performance of the neuro-fuzzy and fuzzy decision tree classifiers of a seabed. In particular, the wavelet coefficients of the bottom echo were investigated along with other echo parameters like energy, amplitude, slope of the falling part of the echo, etc. The data were processed in an Adaptive Neuro Fuzzy Inference System (ANFIS), which was implemented in two multistage structures, viz.; Incremental Fuzzy Neural Network (IFNN) and Aggregated Fuzzy Neural Network (AFNN). The number of input parameters for the networks was reduced by using the Principal Component Analysis (PCA). A fuzzy decision tree algorithm was developed and used directly (without PCA data reduction) in the classification procedure utilizing the same data. The performances of both approaches were analyzed and compared.
A fuzzy classifier system for process control
NASA Technical Reports Server (NTRS)
Karr, C. L.; Phillips, J. C.
1994-01-01
A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.
Refining fuzzy logic controllers with machine learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1994-01-01
In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.
Robust Fuzzy Controllers Using FPGAs
NASA Technical Reports Server (NTRS)
Monroe, Author Gene S., Jr.
2007-01-01
Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.
Stabilizing RED using a Fuzzy Controller
Jinsheng Sun; Moshe Zukerman; Marimuthu Palaniswami
2007-01-01
Active queue management (AQM) is an effective method to provide an early notification of network congestion by pro-actively dropping or marking packets. In this paper, we propose a novel algorithm called fuzzy control RED (FCRED) that overcomes the drawbacks of the original RED. FCRED uses a fuzzy controller to adjust the maximum drop probability to stabilize the average queue length
Fuzzy logic control for parallel hybrid vehicles
Niels J. Schouten; Mutasim A. Salman; Naim A. Kheir
2002-01-01
In this paper, a fuzzy logic controller is developed for hybrid vehicles with parallel configuration. Using the driver command, the state of charge of the energy storage, and the motor\\/generator speed, a set of rules have been developed, in a fuzzy controller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme
Stability of fuzzy linguistic control systems
Gholamreza Langari; M. Tomizuka
1990-01-01
A new approach to the stability analysis of fuzzy linguistic control (FLC) systems is presented. Specifically, it is shown that the direct method of Lyapunov can be used to determine sufficient conditions for global stability of a broad class of fuzzy control schemes. Moreover, a measure of robustness is proposed that can be used to evaluate and possibly redesign a
A new methodology for designing a fuzzy logic controller
Han-Xiong Li; H. B. Gatland
1995-01-01
A new methodology is proposed for designing a fuzzy logic controller (FLC). A phase plane is used to bridge the gap between the time-response and rule base. The rule base can be easily built using the general dynamics of the process, and then readily updated to contain the delayed information for reducing the deadtime effects of the process. An adaptive
A fuzzy logic controller for autonomous vehicle control
Vinson, Yale Patrick
1995-01-01
This thesis presents a feasibility study for the use of fuzzy logic control to solve the autonomous vehicle following problem. After developing the original vehicle following system, the applicability of fuzzy logic to the problem is demonstrated...
Assistant-Parking System Based on Vision Perception System and Fuzzy Logic Controller
Ma Shaohua; Wang Chao
2010-01-01
This paper develops Assistant-Parking system based on four-cameras and fuzzy logic controller, acquires the real-time information around vehicle and pre-parking space using wide-angle cameras, using vision perception system transform three-dimensional image of reality environment to top view, and offers driver the parking-controlling and real-time monitoring. Using the improved fuzzy system learning optimal fuzzy algorithm, enhance the adaptability for nonlinearity, time-varying
Implementation of fuzzy logic control based on PLC
Jiri Kocian; Jiri Koziorek; Miroslav Pokorny
2011-01-01
A substantial portion of the literature on fuzzy control deals with the usage of fuzzy rules to implement PID type control and also fuzzy supervisory control. Fuzzy models have received significant attention from various fields of interest. Especially so called the Takagi-Sugeno-type fuzzy model which superbly describes a nonlinear system. In this paper we present implementation of universal fuzzy PS\\/PD
A Neuro-fuzzy based Unified Power Flow Controller for Improvement of Transient Stability Performance
P K Hota
2004-01-01
In this paper an adaptive unified power flow controller (UPFC) has been designed with the application of the intelligent techniques such as a combination of neural network and fuzzy logic. The Mumdani type fuzzy controller with constant center for output classes may not perform satisfactorily in all operating conditions. To overcome this limitation it is made variable according to operating
José António Barros Vieira; Fernando Morgado Dias; Alexandre Manuel Mot
This article presents a comparison of Artificial Neural Networks and Neuro- Fuzzy Systems applied for modeling and controlling a real system. The main objective is to control the temperature inside of a ceramics kiln. The details of all system components are described. The steps taken to arrive at the direct and inverse models using the two architectures: Adaptive Neuro Fuzzy
A fuzzy control design case: The fuzzy PLL
NASA Technical Reports Server (NTRS)
Teodorescu, H. N.; Bogdan, I.
1992-01-01
The aim of this paper is to present a typical fuzzy control design case. The analyzed controlled systems are the phase-locked loops (PLL's)--classic systems realized in both analogic and digital technology. The crisp PLL devices are well known.
Study of fuzzy controller based on analysis method with PLC
Jingzhao Li; Chongwei Zhang
2002-01-01
Fuzzy controller based on analysis method is achieved in this paper. Fuzzification of input signal and fuzzy control rule based on analysis method and defuzzification of output signal are researched, and PLC's ladder is given. The fact shows that this fuzzy controller is inexpensive and practical. It provides wider application for fuzzy control technology.
Fuzzy Control System for Intelligent Car
Xiao-feng Wan; Yi-si Xing; Li-xiang Cai
2009-01-01
Fuzzy control system of intelligent car is designed. The control system selects the 16-bit microcontroller unit MC9S12DG128 produced by Freescale Semiconductor Company as the core controller; employs COMS camera to detect the location and movement direction of the intelligent car; applys photoelectric encoder to examine the carpsilas speed; makes use of fuzzy control algorithm to adjust DC motorpsilas speed and
Synthesis of nonlinear control strategies from fuzzy logic control algorithms
NASA Technical Reports Server (NTRS)
Langari, Reza
1993-01-01
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.
An architecture for intelligent sensors and fuzzy inputs for fuzzy logic controllers
Gillespie, Charles Wayne
1993-01-01
Many papers have shown that fuzzy logic can be successfully applied to problems that are nonlinear in nature. Specifically in the area of control, many fuzzy logic controllers (FLCS) have been shown to be excellent means of control, most notably...
Control loop noise rejection using fuzzy logic.
Hay, Glen; Svrcek, William; Ross, Timothy; Young, Brent
2005-10-01
This paper describes an application of fuzzy logic to noise rejection in a control loop. This new use of fuzzy logic solves the problem of sluggish control loop response when using a set-point range to stop constant valve chattering due to noise in the output signal being sent to a control valve. Multiple related variables and a general understanding of their inter-relationship must be available for this method to be successfully applied. An overview of the specific fuzzy logic method used for this application is presented along with guidelines for the practical application. In addition, this paper includes results from the successful implementation of fuzzy logic to a control loop on a pilot plant distillation column. PMID:16294773
Performance comparison of alternative fuzzy control modalities
House, Corey Dean
1998-01-01
Through the medium of computer simulation, this thesis documents thorough performance caparisons of several different fuzzy control schemes, using a standard problem in the literature, the inverted pendulum. In order to have a firm basis...
Fuzzy control of pH using genetic algorithms
Charles L. Karr; Edward J. Gentry
1993-01-01
Abstruct- Establishing suitable control of pH, a requirement in a number of mineral and chemical industries, poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing such systems. In this technique, a genetic
Fuzzy modeling and control of HIV infection.
Zarei, Hassan; Kamyad, Ali Vahidian; Heydari, Ali Akbar
2012-01-01
The present study proposes a fuzzy mathematical model of HIV infection consisting of a linear fuzzy differential equations (FDEs) system describing the ambiguous immune cells level and the viral load which are due to the intrinsic fuzziness of the immune system's strength in HIV-infected patients. The immune cells in question are considered CD4+ T-cells and cytotoxic T-lymphocytes (CTLs). The dynamic behavior of the immune cells level and the viral load within the three groups of patients with weak, moderate, and strong immune systems are analyzed and compared. Moreover, the approximate explicit solutions of the proposed model are derived using a fitting-based method. In particular, a fuzzy control function indicating the drug dosage is incorporated into the proposed model and a fuzzy optimal control problem (FOCP) minimizing both the viral load and the drug costs is constructed. An optimality condition is achieved as a fuzzy boundary value problem (FBVP). In addition, the optimal fuzzy control function is completely characterized and a numerical solution for the optimality system is computed. PMID:22536298
Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach
NASA Astrophysics Data System (ADS)
Prato, Tony
2011-07-01
Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods.
Adaptively managing wildlife for climate change: a fuzzy logic approach.
Prato, Tony
2011-07-01
Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods. PMID:21374089
Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.
Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu
2015-05-01
This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems. PMID:25137736
Adaptive Fuzzy Segmentation of Magnetic Resonance Images
Dzung L. Pham; Jerry L. Prince
1999-01-01
An algorithm is presented for the fuzzy segmentation of two and three-dimensionalmultispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities,also known as shading artifacts. The algorithm is an extension of the two-dimensionaladaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. Thisalgorithm models the intensity inhomogeneities as a gain field that causes image intensities
Learning and tuning fuzzy logic controllers through reinforcements
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap
1992-01-01
A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.
Terminology and concepts of control and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan
1990-01-01
Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.
Implementation of a fuzzy-based level control using SCADA
Zafer Aydogmus
2009-01-01
This paper presents a SCADA (supervisory control and data acquisition) control via PLC (programmable logic controller) for a fluid level control system with fuzzy controller. For this purpose, a liquid level control set and PLC have been assembled together. The PLC used in this work has no fuzzy module or software. The required fuzzy program algorithms are written by the
Decentralized fuzzy control of multiple nonholonomic vehicles
Driessen, B.J.; Feddema, J.T.; Kwok, K.S.
1997-09-01
This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.
Prediction of conductivity by adaptive neuro-fuzzy model.
Akbarzadeh, S; Arof, A K; Ramesh, S; Khanmirzaei, M H; Nor, R M
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
Prediction of Conductivity by Adaptive Neuro-Fuzzy Model
Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
FPGA implementation of a recurrent neural fuzzy network for on-line temperature control
Chia-feng Juang; Chao-hsin Hsu; Yuan-chang Liou
2005-01-01
FPGA implementation of a TSK-type recurrent neural fuzzy network (TRNFN) for water bath temperature control is proposed in this paper. The TRNFN is constructed from recurrent fuzzy if-then rules and is built through a concurrent structure and parameter learning. To apply TRNFN to temperature control, the direct inverse control configuration is adopted. For the on-line adaptive control objective, the implemented
Fuzzy predictive control applied to an air-conditioning system
J. M. Sousa; R. Babuška; H. B. Verbruggen
1997-01-01
A method of designing a nonlinear predictive controller based on a fuzzy model of the process is presented. The Takagi-Sugeno fuzzy model is used as a powerful structure for representing nonlinear dynamic systems. An identification technique which enables the acquisition of the fuzzy model from process measurements is described. The fuzzy model is incorporated as a predictor in a nonlinear
Variable-order fuzzy fractional PID controller.
Liu, Lu; Pan, Feng; Xue, Dingyu
2015-03-01
In this paper, a new tuning method of variable-order fractional fuzzy PID controller (VOFFLC) is proposed for a class of fractional-order and integer-order control plants. Fuzzy logic control (FLC) could easily deal with parameter variations of control system, but the fractional-order parameters are unable to change through this way and it has confined the effectiveness of FLC. Therefore, an attempt is made in this paper to allow all the five parameters of fractional-order PID controller vary along with the transformation of system structure as the outputs of FLC, and the influence of fractional orders ? and ? on control systems has been investigated to make the fuzzy rules for VOFFLC. Four simulation results of different plants are shown to verify the availability of the proposed control strategy. PMID:25440947
Fuzzy logic control of an automotive engine
George Vachtsevanos; Shehu S. Farinwata; Dimitrios K. Pirovolou
1993-01-01
A systematic fuzzy logic control design method for control of automotive engine idling speed is discussed. The method uses the direct intelligent control paradigm. The procedure is based on partitioning of the state space into small rectangles called cell groups, and quantization of the states and the available controls into finite levels or bins. Membership functions are then assigned for
Application of fuzzy controls in sewage treatment
Wei-Lu Zhu; Hui-Qiong Deng; Shuai Wang
2010-01-01
For the shortages of the traditional flow program control and time program control in the process of sewage treatment, this paper adopts a Fuzzy programming method based on PLC. The PLC and its general I\\/O module are involved in forming the Module Controller in hardware. We use the DO and the sludge reflux ratio as the main control parameters, and
Intelligent control based on fuzzy logic and neural net theory
NASA Technical Reports Server (NTRS)
Lee, Chuen-Chien
1991-01-01
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Study on electricity saving fuzzy controller of asynchronous motors based on fuzzy logic
Song Jiancheng; Li Haiying; Hao Junfang; Zhai Shengqin; Xie Hengkun
2001-01-01
A kind of electricity saving fuzzy controller (ESFC) has been developed based on fuzzy logic principle in order to save energy and obtain greater electricity efficiency for asynchronous motors during their operation and a set of fuzzy rules are set up to control the input power by adjusting the stator voltage and frequency of asynchronous motors according to the stator
Comparison between the performance of two classes of fuzzy controllers
NASA Technical Reports Server (NTRS)
Janabi, T. H.; Sultan, L. H.
1992-01-01
This paper presents an application comparison between two classes of fuzzy controllers: the Clearness Transformation Fuzzy Controller (CTFC) and the CRI-based Fuzzy Controller. The comparison is performed by studying the application of the controllers to simulation examples of nonlinear systems. The CTFC is a new approach for the organization of fuzzy controllers based on a cognitive model of parameter driven control, the notion of fuzzy patterns to represent fuzzy knowledge and the Clearness Transformation Rule of Inference (CTRI) for approximate reasoning. The approach facilitates the implementation of the basic modules of the controller: the fuzzifier, defuzzifier, and the control protocol in a rule-based architecture. The CTRI scheme for approximate reasoning does not require the formation of fuzzy relation matrices yielding improved performance in comparison with the traditional organization of fuzzy controllers.
Some remarks on adaptive neuro-fuzzy systems
Romeo Ortega; Genie Informatique
1995-01-01
Makes three remarks concerning adaptive implementations of neural networks and fuzzy systems. First, the author brings to the readers attention the fact that the potential power of these systems as function approximators is lost when, as done in recently published work, the adjustable parameters are only the linear combination weights of the basis functions. Second, the author shows that the
T. S. Mahmoud; Mohammed H. Marhaban; Tang S. Hong; Sokchoo Ng
2009-01-01
In this paper, adaptive neural fuzzy inference system (ANFIS) and fuzzy subtractive clustering method (FSCM) were used to solve non-linearity, trajectory, and interaction problems of twin rotor MIMO system (TRMS). Basically, four fuzzy logic controllers (FLC) have been proposed to match the control objectives on TRMS. The four FLCs are considered as high consumers of memory and processing time relatively.
Coordination of Excitation and Governing Control Based on Fuzzy Logic
Coordination of Excitation and Governing Control Based on Fuzzy Logic Taiyou Yong, Robert H. In this paper, we present a fuzzy logic based method for the excitation control and governing control. Fuzzy logic is applied to generate two compensating signals to modify the controls during system disturbances
Adaptive Neuro-Fuzzy Intrusion Detection Systems
Sampada Chavan; Khusbu Shah; Neha Dave; Sanghamitra Mukherjee; Ajith Abraham; Sugata Sanyal
2004-01-01
The Intrusion Detection System architecture commonly used in commercial and research systems have a number of problems that limit their configurability, scalability or efficiency. In this paper, two machine-learning paradigms, Artificial Neural Networks and Fuzzy Inference System, are used to design an Intrusion Detection System. SNORT is used to perform real time traffic analysis and packet logging on IP network
A transductive neuro-fuzzy controller: application to a drilling process.
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
2010-07-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. PMID:20659865
Fuzzy logic resource manager: real-time adaptation and self-organization
NASA Astrophysics Data System (ADS)
Smith, James F., III
2004-08-01
A fuzzy logic expert system has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The resource manager (RM) is made up of five parts, the isolated platform model, the multi-platform decision tree, the fuzzy EA model, the fuzzy parameter selection tree and the fuzzy strategy tree. The platforms are each controlled by their own copy of the RM allowing them to automatically work together, i.e., they self-organize through communication without the need of a central commander. A group of platforms working together will automatically show desirable forms of emergent behavior, i.e., they will exhibit desirable behavior that was never explicitly programmed into them. This is important since it is impossible to have a rule covering every possible situation that might be encountered. An example of desirable emergent behavior is discussed as well as a method using a co-evolutionary war game for eliminating undesirable forms of behavior. The RM"s ability to adapt to changing situations is enhanced by the fuzzy parameter selection tree. Tree structure is discussed as well as various related examples.
Genetic reinforcement learning through symbiotic evolution for fuzzy controller design.
Juang, C F; Lin, J Y; Lin, C T
2000-01-01
An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems. PMID:18244755
Neural-Network-Based Fuzzy Logic Control and Decision System
Chin-teng Lin; C. S. George Lee
1991-01-01
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training
Fuzzy control of a double-inverted pendulum
Fuyan Cheng; Guomin Zhong; Youshan Li; Zhengming Xu
1996-01-01
A high-accuracy and high-resolution fuzzy controller is designed to stabilize a double-inverted pendulum at an upright position successfully. A new idea of dealing with multivariate systems is described. The composition coefficient is gained by combining the fuzzy control theory with the optimal control theory. The fuzzy control rules of a double-inverted pendulum are given and a powerful fuzzy decision way
Ovidiu Grigore; Adriana Florescu; Alexandru Vasile; Dan Alexandru Stoichescu
The paper compares fuzzy and neuro-fuzzy designs of a duty-cycle compensation controller used to linearize the nonlinear external characteristics family of a step-down (Buck) or forward DC-DC converter that supplies DC motors. This controller is additionally introduced in high precision speed control systems. Comparison reveals the advantages of neuro-fuzzy controllers upon fuzzy controllers. A discussion on real-time implementation is also
Vector control of induction motor with fuzzy PI controller
Ichiro Miki; Naoshi Nagai; Sakae Nishiyama; Tetsuo Yamada
1991-01-01
A description is presented of the fuzzy proportional-plus-integral controller for the vector control system of an induction motor, and the performance of the system using this controller is discussed. The theoretical process of the fuzzy inference and the guide to a design of the controller are presented. This controller is applied to the laboratory model drive system with 0.75 kW
Kazuo Kiguchi; Toshio Fukuda
1997-01-01
An intelligent controller, which consists of an intelligent planner and an adaptive fuzzy neural position\\/force controller, is proposed for a robot manipulator. The proposed controller deals with the human expert knowledge and skills for planning and control. In this paper, it is applied to the task of deburring with an unknown object. The effectiveness of the proposed controller is evaluated
Toward intelligent machining: hierarchical fuzzy control for the end milling process
R. E. Haber; C. R. Peres; A. Alique; S. Ros; C. Gonzalez; J. R. Alique
1998-01-01
The difficulties in implementing adaptive and other advanced control schemes in industrial machining processes have encouraged researchers to combine the utilization of one hierarchical level, a fuzzy control algorithm, and robust sensing systems. The main idea of this paper deals with self-regulating controllers (SRCs). The control signal's scaling factor (output scaling factor) is self-regulated during the control process, and it
FPGA Implementation of Embedded Fuzzy Controllers for Robotic Applications
Santiago Sánchez-Solano; Alejandro J. Cabrera; Iluminada Baturone; Francisco J. Moreno-Velo; María Brox
2007-01-01
Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips.
PSO Optimized Fuzzy Logic Controller for Active Suspension System
K. Rajeswari; P. Lakshmi
2010-01-01
In this paper, Particle Swarm Optimization (PSO) is developed for tuning Fuzzy Logic Controller applied to Active suspension system. First the controller is designed according to Fuzzy Logic rules for disturbance rejection to reduce unwanted vehicle's motion. Then the Fuzzy Logic Controller (FLC) is optimized with PSO and Genetic Algorithm (GA) so as to obtain optimal adjustment of the scaling
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H? Filter
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H? (NH?) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH? filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H? (FANH?) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH? filter continually adjusts the higher order of the Taylor development thorough adaptive bounds (?i) and adaptive disturbance attenuation (?), which significantly increases the UAV localization performance. The results obtained using the FANH? navigation filter are compared to the NH? navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH? filter. PMID:25244587
FUZZY LOGIC CONTROL FOR AN AUTONOMOUS ROBOT
Simon, Dan
to control the robot's motion along the predefined path. The robot was first modeled in Matlab SimulinkFUZZY LOGIC CONTROL FOR AN AUTONOMOUS ROBOT Vamsi Mohan Peri Dan Simon Department of Electrical and Computer Engineering Department of Electrical and Computer Engineering Cleveland State University Cleveland
Fuzzy Sequential Control Based On Petri Nets
J. C. Pascal; R. Valette; D. Andreu
1992-01-01
Programmable Logic Controllers are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In case of simple regulation problems it could be of particular interest to introduce a notion of fuzzy events. Such an event corresponds to a continuous evolution from one state to another and results
Fuzzy-control with a PEARL-based multi-loop controller
H. J. Beestermoller; G. Thiele; J. Becker
1995-01-01
To serve the demand for fuzzy control components in automation systems, manufacturers extend the function block libraries of their multi loop and PLC software by respective function blocks. Other demands are for the openness of PLC software and its adaptation to the IEC 1131 standard which, for the first time, gives recommendations with respect to a task model and task
Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
Position control of a servopneumatic system using fuzzy compensation
Sathyanarayana, Sreenivas
2000-01-01
The position control of a servopneumatic system in the presence of stick-slip type of friction is investigated. A cost effective, model-free fuzzy compensation scheme is proposed. The fuzzy compensation scheme compensates for the friction force...
Freeway Ramp Control Based on Genetic PI and Fuzzy Logic
Xinrong Liang; Zheng Li
2008-01-01
An approach of fuzzy logic and genetic PI is proposed to regulate the number of vehicles entering a freeway entrance point. First, the freeway traffic flow model is built, and the objective of ramp metering is determined. In conjunction with the advantages of fuzzy logic in processing language information and PI control in processing error, a fuzzy-PI mix controller is
Fuzzy logic control based on neural network nonlinear prediction
Zhang Hongliang; Sun Zhiyi; Zhao Zhicheng
2008-01-01
This paper presents a new fuzzy logic control method for time invariant nonlinear system based on neural network nonlinear prediction. It includes two parts: the neural network predictive model, which predicts the nonlinear system output; the weighted fuzzy logic controllers, which do the fuzzy operation according to prediction error between set value and predictive value, and then give out the
Temperature control by hardware implemented recurrent fuzzy controller
Chia-Feng Juang; Jung-Shing Chen; Hao-Jung Huang
2004-01-01
Hardware implementation of a TSK-type recurrent fuzzy network (TRFN-H) for water bath temperature control is proposed in this paper. The TRFN-H is constructed from recurrent fuzzy if-then rules and are built on-line through concurrent structure and parameter learning. To design TRFN-H for temperature control, the direct inverse control configuration is adopted, and owing to the structure of TRFN-H, no a
Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control
NASA Astrophysics Data System (ADS)
Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel
2014-12-01
Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.
A Laboratory Testbed for Embedded Fuzzy Control
ERIC Educational Resources Information Center
Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.
2011-01-01
This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses students'…
FUZZY LOGIC CONTROL OF AC INDUCTION MOTORS
The paper discusses the fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads. lectric motors use 60% of the electrical energy generated in t...
NASA Technical Reports Server (NTRS)
Sultan, Labib; Janabi, Talib
1992-01-01
This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.
Generalizations of fuzzy linguistic control points in geometric design
NASA Astrophysics Data System (ADS)
Sallehuddin, M. H.; Wahab, A. F.; Gobithaasan, R. U.
2014-07-01
Control points are geometric primitives that play an important role in designing the geometry curve and surface. When these control points are blended with some basis functions, there are several geometric models such as Bezier, B-spline and NURBS(Non-Uniform Rational B-Spline) will be produced. If the control points are defined by the theory of fuzzy sets, then fuzzy geometric models are produced. But the fuzzy geometric models can only solve the problem of uncertainty complex. This paper proposes a new definition of fuzzy control points with linguistic terms. When the fuzzy control points with linguistic terms are blended with basis functions, then a fuzzy linguistic geometric model is produced. This paper ends with some numerical examples illustrating linguistic control attributes of fuzzy geometric models.
Gail A. Carpenter; Stephen Grossberg; David B. Rosen
1991-01-01
A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns
Introduction to n-adaptive fuzzy models to analyze public opinion on AIDS
Dr. W. B. Vasantha Kandasamy; Dr. Florentin Smarandache
2006-02-18
There are many fuzzy models like Fuzzy matrices, Fuzzy Cognitive Maps, Fuzzy relational Maps, Fuzzy Associative Memories, Bidirectional Associative memories and so on. But almost all these models can give only one sided solution like hidden pattern or a resultant output vector dependent on the input vector depending in the problem at hand. So for the first time we have defined a n-adaptive fuzzy model which can view or analyze the problem in n ways (n >=2) Though we have defined these n- adaptive fuzzy models theorectically we are not in a position to get a n-adaptive fuzzy model for n > 2 for practical real world problems. The highlight of this model is its capacity to analyze the same problem in different ways thereby arriving at various solutions that mirror multiple perspectives. We have used the 2-adaptive fuzzy model having the two fuzzy models, fuzzy matrices model and BAMs viz. model to analyze the views of public about HIV/ AIDS disease, patient and the awareness program. This book has five chapters and 6 appendices. The first chapter just recalls the definition of four fuzzy models used in this book and gives illustration of some of them. Chapter two introduces the new n-adaptive fuzzy models. Chapter three uses for the first time 2 adaptive fuzzy models to study psychological and sociological problems about HIV/AIDS. Chapter four gives an outline of the interviews. Chapter five gives the suggestions and conclusion based on our study. Of the 6 appendices four of them are C-program made to make the working of the fuzzy model simple.
PI and fuzzy logic controllers for shunt Active Power Filter--a report.
P, Karuppanan; Mahapatra, Kamala Kanta
2012-01-01
This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads. PMID:21982358
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
The simplification of fuzzy control algorithm and hardware implementation
NASA Technical Reports Server (NTRS)
Wu, Z. Q.; Wang, P. Z.; Teh, H. H.
1991-01-01
The conventional interface composition algorithm of a fuzzy controller is very time and memory consuming. As a result, it is difficult to do real time fuzzy inference, and most fuzzy controllers are realized by look-up tables. Here, researchers derive a simplified algorithm using the defuzzification mean of maximum. This algorithm takes shorter computation time and needs less memory usage, thus making it possible to compute the fuzzy inference on real time and easy to tune the control rules on line. A hardware implementation based on a simplified fuzzy inference algorithm is described.
SoPC-Based Adaptive PID Control System Design for Magnetic Levitation System
Chih-Min Lin; Ming-Hung Lin; Chun-Wen Chen
2011-01-01
This paper develops an adaptive proportional-inte- gral-derivative (APID) control system to deal with the metallic sphere position control of a magnetic levitation system (MLS), which is an intricate and highly nonlinear system. The proposed control system consists of an adaptive PID controller and a fuzzy compensation controller. The adaptive PID controller is a main tracking controller, and the parameters of
Integration of fuzzy logic based control procedures in brewing
B. O' Connor; C. Riverol; P. Kelleher; N. Plant; R. Bevan; E. Hinchy; J. D'Arcy
2002-01-01
The objective of this paper is to integrate fuzzy logic into the fermentation process in a brewery. The brewery involved is a local commercial brewery, Beamish and Crawford Brewery plc in Cork, Ireland. Our approach consists of developing a control system for a fermentation process using fuzzy logic in two stages. In the first stage the software package fuzzyTech from
The study of fuzzy PID control of dosing pump in sewerage system
Li-jun Xu; Zi Li; Jing Cheng
2012-01-01
Taking the practical needs for treating sewage resulting from cleansing recycled mulching films in Xinjiang into consideration, the study designed self-adaptive fuzzy PID controller for dosing pump in sewerage system based on the combination of PLC and MATLAB simulation software, and explained the structure of this system and its control principle, with a focus on control strategy and the determination
A self-learning fuzzy logic controller using genetic algorithms with reinforcements
Chih-Knan Chiang; Hung-Yuan Chung; Jin-Jye Lin
1997-01-01
This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even
Development of a Fuzzy Expert system based on PCS7 and FuzzyControl++ Cement Mill control
L. Hayet Mouss; Sonia Benaicha
The basic idea of this work was to study the application of expert systems and fuzzy logic in the field of diagnostic and industrial maintenance. For this, a fuzzy expert system designed, developed and simulated in Ain Touta cement society in Batna in the East of Algeria. Dedicated to control cement mill. The application of fuzzy logic and expert systems
Fuzzy implementation of direct self-control of induction machines
Sayeed A. Mir; Malik E. Elbuluk; Donald S. Zinger
1994-01-01
A system with fast torque response is very beneficial in applications where direct torque control is highly desirable. The response of direct self control is slower during start-up and during change in command torque. Fuzzy control is used for the implementation of direct self control to improve its slow response. Experimental implementation of the fuzzy logic controller was carried out
Mine Water Level Fuzzy Control System Design Based on PLC
Guimei Wang; Hui Song; Qingna Niu
2009-01-01
In the coal mine water level control systems, because the variables are non-linear, time lagged and uncertain, it is not possible to establish the mathematical model precisely. This article combines the intelligent control with the traditional automatic control device. According to the principle of the fuzzy control and the characteristic of PLC, designing the mine water level fuzzy control system,
Maximum entropy approach to fuzzy control
NASA Technical Reports Server (NTRS)
Ramer, Arthur; Kreinovich, Vladik YA.
1992-01-01
For the same expert knowledge, if one uses different &- and V-operations in a fuzzy control methodology, one ends up with different control strategies. Each choice of these operations restricts the set of possible control strategies. Since a wrong choice can lead to a low quality control, it is reasonable to try to loose as few possibilities as possible. This idea is formalized and it is shown that it leads to the choice of min(a + b,1) for V and min(a,b) for &. This choice was tried on NASA Shuttle simulator; it leads to a maximally stable control.
Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management
NASA Astrophysics Data System (ADS)
Chang, N.
2006-12-01
The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals of sustainable development on a regional scale.
Kun Zhou; Yanmin Song; Xintian Shen; Yurong Yan; Wen Wang
2008-01-01
Taking the electronic controlled weighing system of fancy soap production line of Tianjin Procter & Gamble Company as an example, the paper adopted fuzzy control technique and studied the fuzzy controller realized on the basis of SIMENS PLC. The paper also analyzed fuzzed input, fuzzy control decision of directly table-looking up method and the de-fuzzy of fuzzy control variables and
Wen-Jer Chang; Min-Wei Chen; Cheung-Chieh Ku
2009-01-01
This paper deals with steering control problem of a computer simulated model car with fuzzy control approach. For simulating the real environment, the stochastic behavior of system is considered as multiplicative noise term. In addition, the external disturbance effect on system is also discussed for achieving attenuation performance by applying passivity theory. Through using the Takagi-Sugeno (T-S) fuzzy model approach,
Xiaodong Liu; Qingling Zhang
2003-01-01
The problems of relaxed quadratic stability conditions, fuzzy observer designs and H? controller designs for T-S fuzzy systems have been studied. First new stability conditions are obtained by relaxing the stability conditions derived in previous papers. Secondly, new fuzzy observers based on the relaxed stability conditions for the T-S fuzzy systems have been proposed. Thirdly two sufficient LMI conditions, which
Fuzzy control of a class of hydraulically actuated industrial robots
T. Corbet; N. Sepehri; P. D. Lawrence
1996-01-01
Application of a fuzzy logic controller to a class of hydraulically actuated industrial robots is investigated. A simple set of membership functions and rules are described which meets certain control requirements. An off-line routine based on the simplex method is outlined to tune the controller gains for an optimum response. The fuzzy control gains are tuned by minimizing the summation
An Interval Fuzzy Controller for Vehicle Active Suspension Systems
Jiangtao Cao; Ping Li; Honghai Liu
2010-01-01
A novel interval type-2 fuzzy controller architecture is proposed to resolve nonlinear control problems of vehicle active suspension systems. It integrates the Takagi-Sugeno (T-S) fuzzy model, interval type-2 fuzzy reasoning, the Wu-Mendel uncertainty bound method, and selected optimization algorithms together to construct the switching routes between generated linear model control surfaces. The stability analysis of the proposed approach is presented.
Application of an adaptive fuzzy system to clustering and pattern recognition
NASA Astrophysics Data System (ADS)
Newton, Scott C.; Mitra, Sunanda
1992-02-01
This paper presents a modular, unsupervised neural network architecture which can be used for clustering and classification of complex data sets. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a conventional fuzzy K-means clustering algorithm as a learning rule embedded within a control structure similar to that found in the adaptive resonance theory (ART-1) network. AFLC adaptively clusters analog inputs into classes without a priori knowledge of the entire data set or of the number of clusters present in the data. The classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. It is shown that the definition of the distance metric can be adjusted as necessary to fit the characteristics of the input data. The AFLC algorithm using two different distance definitions is discussed and then the operating characteristics are described. The performance of the algorithm is presented through application of the algorithm to clustering computer generated normally distributed data, the Anderson & Fisher Iris data, and data generated from projections of 3-D objects in constrained motion.
A fuzzy logic controller for aircraft flight control
Lawrence I. Larkin
1984-01-01
This paper describes a model of an autopilot controller based on fuzzy algorithms. The controller maneuvers an aircraft from level flight into a final-approach flight path and maintains the aircraft along the glide path until just before touchdown. To evaluate the performance and effectiveness of the model, the aircraft response to controller actions is simulated using flight simulation techniques. The
Active structural control by fuzzy logic rules: An introduction
Tang, Y.
1995-07-01
An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.
Active structural control by fuzzy logic rules: An introduction
Tang, Yu [Argonne National Lab., IL (United States). Reactor Engineering Div.; Wu, Kung C. [Texas Univ., El Paso, TX (United States). Dept. of Mechanical and Industrial Engineering
1996-12-31
A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.
Fuzzy Explicit Marking for Congestion Control in Differentiated Services Networks
Chrysostomos Chrysostomou; Andreas Pitsillides; George Hadjipollas; Y. Ahmet Sekercioglu; Marios M. Polycarpou
2003-01-01
This paper presents a new active queue management scheme, Fuzzy Explicit Marking (FEM), implemented within the differentiated services (Diff-Serv) framework to provide congestion control using a fuzzy logic control approach. Network congestion control remains a critical and high priority issue. The rapid growth of the Internet and increased demand to use the Internet for time- sensitive voice and video applications
Self-Learning Fuzzy Controllers Based on Temporal Back Propagation
J. sr. Jang
1992-01-01
This paper presents a generalized control strategy that enhances fuzzy controllers with selflearningcapability for achieving prescribed control objectives in a near-optimal manner. Thismethodology, termed temporal back propagation, is model-insensitive in the sense that it candeal with plants that can be represented in a piecewise differentiable format, such as differenceequations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of
Fuzzy controlled fast charging system for lithium-ion batteries
Ming-Wang Cheng; Shih-Ming Wang; Yuang-Shung Lee; Sung-Hsin Hsiao
2009-01-01
A DSP is adopted to construct a fuzzy controlled lithium-ion battery charging system. By using this intelligent charging system, the data collection, calculation and peripheral circuit control are performed for the battery charging status. According to the lithium-ion battery charging specifications, two hours are required for battery charging. A fuzzy logic controller (FLC) is constructed by using the battery protection
A design methodology of constraint-based fuzzy logic controller
David Chiang; Robert Lai
2001-01-01
Constraint-based fuzzy logic controllers (CFLCs) have been recognized as a unified framework of control system design. Arbitrary types of constraints are allowed in a CFLC to specify the desired states of a plant. The purpose of this paper is to present a design methodology of CFLCs, transforming the intention of a control system into a network of fuzzy constraints. The
Membership function modification of fuzzy logic controllers with histogram equalization
Hanqi Zhuang; Xiaomin Wu
2001-01-01
In most fuzzy logic controllers (FLCs), initial membership functions (MFs) are normally laid evenly all across the universes of discourse (UD) that represent fuzzy control inputs. However, for evenly distributed MFs, there exists a potential problem that may adversely affect the control performance; that is, if the actual inputs are not equally distributed, but instead concentrate within a certain interval
Fuzzy logic assisted manual control of joystick operated hydraulic crane
Esa NIEMELA; Tapio VIRVALO
1994-01-01
Fuzzy logic has been applied widely in various closed-loop control systems. In the case of a hydraulic mobile crane, the operator often has many mechanical manual valve lever arms to handle simultaneously. In this paper, it is shown how by using a fuzzy logic controller, the operator's task of controlling independent booms can be reduced using an electrically operated joystick
Discrete-time optimal fuzzy controller design: global concept approach
Shinq-Jen Wu; Chin-Teng Lin
2002-01-01
Proposes a systematic and theoretically sound way to design a global optimal discrete-time fuzzy controller to control and stabilize a nonlinear discrete-time fuzzy system with finite or infinite horizon (time). A linear-like global system representation of a discrete-time fuzzy system is first proposed by viewing such a system in a global concept and unifying the individual matrices into synthetic matrices.
Application of fuzzy sliding-mode control in robot
NASA Astrophysics Data System (ADS)
Fu, Yongling; Wang, Yan
2006-11-01
The system of Pb-211 robot waist is a nonlinear hydraulic servo control system. It is very difficult to achieve speedy response without overshoot by the PID control algorithm for the system control. To improve the performance of the system, a new controller is designed with a fuzzy sliding-mode control algorithm, which makes use of the merits both the fuzzy control and the sliding-mode control algorithm. The simulation results show that the new controller is effective, which can achieve high speediness and steady accuracy without overshoot. The fuzzy sliding-mode control has obvious advantage compared the traditional PID algorithm, and it has strong robust too.
Modal control of a plate using a fuzzy logic controller
NASA Astrophysics Data System (ADS)
Sharma, Manu; Singh, S. P.; Sachdeva, B. L.
2007-08-01
This paper presents fuzzy logic based independent modal space control (IMSC) and fuzzy logic based modified independent modal space control (MIMSC) of vibration. The rule base of the controller consists of nine rules, which have been derived based upon simple human reasoning. Input to the controller consists of the first two modal displacements and velocities of the structure and the output of the controller is the modal force to be applied by the actuator. Fuzzy logic is used in such a way that the actuator is never called to apply effort which is beyond safe limits and also the operator is saved from calculating control gains. The proposed fuzzy controller is experimentally tested for active vibration control of a cantilevered plate. A piezoelectric patch is used as a sensor to sense vibrations of the plate and another piezoelectric patch is used as an actuator to control vibrations of the plate. For analytical formulation, a finite element method based upon Hamilton's principle is used to model the plate. For experimentation, the first two modes of the plate are observed using a Kalman observer. Real-time experiments are performed to control the first mode, the second mode and both modes simultaneously. Experiments are also performed to control the first mode by IMSC, the second mode by IMSC and both modes simultaneously by MIMSC. It is found that for the same decibel reduction in the first mode, the voltage applied by the fuzzy logic based controller is less than that applied by IMSC. While controlling the second mode by IMSC, a considerable amount of spillover is observed in the first mode and region just after the second mode, whereas while controlling the second mode by fuzzy logic, spillover effects are much smaller. While controlling two modes simultaneously, with a single sensor/actuator pair, appreciable resonance control is observed both with fuzzy logic based MIMSC as well as with direct MIMSC, but there is a considerable amount of spillover in the off-resonance region. This may be due to the sub-optimal location and/or an insufficient number of actuators. So, another smart plate with two piezoelectric actuators and one piezoelectric sensor is considered. Piezoelectric patches are fixed in an area where modal strains are high. With this configuration of the smart plate, experiments are conducted to control the first three modes of the plate and it is found that spillover effects are greatly reduced.
Development of fuzzy process control charts and fuzzy unnatural pattern analyses
Murat Gülbay; Cengiz Kahraman
2006-01-01
Many problems in scientific investigation generate nonprecise data incorporating nonstatistical uncertainty. A nonprecise observation of a quantitative variable can be described by a special type of membership function defined on the set of all real numbers called a fuzzy number or a fuzzy interval. A methodology for constructing control charts is proposed when the quality characteristics are vague, uncertain, incomplete
Phase control of fiber optic ESPI using fuzzy PID controller
NASA Astrophysics Data System (ADS)
Park, Hyoung Jun; Song, Minho
2005-02-01
We propose an efficient phase stabilization/shifting technique for the use in fiber-optic ESPI system by using a Fuzzy PID controller. To obtain required phase steps between the CCD captured speckle patterns, we implemented a Fuzzy-logic-based PID controller which is known as more suitable for nonlinear, time-delayed, and vague systems. Phase steps with a quarter-wave phase difference, which are required for four phase step methods, are continuously generated by a closed-loop switching and a synchronization signal. From the experimental results, the Fuzzy controller system has shown the faster and more accurate phase stabilization and continuously generated the four phase shifting in the presence of ambient temperature drift and vibration.
Fuzzy Delay Compensation Control for T-S Fuzzy Systems Over Network.
Zhang, Jinhui; Shi, Peng; Xia, Yuanqing
2012-07-11
This paper is concerned with the network delay compensation problem for nonlinear networked control systems (NCSs). By taking full advantage of the characteristics of the packet-based transmission in NCSs, new network delay compensation approaches are proposed to actively compensate the network communication delay under the fuzzy control framework. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the predictive control input packets are constructed based on parallel distributed compensation technique. Both state and output feedback fuzzy delay compensation controllers are designed. Finally, two examples are provided to illustrate the effectiveness and applicability of the developed techniques. PMID:22801520
Fuzzy logic control for an automated guided vehicle
NASA Astrophysics Data System (ADS)
Cao, Ming; Hall, Ernest L.
1998-10-01
This paper describes the use of fuzzy logic control for the high level control systems of a mobile robot. The advantages of the fuzzy logic system are that multiple types of input such as that from vision and sonar sensors as well as stored map information can be used to guide the robot. Sensor fusion can be accomplished between real time sensed information and stored information in a manner similar to a human decision maker. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected through a commercial tracking device, communicating to the computer the X,Y coordinates of a lane marker. Testing of these systems yielded positive results by showing that at five miles per hour, the vehicle can follow a line and avoid obstacles. The obstacle detection uses information from Polaroid sonar detection system. The motor control system uses a programmable Galil motion control system. This design, in its modularity, creates a portable autonomous controller that could be used for any mobile vehicle with only minor adaptations.
Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models
Lin, Chien-Chuan; Wang, Ming-Shi
2012-01-01
A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650
NASA Technical Reports Server (NTRS)
Kreinovich, Vladik YA.; Quintana, Chris; Lea, Robert
1991-01-01
Fuzzy control has been successfully applied in industrial systems. However, there is some caution in using it. The reason is that it is based on quite reasonable ideas, but each of these ideas can be implemented in several different ways, and depending on which of the implementations chosen different results are achieved. Some implementations lead to a high quality control, some of them not. And since there are no theoretical methods for choosing the implementation, the basic way to choose it now is experimental. But if one chooses a method that is good for several examples, there is no guarantee that it will work fine in all of them. Hence the caution. A theoretical basis for choosing the fuzzy control procedures is provided. In order to choose a procedure that transforms a fuzzy knowledge into a control, one needs, first, to choose a membership function for each of the fuzzy terms that the experts use, second, to choose operations of uncertainty values that corresponds to 'and' and 'or', and third, when a membership function for control is obtained, one must defuzzy it, that is, somehow generate a value of the control u that will be actually used. A general approach that will help to make all these choices is described: namely, it is proved that under reasonable assumptions membership functions should be linear or fractionally linear, defuzzification must be described by a centroid rule and describe all possible 'and' and 'or' operations. Thus, a theoretical explanation of the existing semi-heuristic choices is given and the basis for the further research on optimal fuzzy control is formulated.
Study on Fuzzy Algorithm of Elevator Group Control System
Gu Deying; Yan Dongmei
2010-01-01
Elevator group control system is a complex, random, multi-objective, non-linear, uncertain decision-making problem. Fuzzy control algorithms are suitable for Elevator group control system because its algorithms are multiobjective. The algorithms optimize the elevator group control harmoniously. Simulation mode of elevator group control system is established in MATLAB in light of the property of elevator group control system and Fuzzy Control
Fuzzy logic control of AC induction motors
NASA Astrophysics Data System (ADS)
Cleland, J.; Turner, W.; Wang, P.; Espy, T.; Chappell, P. J.
The fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads is discussed. Electric motors use 60 percent of the electrical energy generated in the U.S. An improvement of 1 percent in operating efficiency of all electric motors could result in savings of 17 billion kWh per year in the U.S. New techniques are required to extract maximum performance from modern motors. One possibility, FLC, has recently demonstrated success in solving control problems of nonlinear, multivariable systems such as ac induction motors and adjustable motor-speed drives. Simulated results of a microprocessor-based fuzzy logic motor controller (FLMC) are described. The investigation includes a motor stator voltage control scheme to minimize motor input power at specified speed/torque conditions; simulation of ac motor performance; and development of a FLMC for optimized motor efficiency. Simulated FLMC results compare favorably with other motor control approaches. Potential energy savings are quantitated based on the preliminary predictions of FLMC performance.
Passive fuzzy controller design for nonlinear systems with multiplicative noises
Cheung-Chieh Ku; Pei-Hwa Huang; Wen-Jer Chang
2010-01-01
This paper proposes a passive fuzzy controller design methodology for nonlinear system with multiplicative noises. Applying the Itô's formula and the sense of mean square, the sufficient conditions are developed to analyze the stability and to design the controller for stochastic nonlinear systems which are represented by the Takagi–Sugeno (T–S) fuzzy models. The sufficient conditions derived in this paper belong
Generalized predictive control using fuzzy neural network model
Jong-Hwan Kim; Jeong-Yeol Jeon; Jeong-Min Yang; Hong-Kook Chae
1994-01-01
This paper presents a generalized predictive control using fuzzy neural network (FNN) modeling for nonlinear systems. FNN identifies the fuzzy model of a nonlinear system, and this model is used in the design of the generalized predictive controller. The effectiveness of the scheme is demonstrated by computer simulations
Parameterized linear matrix inequality techniques in fuzzy control system design
H. D. Tuan; P. Apkarian; T. Narikiyo; Y. Yamamoto
2001-01-01
This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations
Fuzzy control of εvarying singularly perturbed systems
Akram M. Fayaz
2000-01-01
Deals with the problem of singularly perturbed systems when the parameter ε, representing the ratio speed between the fast and slow dynamics, is time-varying. We show that using an adequate fuzzy partition of ε, we can combine techniques of fuzzy control and singular perturbations to control the system. The parameter ε, may be only partially known (known only on some
A fuzzy logic controller application for thermal power plants
?lhan Kocaarslan; Ertu?rul Çam; Hasan Tiryaki
2006-01-01
This study presents a fuzzy logic based control technique to regulate the power and enthalpy outputs in a boiler of a 765MW coal fired thermal power plant. An approximate mathematical model of the thermal power plant was developed by using real time data on Computer Aided Design and Control (CADACS) software. Conventional proportional, integral and derivative (PID), fuzzy logic (FL)
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
ERIC Educational Resources Information Center
Mamdani, E. H.; Assilian, S.
1975-01-01
This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)
A linear output structure for fuzzy logic controllers
Hailong Sun; Lixiang Liu
2002-01-01
In this paper, the output structure of multiple-input–single-output fuzzy logic controller (FLCr) is studied. The input and output variables of the FLCr are all characterized by normal triangular-shaped membership functions and fuzzy partitions are considered for corresponding universes of discourse. A special mapping, linear rule mapping [(Fuzzy Sets and Systems 57 (1993) 149)], which describes the relationship between the input
Fuzzy Neural Network Control in Automatic Transmission of Construction Vehicle
Shijing Wu; Hongshan Lu
2006-01-01
The control of off-line fuzzy neural network can realize the shift of multi-parameter automatic transmission in construction vehicles. The method is based on fuzzy look-up table database. The gear shifting dynamically acts according to the shift database. The database was established by training the fuzzy neural network and was save in advance. The shift schedule table is saved in the
Adaptive Control Strategies for Flexible Robotic Arm
NASA Technical Reports Server (NTRS)
Bialasiewicz, Jan T.
1996-01-01
The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.
Self-learning fuzzy controllers based on temporal back propagation
NASA Technical Reports Server (NTRS)
Jang, Jyh-Shing R.
1992-01-01
This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.
Hierarchical fuzzy control of low-energy building systems
Yu, Zhen; Dexter, Arthur [Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ (United Kingdom)
2010-04-15
A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profile can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)
An Adaptive Fuzzy Model for Failure Rates of Overhead Distribution Feeders
Shalini Gupta; Anil Pahwa; Yujia Zhou; Sanjoy Das; Richard E. Brown
2005-01-01
This article presents the development of an adaptive-fuzzy model to predict the failure rate of overhead distribution feeders based on factors such as tree density, tree trimming, lightning intensity and wind index. A gradient descent method was used to train the fuzzy model. To check performance of the model, two error terms, the root mean square error (RMSE) and absolute
Fuzzy position control of hydraulic robots with valve deadbands
N. Sepehri; T. Corbet; P. D. Lawrence
1995-01-01
The development of a real-time fuzzy-logic controller for a class of industrial hydraulic robots is described. The main element of the controller is a PD-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. The controller, although effective, is shown to produce steady-state errors. The steady-state error
Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan
2013-06-01
The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently. PMID:23757441
Design of fuzzy neural network based control system for cement rotary kiln
Zheng Li
2010-01-01
This paper presents a fuzzy neural network control system for the process of cement production with rotary cement kiln. Since the dynamic characteristics and reaction process parameters are with large inertia, pure hysteresis, nonlinearity and strong coupling, a fuzzy neural network controller combining both the advantages of neural network and fuzzy control is applied. This fuzzy neural network controller adjusts
Type-2 fuzzy model based controller design for neutralization processes.
Kumbasar, Tufan; Eksin, Ibrahim; Guzelkaya, Mujde; Yesil, Engin
2012-03-01
In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures. PMID:22036014
Fuzzy attitude control for a nanosatellite in leo orbit
NASA Astrophysics Data System (ADS)
Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria; Aviles, Taisir
Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In this work, a tailored fuzzy controller is designed for a nanosatellite and is compared with a traditional Proportional Integrative Derivative (PID) controller. Both control methodologies are compared within the same specific mission. The orbit height varies along the mission from injection at around 380 km down to a 200 km height orbit, and the mission requires pointing accuracy over the whole time. Due to both the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, a robust and efficient ADCS is required. For these reasons a fuzzy logic controller is implemented as the brain of the ADCS and its performance and efficiency are compared to a traditional PID. The fuzzy controller is designed in three separated controllers, each one acting on one of the Euler angles of the satellite in an orbital frame. The fuzzy memberships are constructed taking into account the mission requirements, the physical properties of the satellite and the expected performances. Both methodologies, fuzzy and PID, are fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. Finally both methods are probed in different environments to test their characteristics. The simulations show that the fuzzy controller is much more efficient (up to 65% less power required) in single maneuvers, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. A brief mission description is depicted as well as the design process of both ADCS controllers. Finally the validation process and the results obtained during the simulations are described. Those results show that the fuzzy logic methodology is valid for small satellites' missions benefiting from a well-developed artificial intelligence theory.
Modal-space reference-model-tracking fuzzy control of earthquake excited structures
NASA Astrophysics Data System (ADS)
Park, Kwan-Soon; Ok, Seung-Yong
2015-01-01
This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.
Fuzzy logic control of a solar power plant
Francisco R. Rubio; Manuel Berenguel; Eduardo F. Camacho
1995-01-01
This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy
Fuzzy Petri net Implementation for Programmable Logic Controllers
P. r. Venkateswaran; Jayadev Bhat; V. i. George
2006-01-01
Abstract The concept of fuzzy reasoning has been extended to Petri nets and has been ,applied for modeling ,of discrete event systems [1]. However, the theory was not extended to supervisory,control of discrete event systems,because ,there is no ,valid translation for fuzzy Petri nets into ladder logic diagrams. Discrete event systems ,are time and event ,dependent ,and hence digital in
Hierarchical genetic fuzzy controller for a solar power plant
J. Y. Ke; K. S. Tang; K. F. Man; P. C. K. Luk
1998-01-01
To regulate the significant variations of the dynamic characteristics of the distributed collector field in a solar power plant, in this paper, a unique hierarchical genetic algorithm (HGA) has been employed for the design and optimization of the fuzzy logic controller for this purpose. This method not only fulfils the required performance, but also minimises the number of fuzzy membership
Fuzzy control of the compressor speed in a refrigeration plant
C. Aprea; R. Mastrullo; C. Renno
2004-01-01
In this paper, referring to a vapor compression refrigeration plant subjected to a commercially available cold store, a control algorithm, based on the fuzzy logic and able to select the most suitable compressor speed in function of the cold store air temperature, is presented. The main aim is to evaluate the energy saving obtainable when the fuzzy algorithm, which continuously
Advance of Systematic Design Methods on Fuzzy Control
Zhang, J.; Chen, Y.
2006-01-01
The heating, ventilation and air-conditioning (HVAC) system possesses some characteristics such as multi-parameters, nonlinear, and coupled parameters. Aimed at control problems, the author targets real-time fuzzy control and research systematically...
Fuzzy logic controllers: A knowledge-based system perspective
NASA Technical Reports Server (NTRS)
Bonissone, Piero P.
1993-01-01
Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.
FzController: A Development Environment for Fuzzy Controllers
I. Alvarez-López; O. Llanes-Santiago; J. L. Verdegay
This chapter presents a general purpose development environment that allows an easy and friendly specification, verification\\u000a and synthesis of fuzzy controllers. This CAD tool also allows the real-time control of systems with proper constant of time\\u000a and it contains the necessary tools for the signal processing. Among the distinctive characteristics of this system is the\\u000a possibility for users to define
Distributed traffic signal control using fuzzy logic
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1992-01-01
We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.
Self-tuning fuzzy-based dispatching strategy for elevator group control systems
Jafferi Jamaludin; Nasrudin Abdul Rahim; Hew Wooi Ping
2008-01-01
Fuzzy logic is recognized as an intelligent approach to complex problems demanding fulfillment of several criteria. A smart solution for an elevator group control system is one complex problem. This paper discusses a fuzzy logic dispatching strategy for elevator group control that uses a self-tuning fuzzy logic controller to tune membership functions and adjust fuzzy rule sets. Computer simulation evaluates,
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665
Fuzzy logic applications to expert systems and control
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
On-line Writer Adaptation for Handwriting Recognition using Fuzzy Inference Harold Mouch`ere
Paris-Sud XI, UniversitÃ© de
On-line Writer Adaptation for Handwriting Recognition using Fuzzy Inference Systems Harold Mouch present an automatic on-line adaptation mechanism to the writer's handwriting style for the recognition to the handwriting style of the writer that is currently using the system. This adaptation mechanism has been tested
Fuzzy support vector machines for adaptive Morse code recognition
Cheng-Hong Yang; Li-Cheng Jin; Li-Yeh Chuang
2006-01-01
Morse code is now being harnessed for use in rehabilitation applications of augmentative–alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code
Generalized predictive control of a thermal plant using fuzzy model
Drago Matko; K. Kavsek-Biasizzo; I. Skrjanc
2000-01-01
A new approach to predictive control of highly nonlinear processes based on Takagi-Sugeno fuzzy model is proposed. It is investigated how the Takagi-Sugeno fuzzy models can be linked to a special type of model based predictive control algorithm, the generalized predictive control (GPC). In original GPC design purely linear transfer function model is used for long-range prediction. The advantage of
Parameter control of metaheuristics with genetic fuzzy systems
Vitor Marques; Fernando Gomide
This paper introduces a genetic fuzzy system for parameter control of metaheuristics. Two basic metaheuristics have been considered\\u000a as examples, genetic algorithm and tabu search. The controlled parameters of the tabu search are the short and long term memories.\\u000a Parameters of the genetic algorithm under control are the mutation and reproduction rates. Fuzzy rule-based models offer a\\u000a natural mechanism to
Helicopter flight control with fuzzy logic and genetic algorithms
Greg Walker
1996-01-01
Researchers at the U.S. Bureau of Mines, in conjunction with researchers at the University of Alabama and the U.S. Army, have developed a fuzzy system for controlling the flight of UH-1 helicopters through various maneuvers. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over
A new fuzzy approach for swing up control of Pendubot
Xiao Qing Ma; Chun-Yi Su
2002-01-01
This paper studies the swing up and balancing control problems for an under-actuated robot, Pendubot, from the point view of fuzzy logic control. To swing up the Pendubot from a rest position to the upright configuration, a fuzzy algorithm is proposed, where a simplified Tsukamoto's reasoning method and quasi-linear-mean aggregating operators are used to derive and analyze the controller input-output
Fuzzy Control of Large Civil Structures Subjected to Natural Hazards
Yeesock Kim; Stefan Hurlebaus; Reza Langari
In this chapter, a new semiactive nonlinear fuzzy control (SNFC) system design framework is proposed through integration of\\u000a a set of Lyapunov-based state feedback controllers and Kalman filters. A nonlinear multi-input multi-output (MIMO) autoregressive\\u000a exogenous (ARX) Takagi-Sugeno (T-S) fuzzy model is constructed out of a set of linear dynamic models. Subsequently, multiple\\u000a Lyapunovbased state feedback controllers are formulated in terms
NASA Astrophysics Data System (ADS)
Zhang, Mei; Zheng, Meng; Li, Yanqiu
2013-12-01
A variable universe fuzzy PID algorithm is designed to control the misalignment of the lithography projection optics to meet the requirement of high image quality. This paper first simulates the alignment of Schwarzschild objective designed by us. Secondly, the variable universe fuzzy PID control is introduced to feed back the misalignment of Schwarzschild objective to the control system to drive the stage which holds the objective. So the position can be adjusted automatically. This feedback scheme can adjust the variables' universe self-adaptively by using fuzzy rules so that the concrete function and parameters of the contraction-expansion factor are not necessary. Finally, the proposed approach is demonstrated by simulations. The results show that, variable universe fuzzy PID method exhibits better performance in both improving response speed and decreasing overshoot compared to conventional PID and fuzzy PID control methods. In addition, the interference signal can be effectively restrained. It is concluded that this method can improve the dynamic and static properties of system and meet the requirement of fast response.
A simulation of PLC-based self-tuning PI - fuzzy logic controller for DC motor
Muhammad Arrofiq; Nordin Saad
2008-01-01
This paper presents simulation of a PLC-based self-tuning PI-fuzzy controller for DC motor speed control. The controller consists of two fuzzy logic blocks, main and gain tuning, respectively. The main fuzzy block acts as a speed controller, while the gain tuning block scales the output of main fuzzy. The gain tuning gets same inputs as main fuzzy (i.e. speed error
Power control of SAFE reactor using fuzzy logic
NASA Astrophysics Data System (ADS)
Irvine, Claude
2002-01-01
Controlling the 100 kW SAFE (Safe Affordable Fission Engine) reactor consists of design and implementation of a fuzzy logic process control system to regulate dynamic variables related to nuclear system power. The first phase of development concentrates primarily on system power startup and regulation, maintaining core temperature equilibrium, and power profile matching. This paper discusses the experimental work performed in those areas. Nuclear core power from the fuel elements is simulated using resistive heating elements while heat rejection is processed by a series of heat pipes. Both axial and radial nuclear power distributions are determined from neuronic modeling codes. The axial temperature profile of the simulated core is matched to the nuclear power profile by varying the resistance of the heating elements. The SAFE model establishes radial temperature profile equivalence by establishing 32 control zones as the nodal coordinates. Control features also allow for slow warm up, since complete shutoff can occur in the heat pipes if heat-source temperatures drop/rise below a certain minimum value, depending on the specific fluid and gas combination in the heat pipe. The entire system is expected to be self-adaptive, i.e., capable of responding to long-range changes in the space environment. Particular attention in the development of the fuzzy logic algorithm shall ensure that the system process remains at set point, virtually eliminating overshoot on start-up and during in-process disturbances. The controller design will withstand harsh environments and applications where it might come in contact with water, corrosive chemicals, radiation fields, etc. .
Elevator group control system tuned by a fuzzy neural network applied method
Naoki Imasaki; S. Kubo; S. Nakai; T. Yoshitsugu; Jun-Ichi Kiji; Tsunekazu Endo
1995-01-01
We have developed a high-performance elevator group control system with a performance tuning function, which employs a fuzzy neural network as a performance forecasting model of the elevator system. The fuzzy neural network, which is a structured neural network based on a fuzzy reasoning framework, stores the correlation between control-parameters and the response of the elevator group as a fuzzy
Controlling Chaos for BLDC Thruster Motor in Deepwater Robot Based on Fuzzy Control
Zhaojun Meng; Changzhi Sun; Yuejun An; Hongmin Yang; Qing Hu
2007-01-01
The method of solving the time-delayed parameter difficult choice in controlling chaos was proposed in this paper. The fuzzy control method was applied into chaotic control by time-delayed feedback. The quadrature-axis stator voltage was used as the manipulated variable. The input of the fuzzy controller was the system error. Fuzzy controller searched the optimum time parameter according to the error.
MAXIMUM ENTROPY APPROACH TO FUZZY CONTROL Arthur Ramer
Kreinovich, Vladik
MAXIMUM ENTROPY APPROACH TO FUZZY CONTROL Arthur Ramer School of CSE, University of New South Wales from processing physical data to processing uncertainties in expert systems [J79, KK79, C83, K89]. Just
Fuzzy Control of Stochastic Global Optimization Algorithms and Very ...
Finance , Management , Medicine , etc.. In practical ... suited to applications involving neuro-fuzzy systems and neural ... parameter spaces , when computational resources are scarce. .... controller) that does nothing more than emulate human.
Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants
Sudath R. Munasinghe; Min-Soeng Kim; Ju-Jang Lee
2005-01-01
A data-driven adaptive neurofuzzy controller is presented for the water-level control of U-tube steam generators in nuclear power plants. This neurofuzzy controller is capable of learning the control action principles from the data obtained using other methods of automatic or manual control. There are four inputs in the neurofuzzy system, yet only eighty fuzzy rules involved. Therefore, the fuzzy system
Nonlinear rescaling of control values simplifies fuzzy control
NASA Technical Reports Server (NTRS)
Vanlangingham, H.; Tsoukkas, A.; Kreinovich, V.; Quintana, C.
1993-01-01
Traditional control theory is well-developed mainly for linear control situations. In non-linear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts' knowledge is usually represented in non-numeric terms, namely, in terms of uncertain statements of the type 'if the obstacle is straight ahead, the distance to it is small, and the velocity of the car is medium, press the brakes hard'. Fuzzy control is a methodology that translates such statements into precise formulas for control. The necessary first step of this strategy consists of assigning membership functions to all the terms that the expert uses in his rules (in our sample phrase these words are 'small', 'medium', and 'hard'). The appropriate choice of a membership function can drastically improve the quality of a fuzzy control. In the simplest cases, we can take the functions whose domains have equally spaced endpoints. Because of that, many software packages for fuzzy control are based on this choice of membership functions. This choice is not very efficient in more complicated cases. Therefore, methods have been developed that use neural networks or generic algorithms to 'tune' membership functions. But this tuning takes lots of time (for example, several thousands iterations are typical for neural networks). In some cases there are evident physical reasons why equally space domains do not work: e.g., if the control variable u is always positive (i.e., if we control temperature in a reactor), then negative values (that are generated by equal spacing) simply make no sense. In this case it sounds reasonable to choose another scale u' = f(u) to represent u, so that equal spacing will work fine for u'. In the present paper we formulate the problem of finding the best rescaling function, solve this problem, and show (on a real-life example) that after an optimal rescaling, the un-tuned fuzzy control can be as good as the best state-of-art traditional non-linear controls.
Fuzzy PD scheme for underactuated robot swing-up control
E. Sanchez; L. A. Nuno; Ya-Chen Hsu; Guanrong Chen
1998-01-01
In this paper, a multi-input multi-output (MIMO) fuzzy proportional-derivative (PD) controller, equipped with a dynamic switching fuzzy system is applied to swing-up an underactuated robot, the so-called Pendubot. This mechanism consists of a double pendulum actuated only at the first joint. The simulations included illustrate the applicability of the proposed scheme. The new controller design and its application are described
Introduction to Fuzzy Control Marcelo Godoy Simoes
SimÃµes, Marcelo Godoy
between humans and machines: humans reason in uncertain, imprecise, fuzzy ways while machines and the computers that run them are based on binary reasoning. Fuzzy logic is a way to make machines more of differential or difference equations. Laplace transforms and z-transforms are respectively used. In order
Yajun Zhang; Tianyou Chai; Hong Wang; Jun Fu; Liyan Zhang; Yonggang Wang
2010-01-01
In this paper, an adaptive generalized predictive control method using adaptive-network-based fuzzy-inference system (ANFIS) and multiple models is proposed for a class of uncertain discrete-time nonlinear systems with unstable zero-dynamics. The proposed controller consists of a linear and robust generalized predictive adaptive controller, a nonlinear generalized predictive adaptive controller based on ANFIS, and a switching mechanism. It has been shown
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
Altitude control system of autonomous airship based on fuzzy logic
Guo Jian-guo; Zhou Jun
2008-01-01
A kind of design method of compound control system is proposed based on fuzzy logic control, according to the problem of altitude control for unmanned autonomous airship. By considering the scheme of buoyancy control system in the airship, the flight kinematics model is established based on forces acted on the airship. During the longitudinal movements, the altitude control system is
A recurrent neural fuzzy network controller for a temperature control system
Chia-Feng Juang; Jung-Shing Chen
2003-01-01
Temperature control by a TSK-type Recurrent Neural Fuzzy Network (TRNFN) controller based on the direct inverse control configuration is proposed in this paper. The TRNFN is a recurrent fuzzy network developed from a series of TSK type fuzzy if-then rules, and is on-line constructed by concurrent structure\\/parameter learning. The TRNFN has the following advantages when applied to temperature control problems
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
NASA Astrophysics Data System (ADS)
Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.
2011-01-01
Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
NASA Astrophysics Data System (ADS)
Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.
2010-09-01
Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
Adaptive neuro-fuzzy estimation of optimal lens system parameters
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor; Pavlovi?, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
An algorithm of the adaptive grid and fuzzy interacting multiple model
NASA Astrophysics Data System (ADS)
Zhang, Yuan; Guo, Chen; Hu, Hai; Liu, Shubo; Chu, Junbo
2014-09-01
This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model (AGFIMM) for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model (FSMM) algorithm's cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model (IMM) algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for engineering applications.
Longitudinal fuzzy control for autonomous overtaking
Joshue Perez; Vicente Milanes; Enrique Onieva; Jorge Godoy; Javier Alonso
2011-01-01
Cooperati ve maneuver among autonomous and con ventional vehicles can be considered one of the next steps for ob taining a safer and more comfortable driving. Some examples of these maneuvers are: Adaptive Cruise Control (ACC), intelligent intersection management or automatic overtaking maneuvering, among others. One of the most important aims of the Intelligent Transportation Systems (ITS) is to use
Fuzzy logic control of the building structure with CLEMR dampers
NASA Astrophysics Data System (ADS)
Zhang, Xiang-Cheng; Xu, Zhao-Dong; Huang, Xing-Huai; Zhu, Jun-Tao
2013-04-01
The semi-active control technology has been paid more attention in the field of structural vibration control due to its high controllability, excellent control effect and low power requirement. When semi-active control device are used for vibration control, some challenges must be taken into account, such as the reliability and the control strategy of the device. This study presents a new large tonnage compound lead extrusion magnetorheological (CLEMR) damper, whose mathematical model is introduced to describe the variation of damping force with current and velocity. Then a current controller based on the fuzzy logic control strategy is designed to determine control currents of the CLEMR dampers rapidly. A ten-floor frame structure with CLEMR dampers using the fuzzy logic control strategy is built and calculated by using MATLAB. Calculation results show that CLEMR dampers can reduce the seismic responses of structures effectively. Calculation results of the fuzzy logic control strategy are compared with those of the semi-active limit Hrovat control structure, the passive-off control structure, and the uncontrolled structure. Comparison results show that the fuzzy logic control strategy can determine control currents of CLEMR dampers quickly and can reduce seismic responses of the structures more effectively than the passive-off control strategy and the uncontrolled structure.
Implementation of a new fuzzy vector control of induction motor.
Rafa, Souad; Larabi, Abdelkader; Barazane, Linda; Manceur, Malik; Essounbouli, Najib; Hamzaoui, Abdelaziz
2014-05-01
The aim of this paper is to present a new approach to control an induction motor using type-1 fuzzy logic. The induction motor has a nonlinear model, uncertain and strongly coupled. The vector control technique, which is based on the inverse model of the induction motors, solves the coupling problem. Unfortunately, in practice this is not checked because of model uncertainties. Indeed, the presence of the uncertainties led us to use human expertise such as the fuzzy logic techniques. In order to maintain the decoupling and to overcome the problem of the sensitivity to the parametric variations, the field-oriented control is replaced by a new block control. The simulation results show that the both control schemes provide in their basic configuration, comparable performances regarding the decoupling. However, the fuzzy vector control provides the insensitivity to the parametric variations compared to the classical one. The fuzzy vector control scheme is successfully implemented in real-time using a digital signal processor board dSPACE 1104. The efficiency of this technique is verified as well as experimentally at different dynamic operating conditions such as sudden loads change, parameter variations, speed changes, etc. The fuzzy vector control is found to be a best control for application in an induction motor. PMID:24629620
Adaptive sequential controller
El-Sharkawi, Mohamed A. (Renton, WA); Xing, Jian (Seattle, WA); Butler, Nicholas G. (Newberg, OR); Rodriguez, Alonso (Pasadena, CA)
1994-01-01
An adaptive sequential controller (50/50') for controlling a circuit breaker (52) or other switching device to substantially eliminate transients on a distribution line caused by closing and opening the circuit breaker. The device adaptively compensates for changes in the response time of the circuit breaker due to aging and environmental effects. A potential transformer (70) provides a reference signal corresponding to the zero crossing of the voltage waveform, and a phase shift comparator circuit (96) compares the reference signal to the time at which any transient was produced when the circuit breaker closed, producing a signal indicative of the adaptive adjustment that should be made. Similarly, in controlling the opening of the circuit breaker, a current transformer (88) provides a reference signal that is compared against the time at which any transient is detected when the circuit breaker last opened. An adaptive adjustment circuit (102) produces a compensation time that is appropriately modified to account for changes in the circuit breaker response, including the effect of ambient conditions and aging. When next opened or closed, the circuit breaker is activated at an appropriately compensated time, so that it closes when the voltage crosses zero and opens when the current crosses zero, minimizing any transients on the distribution line. Phase angle can be used to control the opening of the circuit breaker relative to the reference signal provided by the potential transformer.
L. R. Burgess; A. E. Zeger; J. R. Binkley
1975-01-01
Radar targets such as slow, low flying aircraft and moving ground vehicles may be detected by employing an adaptively controlled antenna array with an AMTI radar set, conformally mounted in a high performance jet aircraft. Clutter cancellation is achieved through moving the active subaperture of the phased array to the rear of the aircraft with the transmission and reception of
Robust control of electrical drives using adaptive control structures — a comparison
Krzysztof Szabat
2008-01-01
In the paper a comparative study of two robust control strategies for electrical drives are presented. The both investigated methods are based on the MRAS (model reference adaptive system) concept. In these structures the fuzzy-neural network speed controller is trained on-line in order to minimize the tracking errors between the output of the plant (drive speed) and the reference model.
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
Gang Feng
2006-01-01
Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
Victor Boskovitz; Hugo Guterman
2002-01-01
An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is
Intelligent fuzzy immune PID controller design for multivariable process control system
Zheng Li
2010-01-01
Based on biological immune principle and fuzzy theory, this paper presents an intelligent fuzzy immune PID control scheme to solve the control difficulties of industry process with multi-variables. The least square algorithm was used for offline optimization to form immune feedback control system. The application on cement rotary kiln control was discussed in detail as an example. The rotary kiln
An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control
Xiaohui Dai; Chi-kwong Li; A. B. Rad
2005-01-01
In this paper, we suggest a new approach for tuning parameters of fuzzy controllers based on reinforcement learning. The architecture of the proposed approach is comprised of a Q estimator network (QEN) and a Takagi-Sugeno-type fuzzy inference system (TSK-FIS). Unlike other fuzzy Q-learning approaches that select an optimal action based on finite discrete actions, the proposed controller obtains the control
Fuzzy sliding mode control for a robot manipulator
B. W. Bekit; J. F. Whidborne; L. D. Seneviratne
1997-01-01
A sliding mode control algorithm combined with a fuzzy control scheme is developed for the trajectory control of a robot manipulator. The scheme is used to compensate for the influence of unmodeled dynamics and to reduce chattering. Simulation results show that the proposed controller gives good system performance in the face of uncertain system parameters and external disturbances
Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes
NASA Technical Reports Server (NTRS)
Duerksen, Noel
1997-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
A fuzzy approach to elevator group control system
Chang Bum Kim; K. A. Seong; Hyung Lee-Kwang; J. O. Kim; Yong Bae Lim
1995-01-01
The elevator group control systems are the control systems that manage systematically, three or more elevators in order to efficiently transport the passengers. In the elevator group control system, the area-weight which determines the load biases of elevators is a control parameter closely related to the system performance. This correspondence proposes a fuzzy model based method to determine the area
Fuzzy controller synthesis for an inverted pendulum system
S. Yurkovich; M. Widjaja
1996-01-01
A frequently discussed issue in the use of fuzzy systems for control design is related to the ad hoc nature by which controller synthesis is performed, where incorporation of the designer's knowledge into the synthesis procedure is often not straightforward. This paper describes a controller synthesis procedure based on the idea of expanding the usable region of a linear control
Wen-Jer Chang; Liang-Zhi Liu; Cheung-Chieh Ku
2011-01-01
This paper investigates the fuzzy control problem of a class of nonlinear continuous-time stochastic systems with achieving\\u000a the passivity performance. A model-based observer feedback fuzzy control utilizing the concept of so-called parallel distributed\\u000a compensation (PDC) is employed to stabilize the class of nonlinear stochastic systems that are represented by the Takagi-Sugeno\\u000a (T-S) fuzzy models. Based on the Lyapunov criteria, the
A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers
NASA Technical Reports Server (NTRS)
Woodard, Stanley E.; Garg, Devendra P.
1998-01-01
This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.
Neuro-Fuzzy Control of a Robotic Manipulator
NASA Astrophysics Data System (ADS)
Gierlak, P.; Muszy?ska, M.; ?ylski, W.
2014-08-01
In this paper, to solve the problem of control of a robotic manipulator's movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator's dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator
Based on Fuzzy - PID self-tuning temperature control system of the furnace
Xiao Junming; Zhu Haiming; Jiao Lingyun; Zhang Rui
2011-01-01
According to the temperature of vacuum sintering furnace inertia big, pure time-delay, nonlinear characteristics, combine with the traditional PID control and Fuzzy control, design auto-tuning Fuzzy-PID control system, obtained by MATLAB simulation: auto-tuning Fuzzy-PID control system has small overshoot, fast response, high steady precision, anti-interference ability, and good robustness etc. It has high industrial utility value. Keywords-fuzzy; PID control; vacuum
Application of genetic algorithms to tuning fuzzy control systems
NASA Technical Reports Server (NTRS)
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
An architecture for designing fuzzy logic controllers using neural networks
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1991-01-01
Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.
Fuzzy sampled-data control for uncertain vehicle suspension systems.
Li, Hongyi; Jing, Xingjian; Lam, Hak-Keung; Shi, Peng
2014-07-01
This paper investigates the problem of sampled-data H? control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy H? disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method. PMID:24043419
Fuzzy Control of Flexible-Link Manipulators: A Review
NASA Technical Reports Server (NTRS)
Akbarzadeh-T, M.-R.; Quintana, S.; Jamshidi, M.
1998-01-01
Several recent research efforts are reviewed here which have applied fuzzy logic in control of flexible-link manipulators. A flexible robot is a distributed parameter system represented by complex nonlinear dynamics, its actuator and the control parameters are non-colocated, and lastly, unstructured/unknown parameters play a significant role in model dynamics of a flexible robot operating in the real world. As a result, control of flexible robots is considered a promising area for application of intelligent control methodologies such as fuzzy logic, genetic algorithms, and neural networks.
Design and application of fuzzy control system of engine variable valve timing
Chang Wenxiu; Li Liguang; Tao Jianwu; Xiao Min; Zeng Zhaoyang
1999-01-01
In this paper, a design method of fuzzy control system of engine variable valve timing (VVT) is proposed. In this system, the fuzzy controller is used. The VVT system adopted fuzzy controller eliminates shortcomings in traditional control method such as low accuracy and unsteadiness etc. It has been proved by vehicle road test that the system is effective to meet
The Application of the Fuzzy Controller Based on PLC in Sewage Disposal System
Kang Sun; Yan-min Song; Guo-chuan Feng
2009-01-01
In order to solve the problems such as long time-delay, non-linear and the difficulty to establish an accurate mathematical model emerged in industrial field, we combine advanced intelligent control method with traditional automated devices, and put forward a Fuzzy Controller in accordance with the principle of fuzzy control and the characteristics of PLC. The Fuzzy Controller realized on the basis
Fuzzy control strategies for temperature of hot-water based on PLC system
Zhang Hua; Cai Zhenjiang; Li Yan
2010-01-01
This paper describes a kind of temperature control system which is composed of PLC. Fuzzy control algorithm was used for the system. The basic structure of fuzzy control, parameter design, and system software and hardware design was expounded. In this system, fuzzy control achieved reasonable optimization of heating time and saving electric energy of the heating element. In practical applications,
Precise Positioning Control Using Auto-Tuned Fuzzy Logic Controller
NASA Astrophysics Data System (ADS)
Abd-Elhameed, Esam H.; Iwasaki, Makoto
Nonlinear friction, resonant vibration modes, in addition to dead time of a positioning mechanism deteriorate the control performance in the microscopic displacement range. A control scheme composed of two types of control methodology is proposed in this paper in order to obtain high speed and high precision positioning of a ball-screw-driven mechanism: a feedforward compensator, based on coprime factorization of the positioning mechanism with dead time compensator, and a feedback compensator, an auto-tuned PDFLC (Proportional plus Derivative Fuzzy Logic Controller) based on real coded genetic algorithm as an optimization technique, with nonlinear friction compensation by using inverse model-based disturbance observer. Experimental results verified the effectiveness and robustness of the proposed control system against the difference of the nonlinear friction accompanied with the repetitive motion.
Analysis and design for a class of complex control systems part II: Fuzzy controller design
S. G. Cao; N. W. Rees; G. Feng
1997-01-01
In this second paper on the analysis and design of complex control systems, we present a controller design method for a class of complex control systems. This class of systems can be represented by a discrete-time dynamical fuzzy model as discussed in Part I, the companion paper. A necessary and sufficient condition for stabilization of this kind of discrete-time fuzzy
NERC compliant load frequency control design using fuzzy rules
Ali Feliachi; Dulpichet Rerkpreedapong
2005-01-01
This paper presents a load frequency control design for interconnected electric power systems using a set of fuzzy logic rules. The design objectives are (i) to comply with the North American Electric Reliability Council's (NERC) control performance standards, CPS1 and CPS2, (ii) to reduce wear and tear of generating unit's equipments, and (iii) to design a feasible control structure. A
Type2 Fuzzy Control for Bioinformatics - A Systems Approach
Aboubekeur Hamdi-Cherif
2010-01-01
Summary Life is governed by high-precision perfectly engineered control processes, from the simplest cell to the most sophisticated ecosystem - and beyond. Biological control systems are at the heart of life. This paper reports the study of regulation and the applicability of type-2 fuzzy control systems in bioinformatics. The framework of study is systems biology. In addition to two previously-described
A fuzzy logic controller for a dry rotary cement kiln
Mazhar Tayel; M. R. M. Rizk; H. A. Hagras
1997-01-01
The dry rotary cement kiln is the most important part of the cement plant. Cement kilns exhibit time-varying nonlinear behavior and relatively few measurements are available, consequently, automatic control is usually restricted to a few simple control loops on secondary variables, leaving the responsibility for the control of primary variables to the kiln operators. In this paper a fuzzy logic
Design of the fuzzy control system of the centerless grinder
Zhang Xue Ming
2011-01-01
With the higher and higher requirements of the mechanical processing, centerless grinding is paid more and more attentions. Therefore, the performance of the control system for the centerless grinding machine is very important. In this paper, a fuzzy control scheme is utilized in the AC motor control by means of the inverter in order to overcome the disadvantages of the
Design and implementation of a fuzzy elevator group control system
Changbum Kim; Kyoung A. Seong; Hyung Lee-kwang; Jeong O. Kim
1998-01-01
Elevator group control systems (EGCSs) are the control systems that systematically manage three or more elevators in order to efficiently transport passengers. Most EGCSs have used the hall call assignment method to assign elevators in response to passengers' calls. This paper proposes a control strategy generation method, a hall call assignment method based on the fuzzy theory, and then the
Design and implementation of FEGCS: fuzzy elevator group control system
Chang Bum Kim; Kyoung A Seong; Hyung Lee-Kwang; Jeong O Kim
1996-01-01
The elevator group control systems (EGCS) are the control systems that manage systematically three or more elevators in order to efficiently transport the passengers. Most of the EGCS's have used the hall call assignment method to assign elevators in response to passenger's calls. This paper proposes a control strategy generation method, a hall call assignment method based on fuzzy theory
Design of an optimal fuzzy controller for antilock braking systems
A. Mirzaei; M. Moallem; B. Mirzaeian; B. Fahimi
2005-01-01
Antilock braking systems (ABS) have been developed to improve vehicle control during sudden braking especially on slippery road surfaces. The objective of such control is to increase wheel tractive force in the desired direction while maintaining adequate vehicle stability and steerability and also reducing the vehicle stopping distance. In this paper, an optimized fuzzy controller is proposed for antilock braking
Applications of fuzzy logic to control and decision making
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.
Fuzzy logic control for active bus suspension system
NASA Astrophysics Data System (ADS)
Turkkan, Mujde; Yagiz, Nurkan
2013-02-01
In this study an active controller is presented for vibration suppression of a full-bus suspension model that use air spring. Since the air spring on the full-bus model may face different working conditions, auxiliary chambers have been designed. The vibrations, caused by the irregularities of the road surfaces, are tried to be suppressed via a multi input-single output fuzzy logic controller. The effect of changes in the number of auxiliary chambers on the vehicle vibrations is also investigated. The numerical results demonstrate that the presented fuzzy logic controller improves both ride comfort and road holding.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
BIBO Stability of Spatial-temporal Fuzzy Control System
Xianxia Zhang; Meng Sun; Guitao Cao
2010-01-01
\\u000a Three-dimensional fuzzy logic controller (3-D FLC) is a novel FLC developed recently for spatially-distributed systems. In\\u000a this study, the BIBO stability issue of the 3-D fuzzy control system is discussed. A sufficient condition is derived and provided\\u000a as a useful criterion for the controller design of the 3-D FLC. Finally, a catalytic packed-bed reactor is presented as an\\u000a example of
Optimized Reactive Power Compensation Using Fuzzy Logic Controller
NASA Astrophysics Data System (ADS)
George, S.; Mini, K. N.; Supriya, K.
2015-03-01
Reactive power flow in a long transmission line plays a vital role in power transfer capability and voltage stability in power system. Traditionally, shunt connected compensators are used to control reactive power in long transmission line. Thyristor controlled reactor is used to control reactive power under lightly loaded condition. By controlling firing angle of thyristor, it is possible to control reactive power in the transmission lines. However, thyristor controlled reactor will inject harmonic current into the system. An attempt to reduce reactive power injection will increase harmonic distortion in the line current and vice versa. Thus, there is a trade-off between reactive power injection and harmonics in current. By optimally controlling the reactive power injection, harmonics in current can be brought within the specified limit. In this paper, a Fuzzy Logic Controller is implemented to obtain optimal control of reactive power of the compensator to maintain voltage and harmonic in current within the limits. An algorithm which optimizes the firing angle in each fuzzy subset by calculating the rank of feasible firing angles is proposed for the construction of rules in Fuzzy Logic Controller. The novelty of the algorithm is that it uses a simple error formula for the calculation of the rank of the feasible firing angles in each fuzzy subset.
Adaptive neuro fuzzy inference system for profiling of the atmosphere
NASA Astrophysics Data System (ADS)
Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.
2014-03-01
Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June-September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.
Multilayered fuzzy behavior fusion for real-time reactive control of systems with multiple sensors
Steven G. Goodridge; Michael G. Kay; Ren C. Luo
1996-01-01
Fuzzy linguistic rules provide an intuitive and powerful means for defining control behavior. Most applications that use fuzzy control feature a single layer of fuzzy inference, mapping a function from one or two inputs to equally few outputs. Highly complex systems, with large numbers of inputs, may also benefit from the use of qualitative linguistic rules if the control task
Singh, Pritpal
FUZZY LOGIC-BASED SOLAR CHARGE CONTROLLER FOR MICROBATTERIES Pritpal Singh and Jagadeesan of a micro- charge/discharge controller has not. In this paper we present a novel, fuzzy logic-based solar is adjusted by modulating the duty cycle of the buck converter's switching MOSFET using a fuzzy logic control
N. Yubazaki; Jianqiang Yi; M. Otani; N. Unemura; K. Hirota
1997-01-01
A trajectory tracking experiment system is constructed. The control object is a table-tennis ball rolling on a table which is rotated about the X and Y axes. A fuzzy controller based on the single input rule modules (SIRM) dynamically connected fuzzy inference model is proposed. The fuzzy controller defines a SIRM and an importance degree for each input item. Especially
Real-time tracking control of underactuated pendubot using Takagi-Sugeno fuzzy systems
Zhen Cai; Chun-Yi Su
2003-01-01
By combining optimal control theory and linear regulator theory with the Takagi-Sugeno fuzzy methodology, a global optimal and stable fuzzy controller is presented to achieve trajectory tracking for a specific underactuated robot: the pendubot. The stability of the entire close-loop system is ensured by the designed optimal fuzzy controller. The paper includes a description of the pendubot system and the
Layered mode selection logic control with fuzzy sensor fusion network
NASA Astrophysics Data System (ADS)
Born, Traig; Wright, Andrew
2007-04-01
Robots developed from the 60's to the present have been restricted to highly structured environments such as work cells or automated guided vehicles, primarily to avoid harmful interactions with humans. Next generation robots must function in unstructured environments. Such robots must be fault tolerant to sensor and manipulator failures, scalable in number of agents, and adaptable to different robotic base platforms. The Central Arkansas Robotics Consortium has developed a robot controller architecture, called Layered Mode Selection Logic (LMSL), which addresses all of these concerns. The LMSL architecture is an implementation of a behavior based controller fused with a planner. The architecture creates an abstraction layer for the robot sensors through a Fuzzy Sensor Fusion Network (FSFN), and it creates an abstraction layer for the robot manipulators through a reactive layer. The LMSL architecture has been implemented and tested on UALR's J5 robotics research platform. A FSFN combines acceleration and force signals for collision detection. The output of the FSFN switches among low level behaviors to accomplish obstacle avoidance and obstacle manipulation. Comparable results are achieved with all sensors functioning, with only the acceleration sensor (force sensor faulted), and with only the force sensor (acceleration sensor faulted).
NASA Technical Reports Server (NTRS)
Kreinovich, V.; Lea, R.; Fuentes, O.; Lokshin, A.
1992-01-01
Fuzzy control techniques are analyzed to explain why the fuzzy control that is based on the expert's knowledge is often smoother and more stable than the control performed manually by the same experts. A precise mathematical explanation of this phenomenon is presented. Results obtained make it possible to predict the quality of the fuzzy control.
Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes
NASA Technical Reports Server (NTRS)
Duerksen, Noel
1996-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Advanced Control Technology Development of Sulfuric Acid-Connecting System Based on Fuzzy Control
Yan Dong; Qin Bin
2010-01-01
The theme of the paper is treatment of discharges of sulfur dioxide from process of petrochemical, aiming at the problems of the long time, none linearity and the precise mathematic that is hard to build, we consider combining the advanced fuzzy control with the traditional automation technology to achieve low cost automatization. According to the principles of fuzzy control and
Adaptive nonlinear flight control
NASA Astrophysics Data System (ADS)
Rysdyk, Rolf Theoduor
1998-08-01
Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator dynamics. The architecture is demonstrated on the XV-15 tiltrotor.
Fuzzy Explicit Marking for Congestion Control in Differentiated Services Networks
Pitsillides, Andreas
) mechanisms (e.g. random early detection - RED) have been proposed [2, 3] within the framework of the DiffFuzzy Explicit Marking for Congestion Control in Differentiated Services Networks C. Chrysostomou), implemented within the differentiated services (Diff-Serv) framework to provide congestion control using
Fuzzy predictive control for nitrogen removal in biological wastewater treatment
Fuzzy predictive control for nitrogen removal in biological wastewater treatment S. Marsili wastewater is too low, full denitrification is difficult to obtain and an additional source of organic carbon predictive control; wastewater treatment plant Introduction The problem of improving the nitrogen removal
A fuzzy satisficing method for multiobjective linear optimal control problems
Masatoshi Sakawa; Masahiro Inuiguchi; Kosuke Kato; Tomohiro Ikeda
1996-01-01
In this paper, we propose a fuzzy satisficing method for the solution of multiobjective linear continuous optimal control problems. To solve these multiobjective linear continuous optimal control problems, we first discretize the time and replace the system of differential equations by difference equations. By introducing suitable auxiliary variables, approximate linear multiobjective programming problems are formulated. Then by considering the vague
Simple tuned fuzzy controller embedded into an FPGA
Oscar Montiel; Yazmin Maldonado; R. Sepulveda; Oscar Castillo
2008-01-01
It is presented a flexible architecture that allows to implement an embedded nonlinear fuzzy controller into an FPGA which can be easily tuned through the use of the Simple Tuning Algorithm (STA) without a controller reference model. The model was developed using VHDL programming, and it was tested in soft real time using Xilinx System Generator and Simulink before the
Rotor levitation by Active Magnetic Bearings using Fuzzy Logic Controller
P. V. S. Sobhan; G. V. N. Kumar; J. Amarnath
2010-01-01
Active Magnetic Bearings(AMB) have many advantages such as no friction loss, no abrasion, lubrication-free quality, and used for high rotational speed applications. A complete system consists of an actuator, power amplifier, a rotor position sensor and a control system. In this paper, a closed loop decentralized Fuzzy Logic control for Active magnetic Bearings is designed. For the numerical evaluation of
Fuzzy logic control of a switched reluctance motor
M. G. Rodrigues; W. I. Suemitsu; P. Branco; J. A. Dente; L. G. B. Rolim
1997-01-01
This paper presents the use of fuzzy logic control (FLC) for switched reluctance motor (SRM) speed. The PLC performs a PI-like control strategy, giving the current reference variation based on speed error and its change. The performance of the drive system was evaluated through digital simulations through the toolbox Simulink of the Matlab program
Elevator group control system with a fuzzy neural network model
S. Nakai; S. Kubo; N. Imasaki; T. Yoshitsugu; J.-I. Kiji; T. Endo
1995-01-01
We have developed a high-performance elevator group control system EJ-1000FN with a performance tuning function, which employs a fuzzy neural network as a performance forecasting model of the elevator system. The performance tuning function utilizes the forecasting model in order to search the optimal control parameters which give the best system performance in the present traffic situation
Fuzzy self-learning control for magnetic servo system
NASA Technical Reports Server (NTRS)
Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.
1994-01-01
It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.
Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks.
Ranganathan, Mohan Krishna; Kilmartin, Liam
2005-09-01
Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays. Index Terms-Fuzzy delay trend analysis, intertalkspurt, intratalkspurt, multilayer perceptrons (MLPs), network delay estimation, playout buffering, playout delay adaptation, time delay neural networks (TDNNs), voice over Internet protocol (VoIP). PMID:16252825
Lu-Hang Zong; Xing-Long Gong; Chao-Yang Guo; Shou-Hu Xuan
2011-01-01
In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc–Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on
Lu-Hang Zong; Xing-Long Gong; Chao-Yang Guo; Shou-Hu Xuan
2012-01-01
In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc–Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on
Control of a flexible beam using fuzzy logic
NASA Technical Reports Server (NTRS)
Mccullough, Claire L.
1991-01-01
The goal of this project, funded under the NASA Summer Faculty Fellowship program, was to evaluate control methods utilizing fuzzy logic for applicability to control of flexible structures. This was done by applying these methods to control of the Control Structures Interaction Suitcase Demonstrator developed at Marshall Space Flight Center. The CSI Suitcase Demonstrator is a flexible beam, mounted at one end with springs and bearing, and with a single actuator capable of rotating the beam about a pin at the fixed end. The control objective is to return the tip of the free end to a zero error position (from a nonzero initial condition). It is neither completely controllable nor completely observable. Fuzzy logic control was demonstrated to successfully control the system and to exhibit desirable robustness properties compared to conventional control.
A PLC-based self-tuning PI-fuzzy controller for linear and non-linear drives control
Muhammad Arrofiq; Nordin Saad
2008-01-01
This paper presents the design, implementation and analysis of a PLC-based self-tuning PI-fuzzy controller for linear and non-linear drives control. The controller consists of two fuzzy logic blocks, main and gain tuning, respectively. The main fuzzy block acts as a speed controller, while the gain tuning block scales the output of main fuzzy. The output gain tuning has the same
Based on PLC temperature PID - fuzzy control system design and simulation
Cao Weibin; Meng Qingjian
2010-01-01
Temperature control plays a very important role in industrial control. It is one of four industrial control parameters. Based on PLC (programmable controller) on the basis of the traditional PID control and current popular intelligent control of fuzzy control combination of control plan, to realize the integral design of the control algorithm, and achieve in a large scale, a fuzzy
#12; #12; !"""# Sonar Behavior-Based Fuzzy Control for a Mobile Robot S. Thongchai, S describes how fuzzy control can be ap- plied to a sonar-based mobile robot. Behavior-based fuzzy control for HelpMate behaviors was designed us- ing sonar sensors. The fuzzy controller provides a mechanism
LIU HONGLING; JIANG CHUANWEN; ZHANG YAN
2006-01-01
In this paper, for nonlinear, time-varying and delayed system, PID parameters self-tuning fuzzy control was applied to a level process. The method of using VC++ programming to realize fuzzy control algorithm and obtain the discrete fuzzy control table is proposed, which simplified the computing process greatly. Furthermore, combing the inquiry of fuzzy control table and PID function module in PLC,
FUZZY LOGIC MOTOR CONTROL FOR POLLUTION PREVENTION AND IMPROVED ENERGY EFFICIENCY
The paper discusses an EPA program investigating fuzzy logic motor control for improved pollution prevention and energy efficiency. nitial computer simulation and laboratory results have demonstrated that fuzzy logic energy optimizers can consistently improve motor operational ef...
Genetic algorithm-based optimal fuzzy control system for the MT 25 microtron
NASA Astrophysics Data System (ADS)
Krist, P.; Bíla, J.; Chvátil, D.
2013-05-01
This paper deals with the design of the control system for an RF cyclic electron accelerator with a cavity resonator, a classical type of microtron. This type of accelerator has until now been controlled manually. The control system is based on a Mamdani-type fuzzy regulator. The fuzzy regulator is set with the aid of an operator description and also a mathematical model of the microtron. The final control system is optimized with the help of genetic algorithms. The normalizing and denormalizing section of the fuzzy controller and also the shape of the fuzzy values of the fuzzy variables are optimized.
NASA Technical Reports Server (NTRS)
Abihana, Osama A.; Gonzalez, Oscar R.
1993-01-01
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.
Temperature Control by Recurrent Fuzzy Network Designed Via Clustering Aided Swarm Intelligence
Chia-Feng Juang; Chao-Hsin Hsu; Cheng-Da Hsieh
2006-01-01
Temperature control by a recurrent fuzzy network designed by Clustering-aided Simplex Particle Swarm Optimization, called CSPSO, is proposed in this paper. With CSPSO, the number of rules in a recurrent fuzzy controller is determined automatically by fuzzy clustering. Once a new rule is generated, the corresponding parameters are further tuned by the hybrid of simplex method and particle swarm optimization.
Research of the micro-EDM discharge state detection method based on matlab Fuzzy control
Yang Yang; Mei Yanghan; He Tian
2010-01-01
Based on the rules which summed up of the micro-EDM discharge state, the paper makes use of Fuzzy control technology to detect discharge state. It mainly uses matlab Fuzzy module to design discharge state detection Fuzzy controller, and get a good identification results through simulating. Compared with the traditional detection methods, it gains better results in the micro-hole EDM experiment
Evolution of a Negative-Rule Fuzzy Obstacle Avoidance Controller for an Autonomous Vehicle
John H. Lilly
2007-01-01
A fuzzy obstacle avoidance controller is designed for an autonomous vehicle. The controller is given the capability for obstacle avoidance by using negative fuzzy rules in conjunction with traditional positive ones. Negative fuzzy rules prescribe actions to be avoided rather than performed. A rule base of positive rules is specified by an expert for directing the vehicle to the target
Linear elevator velocity control system based on correction factor fuzzy PID
Haiyan Yu; Qing Hu; Jing Zhang
2008-01-01
The elevator cab will vibrate because of the edge effect in the linear elevators that is driven by the permanent magnet linear synchronous motor (PMLSM). This article adopts correction factor fuzzy - PID control method in order to solve the vibration of elevator cab. The fuzzy control is not completely dependent on the mathematics model, but the fuzzy rules are
A fuzzy behaviorist approach to sensor-based robot control
Pin, F.G.
1996-05-01
Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.
Design for Fuzzy Decoupling Control System of Temperature and Humidity
Xi-Wen Liu; Tie-Feng Dai
\\u000a Grain drying process is a complicatedly hysteretic system with multivariate, nonlinearity, time-varying. Traditional control\\u000a method is difficult to get ideal control effect since there’s coupled relation between temperature and humidity, and it’s\\u000a difficult to build accurate mathematical models. Therefore, this thesis inducts decoupling ideas as well as fuzzy control\\u000a method, realizing decoupling control of temperature and humidity of the system,
Fuzzy control of hydraulic servo system based on DSP
NASA Astrophysics Data System (ADS)
He, Juan; Yuan, Song-Yue
2011-10-01
On the basis of high-speed switching valve of hydraulic servo system, the complex mathematical model of nonlinear hydraulic servo system was analyzed and constructed. A intelligent Fuzzy control method using TMS320LF2407A DSP chip as primary processor was put forward. The simulation results show that the control strategy has a better effect than the conventional PID control has. And the non-differential control of the system has been basically achieved.
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…
A demosaicing algorithm based on adaptive edge sensitive and fuzzy assignment in CMOS image sensor
Ge Zhiwei; Yao Suying; Xu Jiangtao
2010-01-01
In order to ensure the PSNRs of the demosaiced images of CMOS image sensor and reduce the computational cost at the same time, the proposed demosaicing algorithm in this paper improves on adaptive edge sensitive algorithm and fuzzy assignment algorithm. In order to estimate the direction of edges more accurately, it adds two adjacent pixels of the current pixel as
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival
Aickelin, Uwe
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival Hazlina Hamdan for breast cancer. I. INTRODUCTION Breast cancer is one of the most common cancers to afflict the female population. It is estimated that one in nine women in the UK will develop breast cancer at some point
An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
Ulas Çaydas; Ahmet Hasçalik; Sami Ekici
2009-01-01
A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness
Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions
Alex. B. McBratney; Inakwu O. A. Odeh
1997-01-01
Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,
Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions
B. McBratney; Inakwu O. A. Odeh
Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,
A ROBUST INTELLIGENT PID-TYPE FUZZY CONTROL STRUCTURE FOR PRESSURE CONTROL
N. Kanagaraj; R. Kumar; P. Sivashanmugam
2008-01-01
A PID-type fuzzy controller for regulating pressure in a pilot pressure control system has been demonstrated in this article. The control algorithm for the proposed control logic has been developed in embedded Keil environment, and then downloaded into a target ARM7 microcontroller (AT91M55800A) for real-time validation. Although well-tuned conventional fuzzy controllers perform well around normal working regions, their outputs have
Controlling Interstate Conflict using Neuro-fuzzy Modeling and Genetic Algorithms
T. Tettey; T. Marwala
2006-01-01
The paper introduces neuro-fuzzy modeling to the problem of controlling interstate conflict. It is shown that a neuro-fuzzy model achieves a prediction accuracy similar to Bayesian trained neural networks. It is further illustrated that a neuro-fuzzy model can be used in a genetic algorithm (GA) based control scheme to avoid 100% of the detected conflict cases. The neuro-fuzzy model is
A Three-Dimensional Fuzzy Control Methodology for a Class of Distributed Parameter Systems
Han-Xiong Li; Xian-Xia Zhang; Shao-Yuan Li
2007-01-01
The traditional fuzzy set is two-dimensional (2-D) with one dimension for the universe of discourse of the variable and the other for its membership degree. This 2-D fuzzy set is not able to handle the spatial information. The traditional fuzzy logic controller (FLC) developed from this 2-D fuzzy set may not be able to effectively control the distributed parameter system
Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.
Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao
2012-08-01
In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller. PMID:22938339
Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model
NASA Astrophysics Data System (ADS)
Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao
2012-08-01
In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.
Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion
Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing
2011-01-01
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF. PMID:22319400
Design and tuning of fuzzy control surfaces with Bezier functions
Hanqi Zhuang; Songwut Wongsoontorn
2005-01-01
Design and tuning a fuzzy logic controller (FLC) are usually done in two stages. In the first stage, the structure of a FLC is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC are selected to optimize the performance of the system. The task of tuning FLCs can be performed by a
Towards emotional control recognition through handwriting using fuzzy inference
Sofianita Mutalib; Roslina Ramli; Shuzlina Abdul Rahman; Marina Yusoff; Azlinah Mohamed
2008-01-01
Emotion control is one of personality characteristics that can be detected through handwriting or graphology. One of the advantages is it may help the counselor that has difficulties in identifying the emotion of their counselee. This study is to explore the fuzzy technique for feature extraction in handwriting and then identify the emotion of person. This study uses baseline or
The Use of Fuzzy Measures in Pain Relief Control
Kreinovich, Vladik
the suffering of the patients suffering from the chronic pain, it is desirable to stop the pain sig nals fromThe Use of Fuzzy Measures in Pain Relief Control Vladik KREINOVICH \\Lambda and Nadipuram R. PRASAD\\Lambda Department of Electrical Engineering, New Mexico State University Abstract: Many people suffer from
Workshop on Fuzzy Control Systems and Space Station Applications
NASA Technical Reports Server (NTRS)
Aisawa, E. K. (compiler); Faltisco, R. M. (compiler)
1990-01-01
The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented.
Neuro-Fuzzy Control for MPEG Video Transmission Over Bluetooth
Hassan B. Kazemian; Li Meng
2006-01-01
The application of a neuro-fuzzy (NF) controller to moving picture expert group (MPEG-2) video transmission over a Bluetooth asynchronous connectionless (ACL) is presented in this paper. MPEG variable bit rate (VBR) data sources experience unpredictability, long delay, and excessive loss, due to sudden variations in bit rate. Therefore, it is practically impossible to transmit MPEG-2 VBR video sources over a
NOZZLE FUZZY CONTROLLER OF AGRICULTURAL SPRAYING ROBOT AIMING
of pesticide residues in agricultural products, the environmental pollution and so on. The precision pesticide lead to the serious consequences easily, such as the waste of pesticide resources, the unattainment., 2007). A pesticide system spraying with changeable quantity based on fuzzy control was studied by Anhui
Intelligent fuzzy logic controller for a solar charging system
Cong-Hui Huang; Chung-Chi Huang; Ting-Chia Ou; Kai-Hung Lu; Chih-Ming Hong
2009-01-01
This paper presents an intelligent solar charging system with fuzzy logic control method. With the scarce energy source and the worsening environmental pollution, how to create and use a clean and never exhausted energy is becoming very important day by day. This solar charging system is composed of a solar cell, a charger, batteries, a buck converter and a digital
GA-optimized fuzzy logic control of high-rise building for wind loads
NASA Astrophysics Data System (ADS)
Yan, Shi; Zheng, Wei; Song, Gangbing
2009-03-01
A fuzzy logic control (FLC) algorithm optimized by the genetic algorithm (GA) is developed in the paper for the benchmark problem application regarding the vibration control of tall buildings under along wind excitation. The adopted control scheme consists of an MR damper which the control action is achieved by a Fuzzy Controller. The fuzzy rules for the controller are optimized by the genetic algorithm to enhance the efficiency of the control system. A fuzzy strategy of two-input and single-output variables is adopted in the control system. The fuzzy subset and rules base for the controller are optimized by the genetic algorithm to further decrease the responses of the controlled structure. The robustness of the controller has been demonstrated through the uncertainty in stiffness (15% and -15% variations from initial stiffness) of the building. The results of the simulation show a good performance by the fuzzy controller for all tested cases.
Adaptive hybrid control of manipulators
NASA Technical Reports Server (NTRS)
Seraji, H.
1987-01-01
Simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecuture is presented. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal and a force feedforward term, and it achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers and an auxiliary signal, and it accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in on-line control with high sampling rates.
Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm
NASA Astrophysics Data System (ADS)
Mittal, Ruchi; Kaur, Magandeep
2010-11-01
In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.
Evolving fuzzy rules in a learning classifier system
NASA Technical Reports Server (NTRS)
Valenzuela-Rendon, Manuel
1993-01-01
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.
Suat Akbulut; A. Samet Hasiloglu; Sibel Pamukcu
2004-01-01
Neuro-fuzzy inference systems have been used in many areas in civil engineering applications. This study was conducted to estimate low strain dynamic properties of composite media from easily measurable physical properties using the adaptive neuro-fuzzy inference system (ANFIS). The inference system was employed to predict the shear modulus and the damping coefficient of the sand samples as an alternative to
Performances of fuzzy-logic-based indirect vector control for induction motor drive
M. Nasir Uddin; Tawfik S. Radwan; M. Azizur Rahman
2002-01-01
This paper presents a novel speed control scheme of an induction motor (IM) using fuzzy-logic control. The fuzzy-logic controller (FLC) is based on the indirect vector control. The fuzzy-logic speed controller is employed in the outer loop. The complete vector control scheme of the IM drive incorporating the FLC is experimentally implemented using a digital signal processor board DS-1102 for
Fuzzy controller design for synchronous motion in a dual-cylinder electro-hydraulic system
Cheng-Yi Chen; Li-Qiang Liu; Chi-Cheng Cheng; George T.-C. Chiu
2008-01-01
In this paper, an integrated fuzzy controller is proposed to achieve a synchronous positioning objective for a dual-cylinder electro-hydraulic lifting system with unbalanced loadings, system uncertainties, and disturbances. The control system consists of one-fuzzy coordination controller for both cylinders and an individual cylinder controller, comprised of a feedforward controller, and a fuzzy tracking controller, for each of the hydraulic cylinders.
Control of induction motor drives using modified-fuzzy logic methods
Muhammad Arrofiq; Nordin Saad
2010-01-01
An investigation into the implementation of a PLC-based modified-fuzzy logic controller for an induction motor drive is presented. The controller consists of two control strategies i.e. a PID-type fuzzy logic controller and an incremental PID action. At the early stage of control action, the task is handled by PID-type fuzzy controller. When the absolute of error is less than a
Direct Adaptive Generalized Predictive Control
Wei Wang; Rolf Henriksen
1992-01-01
This paper is concerned with the direct approach of adaptive generalized predictive control. An implicit model With control law parameters is developed. A direct adaptive generalized predictive control algorithm and an improved variant are suggested. Global convergence of the algorithms is analyzed under some assumptions.
Precision positioning system based on intelligent Fuzzy-PID control
NASA Astrophysics Data System (ADS)
Liu, Zhen; Zhang, Liqiong; Li, Yan
2010-08-01
To break through the limitations of static and dynamic characteristics of conventional step motor driven open-loop positioning devices, a two-dimensional precision positioning system with a travel range of 100mm×100mm has been developed. This paper presents its structure, control principle and performance experiments. This system, equipped with cross roller guides working as linear guiding elements, is driven by step motors through ball screw transmission. A threeaxis dual-frequency laser interferometric measurement system is established for real-time measurement and feedback of system's movements in three degrees of freedom (DOF) and an intelligent Fuzzy-PID controller is implemented for this system's motion control. In the controller, the PID module calculates the output from motor drivers and its initial parameters are tuned through expansion of critical proportioning method; the Fuzzy module optimizes PID parameters to fulfill specific requirements of different movement stages. A dead zone control mechanism is developed in this controller to minimize the oscillations around target position. Experimental results indicate that system with Fuzzy-PID controller shows faster response than that with ordinary PID controller. Moreover, with this controller implemented, the developed precision positioning system achieves better repeatability (+/-2?m) and accuracy (+/-2.5?m) within the full range than open-loop system using step motor.
NASA Astrophysics Data System (ADS)
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
A reinforcement learning-based architecture for fuzzy logic control
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1992-01-01
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.
Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao
2015-02-01
This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research. PMID:25608292
Research on cam grinding process used on-line variable velocity based on fuzzy control theory
Peng Baoying; Han Qiushi
2010-01-01
To get to constant force control is very important in non-circular parts manufacting. By gathering the singal from force transducer, adopt grinding force and its rate of change and C axis velocity to developing a fuzzy models. The fuzzy program codes is wrote in several PLC programs. By the on-line fuzzy caculating, the final C Axis velocity is got and
Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1996-01-01
Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.
Adaptive fuzzy leader clustering of complex data sets in pattern recognition
NASA Technical Reports Server (NTRS)
Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
NASA Technical Reports Server (NTRS)
Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza
2003-01-01
Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.
Adaptation of CDMA soft handoff thresholds using fuzzy inference system
Bongkarn Homnan; V. Kunsriruksakul; W. Benjapolakul
2000-01-01
This paper proposes a new procedure to adjust soft handoff thresholds (T-DROP, T-ADD) by using a fuzzy inference system (FIS). The aims are to increase the value of T-DROP in order to release the traffic channel (TCH) at high traffic loads for increasing the carried traffic, and to decrease the value of T-DROP in order to add the TCH in
Trajectory priming with dynamic fuzzy networks in nonlinear optimal control.
Becerikli, Yasar; Oysal, Yusuf; Konar, Ahmet Ferit
2004-03-01
Fuzzy logic systems have been recognized as a robust and attractive alternative to some classical control methods. The application of classical fuzzy logic (FL) technology to dynamic system control has been constrained by the nondynamic nature of popular FL architectures. Many difficulties include large rule bases (i.e., curse of dimensionality), long training times, etc. These problems can be overcome with a dynamic fuzzy network (DFN), a network with unconstrained connectivity and dynamic fuzzy processing units called "feurons." In this study, DFN as an optimal control trajectory priming system is considered as a nonlinear optimization with dynamic equality constraints. The overall algorithm operates as an autotrainer for DFN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. For this, DFN encapsulates and generalizes the optimal control trajectories. By the algorithm, the time-varying optimal feedback gains are also generated along the trajectory as byproducts. This structure assists the speeding up of trajectory calculations for intelligent nonlinear optimal control. For this purpose, the direct-descent-curvature algorithm is used with some modifications [called modified-descend-controller (MDC) algorithm] for the nonlinear optimal control computations. The algorithm has numerically generated robust solutions with respect to conjugate points. The minimization of an integral quadratic cost functional subject to dynamic equality constraints (which is DFN) is considered for trajectory obtained by MDC tracking applications. The adjoint theory (whose computational complexity is significantly less than direct method) has been used in the training of DFN, which is as a quasilinear dynamic system. The updating of weights (identification of DFN parameters) are based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Simulation results are given for controlling a difficult nonlinear second-order system using fully connected three-feuron DFN. PMID:15384531
How to control if even experts are not sure: Robust fuzzy control
NASA Technical Reports Server (NTRS)
Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert; Tolbert, Dana
1992-01-01
In real life, the degrees of certainty that correspond to one of the same expert can differ drastically, and fuzzy control algorithms translate these different degrees of uncertainty into different control strategies. In such situations, it is reasonable to choose a fuzzy control methodology that is the least vulnerable to this kind of uncertainty. It is shown that this 'robustness' demand leads to min and max for &- and V-operations, to 1-x for negation, and to centroid as a defuzzification procedure.
Water bath temperature control by a recurrent fuzzy controller and its FPGA implementation
Chia-Feng Juang; Jung-Shing Chen
2006-01-01
A hardware implementation of the Takagi-Sugeno-Kan (TSK)-type recurrent fuzzy network (TRFN-H) for water bath temperature control is proposed in this paper. The TRFN-H is constructed by a series of recurrent fuzzy if-then rules built on-line through concurrent structure and parameter learning. To design TRFN-H for temperature control, the direct inverse control configuration is adopted, and owing to the structure of
Reghunadhan Rajesh; M. Ramachandra Kaimal
2007-01-01
In this paper a new Takagi–Sugeno (T–S) fuzzy model with nonlinear consequence (TSFMNC) is presented which can approximate a class of smooth nonlinear systems, nonlinear dynamical systems and nonlinear control systems. It is also proved that Takagi–Sugeno fuzzy controller with nonlinear consequence (TSFCNC) can be used to approximate a class of nonlinear state-feedback controllers using the so-called parallel distributed compensation
Radu-Emil Precup; Stefan Preitl; Gabriel Faur
2003-01-01
The paper presents two structures of PI predictive fuzzy controllers (PI-P-FCs) with first- and second-order prediction. The PI-P-FCs are meant for the speed control of electrical drives with variable inertia used in several industrial applications. The new development method for these PI-P-FCs is based on guaranteeing a desired domain for the “phase margin” of the fuzzy control systems (FCSs) with
A reinforcement neuro-fuzzy combiner for multiobjective control.
Lin, C T; Chung, I F
1999-01-01
This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme. PMID:18252353
Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei
2014-04-01
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively. PMID:23782841
A novel SMC-fuzzy speed controller for permanent magnet brushless DC motor
Hailong Song; Yong Yu; Ming Yang; Dianguo Xu
2003-01-01
This paper presents a novel SMC-fuzzy speed controller for permanent magnet brushless DC motor (BLDCM). The proposed controller employs only the signal of speed error, avoiding the sensitivity to the noise of the acceleration signal as in the conventional sliding mode controller (SMC). It is composed of an equivalent control term, a switching term and a fuzzy control term. The
Hybrid fuzzy neural-network control for nonlinear motor-toggle servomechanism
Rong-Jong Wai
2002-01-01
This study addresses the application of a hybrid fuzzy neural network control (HFNNC) system to control a nonlinear mechanism system. First, the design procedures of the proposed HFNNC system are described in detail. In the HFNNC system, a fuzzy neural network (FNN) controller is the main tracking controller, which is used to mimic a perfect control law, and a compensated
The Design of Improved Fuzzy Controller Based on MCU for Central Air Conditioner
Yingjun Guo; Yingbao Zhao; Zengli Lu; Jianguang Liu
2008-01-01
A system structure and the procedure of fuzzy control of central air conditioner (CAC) in building are presented in this paper. Discuss the design of improved fuzzy controller about room temperature controller in detail, including the hardware design and software programming based on MCU (Micro programmed Control Unit). The functions of temperature controller are completed under the application that includes
The PLC System of Egg Powder Treatment Based on Fuzzy Control Algorithm
Yanmin Song; Zhongwei Bi; Kun Liu
2007-01-01
In this paper, we take the electric control system of egg powder treatment as an example. By means of fuzzy control technology and transducer technology, the system overcomes the instability of the system, the difficulty in parameter tuning and the problem of grading speed regulation in the traditional control field. Fuzzy controller based on PLC (programmable logic controller) direct lookup
Control of three degrees-of-freedom underactuated manipulator using fuzzy based switching
Lanka Udawatta; Keigo Watanabe; Kiyotaka Izumi; Kazuo Kiguchi
2004-01-01
A novel concept for designing a fuzzy logic-based switching controller to control underactuated manipulators is presented.\\u000a The proposed controller employs elemental controllers, which are designed in advance. Parameters of both antecedent and consequent\\u000a parts of a fuzzy indexer are optimized by using evolutionary computation. Design parameters of the fuzzy indexer are encoded\\u000a into chromosomes, i.e., the shapes of the Gaussian
Nonlinear fuzzy control of a fed-batch reactor for penicillin production
Bartolomeo Cosenza; Mosè Galluzzo
The process of penicillin production is characterized by nonlinearities and parameter uncertainties that make it difficult to control. In the paper the development and testing of a multivariable fuzzy control system that makes use of type-2 fuzzy sets for the control of pH and temperature are described. The performance of the type-2 fuzzy logic control system (T2FLCS) is compared by
Fuzzy controller training using particle swarm optimization for nonlinear system control.
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. PMID:17976603
Fuzzy graphic rule network and its application on water bath temperature control system
C. Treesatayapun; S. Uatrongjit; K. Kantapanit
2002-01-01
In this paper, a novel fuzzy neural network called fuzzy graphic rule network (FGRN) is presented. FGRN has a simple structure and the initial value of its parameters can be easily chosen based on human experience. These parameters are then adjusted during system operation using steepest descent technique. The step length or learning rate is adaptively selected to ensure system
Hardware implementation of fuzzy Petri net as a controller.
Gniewek, Les?aw; Kluska, Jacek
2004-06-01
The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented. PMID:15484905
Yu, Jinpeng; Shi, Peng; Yu, Haisheng; Chen, Bing; Lin, Chong
2014-09-01
This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained. PMID:25216493
Adaptive neuro-fuzzy fusion of sensor data
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor
2014-11-01
A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.
A NOISE ADAPTIVE FUZZY EQUALIZATION METHOD FOR PROCESSING SOLAR EXTREME ULTRAVIOLET IMAGES
Druckmueller, M., E-mail: druckmuller@fme.vutbr.cz [Institute of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology, Technicka 2, 616 69 Brno (Czech Republic)
2013-08-15
A new image enhancement tool ideally suited for the visualization of fine structures in extreme ultraviolet images of the corona is presented in this paper. The Noise Adaptive Fuzzy Equalization method is particularly suited for the exceptionally high dynamic range images from the Atmospheric Imaging Assembly instrument on the Solar Dynamics Observatory. This method produces artifact-free images and gives significantly better results than methods based on convolution or Fourier transform which are often used for that purpose.
Quadcopter see and avoid using a fuzzy controller
Miguel A. Olivares-Mendez; Luis Mejias; Pascual Campoy; Ignacio Mellado-Bataller
2012-01-01
Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
Chih-min Lin; Chun-fei Hsu
2004-01-01
Wing rock is a highly nonlinear phenomenon in which an aircraft undergoes limit cycle roll oscillations at high angles of attack. In this paper, a supervisory recurrent fuzzy neural network control (SRFNNC) system is developed to control the wing rock system. This SRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN
[The control method design of thermal treatment system via fuzzy logic].
Song, Mingyang; Cai, Zhanghao; Bai, Jingfeng; Sun, Jianqi
2012-05-01
A novel system is proposed to control the liquid nitrogen cooling and radio frequency heating of tissue to achieve effective thermal ablation in the treatment using fuzzy logic controller and fuzzy logic PID type controller separately. Results of ex-vivo pig liver experiments demonstrate that this system is useful and could p control the desired treatment procedure. PMID:22916471
A composite self tuning strategy for fuzzy control of dynamic systems
NASA Technical Reports Server (NTRS)
Shieh, C.-Y.; Nair, Satish S.
1992-01-01
The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.
Dejan J. Sobajic
1997-01-01
This paper presents a design technique for a new hydropower plant controller using fuzzy set theory and artificial neural networks. The controller is suitable for real time operation, with the aim of improving the generating unit transients by acting through the exciter input, the guide vane and the runner blade positions. The developed fuzzy logic based controller (FLC) whose control
The Research and PLC Application of Fuzzy Control in Greenhouse Environment
Xiaobo Zhou; Chengduan Wang; Hong Lan
2009-01-01
Aimed at the single control parameter and low degree of automation of traditional green house, the paper researched fuzzy control system based on multi-factor control of green house. With real-time monitoring environmental parameters, and based on the physiological characteristics of crops and the reaction of the environmental conditions in the green house, the fuzzy control algorithm is applied to PLC
Autonomous fuzzy parking control of a car-like mobile robot
Tzuu-hseng S. Li; Shih-jie Chang
2003-01-01
This paper is devoted to design and implement a car-like mobile robot (CLMR) that possesses autonomous garage-parking and parallel-parking capability by using real-time image processing. For fuzzy garage-parking control (FGPC) and fuzzy parallel-parking control (FPPC), feasible reference trajectories are provided for the fuzzy logic controller to maneuver the steering angle of the CLMR. We propose two FGPC methods and two
Research on the fuzzy predictive control for calcining temperature of the rotary cement kiln
Guo Feng; Liu Bin; Hao Xiaochen; Gao Peng
2010-01-01
According to the analysis of the characteristics of time-varying and nonlinear, long delays in industry processes, a fuzzy predictive controller based on the T-S fuzzy model predictive control algorithm is designed for calcining temperature of the rotary cement kiln in this paper. First, a T-S fuzzy model for temperature control system is constructed. The input variables are divided according to
Predictive functional control based on fuzzy model for heat-exchanger pilot plant
I. Skrjanc; D. Matko
2000-01-01
In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong
State-Feedback Control of Fuzzy Discrete-Event Systems
Lin, Feng; Ying, Hao
2014-01-01
In a 2002 paper, we combined fuzzy logic with discrete-event systems (DESs) and established an automaton model of fuzzy DESs (FDESs). The model can effectively represent deterministic uncertainties and vagueness, as well as human subjective observation and judgment inherent to many real-world problems, particularly those in biomedicine. We also investigated optimal control of FDESs and applied the results to optimize HIV/AIDS treatments for individual patients. Since then, other researchers have investigated supervisory control problems in FDESs, and several results have been obtained. These results are mostly derived by extending the traditional supervisory control of (crisp) DESs, which are string based. In this paper, we develop state-feedback control of FDESs that is different from the supervisory control extensions. We use state space to describe the system behaviors and use state feedback in control. Both disablement and enforcement are allowed. Furthermore, we study controllability based on the state space and prove that a controller exists if and only if the controlled system behavior is (state-based) controllable. We discuss various properties of the state-based controllability. Aside from novelty, the proposed new framework has the advantages of being able to address a wide range of practical problems that cannot be effectively dealt with by existing approaches. We use the diabetes treatment as an example to illustrate some key aspects of our theoretical results. PMID:19884087
Design for temp-humidity control system of tobacco parching house based on Fuzzy-PID control
Wangbiao Qiu; Zhiyuan Qiu
2006-01-01
The paper aimed at tobacco parching house and introduced a smart temp-humidity control system for some tobacco planted area in Guizhou province. Writer adopted fuzzy-PID error control technology and adjusted power-weight of fuzzy and PID control on-time based on variety of measurement and setup value. Writers adopted digital temperature sensor, PHILIPS LM75A and AT89C51 to construct controller of fuzzy parameter
Lam, H K
2012-02-01
This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach. PMID:21900076
Hybrid Adaptive Flight Control with Model Inversion Adaptation
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
Han, Seong-Ik; Lee, Jang-Myung
2014-01-01
This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. PMID:24055100
Fuzzy control for a nonlinear mimo-liquid level problem
Smith, R. E. (Ronald E.); Mortensen, F. N. (Fred N.); Wantuck, P. J. (Paul J.); Parkinson, W. J. (William Jerry),
2001-01-01
Nonlinear systems are very common in the chemical process industries. Control of these systems, particularly multivariable systems, is extremely difficult. In many chemical plants, because of this difficulty, control is seldom optimal. Quite often, the best control is obtained in the manual mode using experienced operators. Liquid level control is probably one of the most common control problems in a chemical plant. Liquid level is important in heat exchanger control where heat and mass transfer rates can be controlled by the amount of liquid covering the tubes. Distillation columns, mixing tanks, and surge tanks are other examples where liquid level control is very important. The problem discussed in this paper is based on the simultaneous level control of three tanks connected in series. Each tank holds slightly less than 0.01 m{sup 3} of liquid. All three tanks are connected, Liquid is pumped into the first and the third tanks to maintain their levels. The third tank in the series drains to the system exit. The levels in the first and third tank control the level in the middle tank. The level in the middle tank affects the levels in the two end tanks. Many other chemical plant systems can be controlled in a manner similar to this three-tank system. For example, in any distillation column liquid level control problems can be represented as a total condenser with liquid level control, a reboiler with liquid level control, with the interactive column in between. The solution to the three-tank-problem can provide insight into many of the nonlinear control problems in the chemical process industries. The system was tested using the fuzzy logic controller and a proportional-integral (PI) controller, in both the setpoint tracking mode and disturbance rejection mode. The experimental results are discussed and comparisons between fuzzy controller and the standard PI controller are made.
Noppadol Khaehintung; P. Sirisuk
2007-01-01
This paper presents the development of maximum power point tracking (MPPT) using an adjustable self-organizing fuzzy logic controller (SOFLC) for a solar-powered traffic light equipment (SPTLE) with an integrated maximum power point tracking (MPPT) system on a low-cost microcontroller. The proposed system is integrated with a boost converter for realizing of high performance SPTLE, whose adaptability properties are very attractive
Hung-Ching Lu; Ming-Hung Chang
2009-01-01
The automatic generation fuzzy neural network (AGFNN) controller with supervisory control for permanent magnet linear synchronous motor (PMLSM) is proposed in this paper. It comprises an AGFNN controller, which has ability of rule automatic generation with on-line learning and a supervisory controller, which is designed to stabilize the system states around a bounded region. The Mahalanobis distance (M-distance) formula is
Variant-frequency fuzzy controller for air conditioning driver by programmable logic controller
Po-Jen Cheng; Chin-Hsing Cheng; Tzai-Shiang Chang
2011-01-01
To control the temperature and humidity in a room with a specific area, the traditional control method of air conditioning systems involves frequent starting and stopping of the compressor. Programmable logic controller (PLC) was applied to realize the fuzzy controlled air conditioning system. The keyboard is used to simulate temperature, the DIP Switch (DSW) is used to simulate humidity, and
Remote fuzzy logic control of networked control system via Profibus-DP
Kyung Chang Lee; Suk Lee; Man Hyung Lee
2003-01-01
This paper focuses on the feasibility of fuzzy logic control for networked control systems (NCSs). In order to evaluate its feasibility, a networked control system for servo motor control is implemented on a Profibus-DP network. The NCS consists of several independent, but interacting, processes running on two separate stations. By using this NCS, the network-induced delay is analyzed to find
Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting
2014-11-12
This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots. PMID:25398185
Rollover prevention for sport utility vehicle using fuzzy logic controller
NASA Astrophysics Data System (ADS)
Lee, Yong-hwi; Yi, Seung-Jong
2005-12-01
The purpose of this study is to develop the fuzzy logic RSC(Roll Stability Control) system to prevent the rollover for the SUV(sport utility vehicle). The SUV model used in this study is the 8-DOF model considering the longitudinal, lateral, yaw and roll motions. The longitudinal and transversal weight transfers are considered in the computation of the vertical forces acting on a wheel. The engine torque is obtained from the throttle position and the r.p.m. of the engine map. The fuzzy logic controller input consists of the roll angle error and its derivative. The output is the brake torque and the throttle angle. The engine torque controller controls the throttle valve angle. The brake controller independently controls both right and left wheels. When the roll angle is +/-4.5° defined as the critical roll angle, the front inner tire experiences the 1/100 ~ 1/50 of the total vertical forces, and the rollover starts. To prevent the rollover in advance, the target angle +/-4.5° is adopted to control the vehicle stability. The RSC system begins operating at +/-4.5° and stops at 0°. The simulations are conducted to evaluate the controller performance at right turns for the excessive steering angle. When the roll angle error and its derivative exceed the limited point, the RSC system makes the longitudinal velocity of the SUV decrease the brake torque and adjusts the throttle angle. The roll motion of the SUV is then stabilized.
Fuzzy control with genetic algorithm in a batch bioreactor.
Ahio?lu, Suna; Altinten, Ayla; Ertunç, Suna; Erdo?an, Sebahat; Hapo?lu, Hale
2013-12-01
In this study, the growth medium temperature in a batch bioreactor was controlled at the set point by using fuzzy model-based control method. Fuzzy control parameters which are membership functions and relation matrix were found using genetic algorithm. Heat input given from the immersed heater and the cooling water flow rate were selected as the manipulated variables in order to control the growth medium temperature in the bioreactor. Controller performance was tested in the face of different types of input variables. To eliminate the noise on the temperature measurements, first-order filter was used in the control algorithm. The achievement of the temperature control was analyzed in terms of both microorganism concentration which was reached at the end of the stationary phase and the performance criteria of Integral of the Absolute Error. It was concluded that the cooling flow rate was suitable as manipulated variable with regard to microorganism concentration. On the other hand, performance of the controller was satisfactory when the heat input given from the immersed heater was manipulated variable. PMID:24037514
Multi-objective fuzzy-GA formulation for optimal placement and sizing of shunt FACTS controller
A. R. Phadke; Manoj Fozdar; K. R. Niazi
2009-01-01
The location and sizing of FACTS controllers for voltage stability enhancement is an important consideration for practical power systems. In this paper, a strategy for placement and sizing of shunt FACTS controller using Fuzzy logic and Real Coded Genetic Algorithm is proposed. A fuzzy performance index based on distance to saddle-node bifurcation, voltage profile and capacity of shunt FACTS controller