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A methodology for designing adaptive hierarchical fuzzycontrollers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzycontroller to accommodate the variations of system parameters. The proposed algorithm
A methodology for designing adaptive hierarchical fuzzycontrollers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzycontroller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach
Raju, G.V.S.; Jun Zhou [Univ. of Texas, San Antonio, TX (United States). Div. of Engineering
In this paper, by using the concept of sliding mode control design and Lyapunov synthesis approach, we propose an indirect adaptivefuzzy sliding mode control (IAFSMC) scheme for a class of nonlinear systems. In contrast to the existing sliding mode fuzzycontrol system designs, where the sliding mode control law is directly substituted by a fuzzycontroller, in our approach
An adaptivefuzzy sliding-mode control design method is proposed for induction servomotor system control. The proposed adaptivefuzzy sliding-mode control system is comprised of a fuzzycontroller and a compensation controller. The fuzzycontroller is the main tracking controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the
Advances in nonlinear control theory have provided the mathematical foundations necessary to establish conditions for stability of several types of adaptivefuzzycontrollers. However, very few, if any, of these techniques have been compared to conventional adaptive or nonadaptive nonlinear controllers or tested beyond simulation; therefore, many of them remain as purely theoretical developments whose practical value is difficult to
R. Ordonez; Jon Zumberge; Jeffrey T. Spooner; Kevin M. Passino
In this study, an adaptivefuzzy sliding-mode control (AFSMC) system with an integral-operation switching surface is adopted to control the position of an electrical servo drive. The AFSMC system is comprised of a fuzzycontrol design and a hitting control design. In the fuzzycontrol design a fuzzycontroller is designed to mimic a feedback linearization (FL) control law. In
This paper develops an adaptivefuzzycontroller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein
This paper presents an application of an adaptivefuzzy system for compensating the effects induced by the friction in mechanical system. An adaptivefuzzy system based on fuzzy basis functions is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The hybrid-controller is a combination of a PD controller and an
An adaptivefuzzycontroller 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 adaptivefuzzycontrollers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable
In this paper, the fuzzy approximator and sliding mode control (SMC) scheme are considered. We propose two methods of adaptive SMC schemes that the fuzzy logic systems (approximators) are used to approximate the unknown system functions in designing the SMC of nonlinear system. In the first method, a fuzzy logic system is utilized to approximate the unknown function f of
Stable direct and indirect adaptivecontrollers are presented, which use Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous-time nonlinear plants with poorly understood dynamics. The indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of
The paper describes the development of two different type-2 adaptivefuzzy logic controllers and their use for the control of a non linear system that is characterized by the presence of bifurcations and parameter uncertainty. Although a type-2 fuzzy logic controller is able to handle the non linearities and the uncertainties present in a system, its robustness and effectiveness can
Presents an online self-adaptive neuro-fuzzycontrol that serves as a better alternative control scheme in controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment. The proposed self-adaptive neuro-fuzzycontroller is a five-layer feedforward neural network that implements fuzzy basis function (FBF) expansions and is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of
This paper focuses on the control problem of the quadruple inverted pendulum by variable universe adaptivefuzzycontrol.\\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 adaptivefuzzycontrol theory.\\u000a Finally the simulation of the quadruple inverted
A robust adaptivefuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed. The fuzzy-neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses a fixed number of control points is developed for online tuning. The concept of tuning the adjustable vectors,
In this paper, an adaptivefuzzy sliding mode controller is proposed for a three-axis SCARA manipulator. The proposed controller possesses the advantages of adaptivecontrol, fuzzycontrol, and sliding mode control. Based on the concept of sliding mode, fuzzy rules are developed to alleviate the input chattering effectively by using the developed adaptation law. The stability of the three-axis SCARA
In this paper, we present a study aimed at assessing, by means of computer simulations, the properties of a fuzzy implementation of the model-following control scheme. We used this approach to augment position set-point tracking performances and disturbance rejection of a PID controlled robot manipulator. We found the fuzzy implementation of the compensator to be effective when parameters were adaptively
A stable adaptivefuzzy sliding-mode controller is developed for nonlinear multivariable systems with unavailable states. When the system states are not available, the estimated states from a semi-high gain observer are used to construct the output feedback fuzzycontroller by incorporating the dynamic sliding mode. It is proved that uniformly asymptotic output feedback stabilization can be achieved with the tracking
This paper presents a 15 rules-base Function Torque Adapted Gain Fuzzy Inference System (FTAGFIS) adaptive speed controller for the Permanent Magnet Synchronous Motor (PMSM). The proposed controller was developed using Adaptive Neuro Fuzzy Inference System (ANFIS). This expanded version of the FIS not only suggests the effectiveness of using the ANFIS to develop an adaptive speed controller for motors but
A fuzzyadaptive model following mechanism for the position control of a traveling-wave-type ultrasonic motor (USM) is described in this study. Since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, fuzzyadaptivecontrol is applied to design the position controller of the USM for high-performance applications. The driving circuit for the
In this paper, an adaptivefuzzycontrol method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptivefuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show
In this study, the dynamic responses of an adaptivefuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptivecontroller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control
An adaptivefuzzy logic controller is described which assists the dri ver in vehicle speed and distance control by offering a driving strategy via an active accelerator and brake pedal. In order to be accepted by the driver, the behavior of the system called ACC (Adaptive Cruise Control) has to meet the expectations of the human driver to a certain
An adaptivefuzzycontroller is synthesized from a collection of fuzzy IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms in the fuzzy IF-THEN rules are changed according to some adaptive laws for the purpose of controlling a plant to track a reference trajectory. In the paper, a direct adaptivefuzzycontrol design method is developed for
This study mainly deals with the key problem of chattering phenomena on the conventional sliding-mode control (SMC) and investigates an adaptivefuzzy sliding-mode control (AFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands. First, an indirect field-orientation method for an IM drive is introduced briefly. Moreover, a fuzzy logic inference mechanism is utilized for implementing
This work is concerned with the development of an adaptivefuzzy 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
For patients suffering from cardiogenic shock cardiopulmonary resuscitation may not be sufficient to restore normal heart function. However, their chances of survival may be increased with the use of an extracorporeal support system. With this system the patient's organs are perfused while being transported to the nearest hospital for proper treatment. In the automation of an extracorporeal support system the patient's vital signals are constantly monitored and proper adjustments are performed to improve organ perfusion. In this paper, an adaptivefuzzycontroller is proposed that uses the knowledge and expertise of a perfusionist as a starting point and reference for regulation. Furthermore it is able to adapt to the patient's specific reactions by manipulating the rule base of the fuzzycontroller. The performance of the adaptivefuzzycontroller is tested with a simulation model of the cardiovascular system. PMID:22254489
Mendoza, G A; Sprunk, N; Baumgartner, B; Schreiber, U; Bauernschmitt, S Eichhorn R; Lange, R; Krane, M; Knoll, A
This paper proposes a method of maximum power point tracking using adaptivefuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptivefuzzy logic to control a switch of a boost converter. Adaptivefuzzy logic controllers
This paper presents two kinds of adaptivecontrol schemes for robot manipulator which has the parametric uncertainties. In order to compensate these uncertainties, we use the FLS (fuzzy logic system) that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation
This paper presents a new and robust algorithm to track a robot gripper during its movement in a teleopcration task. Based on acquired irnagc and knowing the gripper modcf, the pose is obtained. This inf'ormation is used io mwvc a pan-tilt camera and keep the gripper centered in the image iisitig an adaptivefuzzy logic controller. This control law is
C. Perez; O. Reinoso; N. Garcia; R. Neco; M. A. Vicente
Overhead cranes are common industrial structures that are used in many factories and harbors. They are usually operated manually or by some conventional control methods, such as the optimal and PLC-based methods. The theme of this paper is to provide an effective all-purpose adaptivefuzzycontroller for the crane. This proposed method does not need the complex dynamic model of
A novel analog integrated circuit implementation of an adaptivefuzzy logic controller (AFLC), called variable universe fuzzy logic controller (VFLC), is presented which has not been reported before. The VFLC is a stable controller which has fewer on-line adapting parameters than the conventional AFLCs based on adaptingfuzzy rules, thus it is more suitable for hardware implementation. The input and
Weiwei Shan; Yuan Ma; Robert W. Newcomb; Dongming Jin
The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzycontroller, revealing that this type of fuzzycontroller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzycontroller structure, namely PID type fuzzycontroller
This paper considers the control of a linear drive system with friction and disturbance compensation. A stable adaptivecontroller integrated with fuzzy model-based friction estimation and switching-based disturbance compensation is proposed via Lyapunov stability theory. A TSK fuzzy model with local linear friction models is suggested for real-time estimation of its consequent local parameters. The parameters update law is derived
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 fuzzyadaptivecontrol 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.
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)
The authors present an adaptive neuro-fuzzycontroller for stabilizing an autonomous bicycle system. The controller has been designed and verified using simulation experiments in MATLAB. The controller has been found successful in balancing an autonomous bicycle system by running a generalized bicycle model under its control. The results show that it balances the bicycle within lean values of plusmn2.5deg around
This paper presents a sensorless vector control system of permanent magnet synchronous motor based on adaptive and fuzzycontrol. The proposed system is constructed in an arbitrary rotating reference frame and the speed estimator is based on the adaptive theory using the current error. Additionally, the fuzzycontrol theory is applied to this system for improving the stability and dynamic
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 adaptivefuzzy 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 adaptivefuzzy 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.
In this article, a robust adaptive self-structuring fuzzycontrol (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptivecontroller and a self-structuring fuzzycontroller. In the self-structuring fuzzycontroller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can
This paper presents a hybrid adaptivefuzzycontrol of bio-robotic leg. A mathematical dynamic model of bio-robotic leg having three links i.e. thigh, shank and foot is considered here. The Lagrange-Euler formulation is used to obtain the dynamic equations of motion for calculating torques at various joints. Since the dynamics of leg is a highly complex and nonlinear system, hybrid
This paper presents a statistical Process Control (SPC) method based on Fuzzy ART (adaptive Resonance Theory) neural network. The Fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms. It is shown that the Fuzzy ART neural network can adaptively learn the features of the histograms of the
A method based on direct adaptivefuzzycontrol was proposed according to upstanding-balancing control problem for two-wheeled upstanding robot. Different from the fuzzycontrol, it didn't need to design the fuzzy rules in the beginning. And there was not strict limit to the constant before input. Experiments showed that the constant could be positive and negative, and could be a
Xiaogang Ruan; Jing Chen; Jianxian Cai; Lizhen Dai
In this paper, both fuzzyadaptive state feedback and output feedback sliding mode controllers are developed. In designing, fuzzy logic systems are used to model the unknown nonlinear system, and a sliding mode control scheme is proposed for SISO nonlinear systems. Both state feedback and output feedback control laws and parameter update laws are derived. By using Lyapunov theory, the
This article presents the application of fuzzyadaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzyadaptive systems is then described. Experimental measurements of
Donald S. Reay; Mehran Mirkazemi-Moud; Tim C. Green; Barry W. Williams
This paper presents a methodology of asymptotically synchronizing two uncertain generalized Lorenz systems via a single continuous composite adaptivefuzzycontroller (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. PMID:22757551
Based on integrating the property of sliding mode control (SMC) with the thought of variable universe in adaptivefuzzycontrol, a design method of variable universe adaptivefuzzy sliding mode control (FSMC) strategy for uncertain chaotic systems is proposed. There are two sets of control rule bases. The first set is utilized to approach the equivalent control of SMC. By
In this study, four adaptive neural network based fuzzy logic controllers (ANNFL) are designed and used as two controllers in terms of interval type-2 fuzzy logic control. The new controllers are called as adaptive neural network based interval type-2 fuzzy logic controller (ANNIT2FL) and applied to a rigid-flexible robot manipulator. Initially dynamic model of the manipulator is obtained by using
Umit Onen; Mete Kalyoncu; Mustafa Tinkir; Fatih Mehmet Botsali
This paper introduces a brushless drive system with an adaptivefuzzy-neural-network controller. First, a neural network-based architecture is described for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. Then, the fuzzy rules and input-output of the system are tuned by the supervised gradient decent
This paper presents a stable neuro-fuzzy (NF) adaptivecontrol approach for the trajectory tracking of the robotic manipulator with poorly known dynamics. Firstly, the fuzzy dynamic model of the manipulator is established using the Takagi-Sugeno (T-S) fuzzy framework with both structure and parameters identi\\/ed through input=output data from the robot control process. Secondly, based on the derived fuzzy dynamics of
This brief proposes hybrid stable adaptivefuzzycontroller design procedures utilizing the conventional Lyapunov theory and, the relatively newly devised harmony search (HS) algorithm-based stochastic approach. The objective is to design a self-adaptivefuzzycontroller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory performance with a
Kaushik Das Sharma; Amitava Chatterjee; Anjan Rakshit
In this paper a novel fuzzy logic based multiple reference model adaptivecontroller approach for the position control of a single link robotic manipulator is presented. The proposed fuzzy logic scheme is used for generating multiple reference models, within the model reference adaptivecontrol (MRAC) framework, in response to changes in modes of operation or modal swings due to manipulator
Sukumar Kamalasadanl; Adel A. Ghandakly; K. S. Al-Olimat
In this paper, the adaptivefuzzy-neural controllers tuned on- line for a class of unknown nonlinear dynamical systems are proposed. To approximate the unknown nonlinear dynamical systems, the fuzzy-neural approximator is established. Furthermore, the control law and update law to tune on-line both the B-spline membership functions and the weighting factors of the adaptivefuzzy-neural controller are derived. Therefore, the
This study develops an adaptive speed controller from the adaptive neuro-fuzzy inference system (ANFIS) for an indirect field-oriented\\u000a (IFO) induction motor drive. First, a two-degree-of-freedom controller (2DOFC) is designed quantitatively to meet the prescribed\\u000a speed command tracking and load regulation responses at the nominal case. When system parameters and operating conditions\\u000a vary, the prescribed control specifications cannot be satisfied. To
Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzycontrol algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzycontrollers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzycontrollers perform better than only conventional/adaptivecontrollers and in particular hybrid controller structure consisting of adaptive neuro-fuzzycontroller and critically damped inverse dynamics controller. PMID:19523623
The proper execution of agricultural robotic tasks needs the use of adaptivecontrol techniques. This fact is mainly due to the nature of the systems to control, which are difficult-to-model and time-varying systems. After a review of previous works concerning adaptivecontrol, a solution using a fuzzyadaptivecontroller is studied for the joint control of such robots. An analytic
Christophe Collewet; Guylaine Rault; Stéphane Quellec; Philippe Marchal
This paper presents an adaptive modeling and control scheme for blood pressure regulation based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural
This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt on line to changes and uncertainties in these systems. It offers salient features, such as dynamic fuzzy
In this paper, we develop a fuzzy logic system with sliding mode control for unknown nonlinear system. First, an observer for unknown tracking error is designed to obtain a sliding surface. Furthermore, the fuzzy system are employed to approximate the unknown nonlinear functions with e-modification learning laws to ensure the boundedness of estimated parameters. It is proved that the overall
In this paper, a robust speed controller for an induction motor is proposed. The speed controller consists of a fuzzy sliding adaptivecontroller (FSAC) and a sliding mode torque observer (SMTO). The FSAC removes the problem of oscillations caused by discontinuous inputs of the sliding mode controller. The controller also provides robust characteristics against parameter and sampling time variations. Although,
Byung-Do Yoon; Yoon-Ho Kim; Chan-Ki Kim; Choon-Sam Kim
This paper proposes a new adaptivefuzzy active disturbance rejection control design technique on permanent magnet synchronous motor (PMSM). Fuzzy logic control is applied to adjust the proportional coefficient of nonlinear proportional error control (NLPE). The extended state observer (ESO) is used to track and estimate the uncertainty of PMSM system including unmodeled dynamics, load disturbances and parameter perturbations. The
An adaptivefuzzycontrol 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
We propose a novel fuzzy-based method of adaptive multimodal vibration suppression with limited structural data. The adaptivecontrol 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 adaptivecontrols 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 nonlinear controller based on a fuzzy model of MISO dynamical systems is described and analysed. Fuzzy sets and fuzzy inference to combine mathematical models in order to construct a nonlinear model of the system are used. The fuzzy rule base consists of a set of linguistic rules in the form of “IF a set of conditions are satisfied, THEN
We describe a general method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show
This paper considers adaptivecontrol of parallel manipulators combined with fuzzy-neural network algorithms (FNNA). With\\u000a this algorithm, the robustness is guaranteed by the adaptivecontrol law and the parametric uncertainties are eliminated.\\u000a FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying\\u000a the weight of fuzzy rules and present these rules to
In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. The paper describes the development of an adaptivefuzzycontrol strategy for tracking the DO reference trajectory applied to the Benchmark Simulation Model n.1. The design methodology of this data-driven controller uses the Lyapunov synthesis approach with a parameter projection algorithm to
Carlos Alberto Coelho Belchior; Rui Alexandre Matos Araújo; Jorge Afonso Cardoso Landeck
The observer-based integral adaptivefuzzy sliding mode controllers are developed for a class of uncertain nonlinear systems. By designing the state observer, the fuzzy systems, which are used to approach any unknown functions, it can be constructed using the state observer-based estimations. Based on Lyapunov stability theorem, the proposed integral adaptivefuzzy sliding mode control system can guarantee the stability
This paper proposes an observer-based indirect adaptivefuzzy integral sliding mode controller with state variable filters for a class of unknown nonlinear dynamic systems that not all the states are available for measurement. First, the fuzzy models for describing the input\\/output behavior of the nonlinear dynamic system are constructed. Next, an observer is applied to estimate the tracking error vector.
This paper presents a decentralized adaptivefuzzy integral sliding mode controller (DAFISMC) for a class of large-scale systems in which not all the system states are available for measurement, but the system outputs are measurable. First, we adopt a fuzzy model to approximate the large scale system, and then design an observer to estimate the system states. Next, we propose
In this paper, an adaptivefuzzy backstepping dynamic surface control (DSC) approach is developed for a class of multiple-input-multiple-output nonlinear systems with immeasurable states. Using fuzzy-logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed to estimate the immeasurable states. By combining adaptive-backstepping technique and DSC technique, an adaptivefuzzy output-feedback backstepping-control approach is developed. The proposed control method not only overcomes the problem of "explosion of complexity" inherent in the backstepping-design methods but also overcomes the problem of unavailable state measurements. It is proved that all the signals of the closed-loop adaptive-control system are semiglobally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach. PMID:21317084
In this paper, an adaptivefuzzy switched swing-up and sliding controller (AFSSSC) is proposed for the swing-up and position controls of a double-pendulum-and-cart system. The proposed AFSSSC consists of a fuzzy switching controller (FSC), an adaptivefuzzy swing-up controller (FSUC), and an adaptive hybrid fuzzy sliding controller (HFSC). To simplify the design of the adaptive HFSC, the double-pendulum-and-cart system is reformulated as a double-pendulum and a cart subsystem with matched time-varying uncertainties. In addition, an adaptive mechanism is provided to learn the parameters of the output fuzzy sets for the adaptive HFSC. The FSC is designed to smoothly switch between the adaptive FSUC and the adaptive HFSC. Moreover, the sliding mode and the stability of the fuzzy sliding control systems are guaranteed. Simulation results are included to illustrate the effectiveness of the proposed AFSSSC. PMID:19661002
Tao, Chin Wang; Taur, Jinshiuh; Chang, J H; Su, Shun-Feng
In order to solve the slightly complicated structure and the big calculation of integral variable universe adaptivefuzzycontroller, as well as the difficulty to choose the contraction-expansion factor, a weighted sum-type variable universe adaptivefuzzycontroller is designed. At the same time the general method on how to choose contraction-expansion factor is given. First, based on weighted sum principle,
Gong Xiaofeng; Li Hongxing; Deng Guannan; Guo Haigang
A neuro-fuzzyadaptivecontrol approach for nonlinear dynamical systems, which are coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact
Andon V. Topalov; Erdal Kayacan; Yesim Oniz; Okyay Kaynak
This paper presents the utilization of a self-adaptive recurrent neuro-fuzzycontrol as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then it utilized as a
This paper presents the utilization of a self-adaptive recurrent neuro-fuzzycontrol as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then utilized as a feedforward
Different gait patterns of stroke patients cannot be derived satisfactorily by traditional treadmill training robots. This paper presents a method to generate adaptive trajectories for controlling a lower extremity rehabilitation exoskeleton designed to help patients recover or improve walking ability. The model-based adaptation mechanism that consists of an inverse dynamic model, a trajectory generator and a fuzzyadaptation algorithm is
An adaptivefuzzy sliding-mode control (AFSMC) is presented for the robust antisway trajectory tracking of overhead cranes subject to both system uncertainty and actuator nonlinearity. First, a fuzzy sliding-mode control (FSMC) law is designed for the antisway trajectory tracking of the nominal plant. In association with a conventional trajectory tracking control law, this FSMC law guarantees asymptotic stability as well
This paper presents a novel parameter adjustment scheme to improve the robustness of fuzzy sliding-mode control achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) architecture. The proposed scheme utilizes fractional-order integration in the parameter tuning stage. The controller parameters are tuned such that the system under control is driven toward the sliding regime in the traditional sense.
For the complex controlled object with time-delay and time-varying, this paper presents an adaptiveFuzzy-Smith control system, combining the fuzzy logic controller optimized by Genetic algorithms with the Smith Predictor. The least-square parameter online identification technology is adopted to accomplish the self-adjusting of Smith Predictor according to the parameters variation of the object model. Meanwhile, the improved genetic algorithm adjusts
A severe nonlinearity of hydraulic pump system with switched reluctance motor (SRM) drive makes it hard to get a good control performance with a conventional PID controller. A novel instantaneous pressure control is presented in this paper. The proposed hydraulic pump system embeds instantaneous pressure controller and advanced current controller. Fuzzy logic based adaptive PID control strategy is developed according
This paper presents a real case study of warehouse replenishment process optimization on a selected sample of representative\\u000a materials. Optimization is performed with simulation model supported by inventory control algorithms. The adaptivefuzzy inventory\\u000a control algorithm based on fuzzy stock-outs, highest stock level and total cost is introduced. The algorithm is tested and\\u000a compared to the simulation results of the
Davorin Kofja?; Miroljub Kljaji?; Andrej Škraba; Blaž Rodi?
This paper is concerned with the problem of adaptivefuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptivefuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptivefuzzycontroller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme. PMID:23757518
A robust servo system is presented for the purpose of maintaining a good speed tracking, load regulating responses and unknown disturbance regulating responses. The proposed system, based on a feedforward control theory, consists of fuzzy sliding adaptivecontroller (FSAC), sliding mode torque observer (SMTO) and parameter controller (PC). FSAC is a sliding mode controller whose structure is continuously changed by
Byung-Do Yoon; Yoon-Ho Kim; Hong-Woo Rhew; Chan-Ki Kim
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
In this paper, a new robust adaptivecontrol 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
In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the
In this paper, an adaptivefuzzy PID control scheme is investigated for a class of nonlinear systems with Hinfin tracking performance. Stability and robustness of the control scheme is analyzed in Lyapunov sense and a modified Riccati-like equation. It is shown that the proposed control scheme can guarantee parameter estimation convergence and stability robustness of the closed-loop system with Hinfin
An indirect adaptivefuzzy predictive control method is presented for attitude tracking of satellites with model uncertainty and external disturbances in this paper. Firstly, the satellite attitude tracking error equation is formulated with quaternion, and a nonlinear predictive control law for attitude error quaternion model of satellites is derived. Then, the uncertain section in predictive control law from system model
This paper focuses on the development of adaptivefuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems. PMID:18784015
An adaptivefuzzycontroller 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 adaptivefuzzycontrol scheme results in H? tracking performance. The efficiency of the proposed adaptivefuzzycontrol scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.
The use of an adaptivefuzzycontrol algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptivefuzzycontroller is similar to that of a neural network. The performance of the fuzzycontroll...
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 fuzzycontrol based on T-S model, the self-adaptivefuzzy 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.
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 fuzzycontrol law can suppress the rotor imbalance-induced vibration and has excellent capability for high-speed tracking and levitation control. PMID:15462450
It is firstly established the database of multi-point vibration signal inside carriage when ammunition is carried by military transportation vehicle in different road conditions, as input signal of simulation system. The design of a fuzzyadaptive PID controller is done, which finds a solution to re-set the PID parameters for wear and aging of equipment, and then hydraulic servo system
Duan Yun-long; Han Bao-hong; Qi Jing-li; Ma Ying-chen
An adaptive neuro-fuzzy (ANF) method is developed for the supply air pressure control loop of a heating, ventilation and air-conditioning (HVAC) system. Although a well-tuned PID controller performs well around normal working points, its tolerance to process parameter variations is severely affected due to the nature of PID controllers. The ANF controller developed overcomes this weakness. The controller design involves
There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adaptive cruise control (ACC). The main features of this kind of controller are
José Eugenio Naranjo; Carlos González; Jesús Reviejo; Ricardo García Rosa; Teresa De Pedro
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
This paper deals with a hybrid active power filter with injection circuit (IHAPF). It exhibits clear promise in decreasing harmonics and increasing the power factor with a comparatively low capacity active power filter. This paper concludes that the stability of the IHAPF based on spotting supply current is exceptional to that of others. To minimize the capacity of IHAPF, an adaptivefuzzy dividing frequency control method is used, which consists of two control units: a generalized integrator control unit and fuzzy adjustor unit. The generalized integrator is used for dividing the frequency integral control, while fuzzy arithmetic is used for adjusting proportional-integral coefficients timely. And the control method is generally useful and applicable to any other active filters. Compared to other IHAPF control methods, the adaptivefuzzy dividing frequency control shows the advantages of shorter response time and higher control precision. It is implemented in an IHAPF with a 100-k VA APF installed in a copper mill in Northern China. The simulation and experimental results show that the new control method is not only easy to be calculated and implemented, but also very effective in reducing harmonics.
The design and properties of an adaptive enhanced fuzzy sliding-mode control (AEFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands are addressed in this study. A newly designed EFSMC system, in which a translation-width idea is embedded into the FSMC, is introduced initially. Moreover, to confront the uncertainties existed in practical applications, an adaptive tuner,
This paper proposes novel adaptivefuzzy 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 AdaptiveFuzzy 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
In this study an adaptivefuzzy-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 adaptivecontroller. 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 adaptivecontroller. In addition, the effectiveness of the adaptivefuzzy-neural-network (AFNN) controlled USM drive system is demonstrated by some experimental results. PMID:18238472
This paper addresses the design of a fuzzycontroller for the control of the wire electrical discharge machining (WEDM) process. The power consumption and short circuit ratio are chosen as control parameters. Incorporated with pulse trains analysis and experience, fuzzy rules for the control of the WEDM process are formulated. A DSP-based on-line pulse monitoring system is developed, and the
This paper proposes an observer-based indirect adaptivefuzzy sliding mode controller with state variable filters for a certain class of unknown nonlinear dynamic systems in which not all the states are available for measurement. To design the proposed controller, we first construct the fuzzy models to describe the input\\/output behavior of the nonlinear dynamic system. Then, an observer is employed
This paper presents and analyzes a cascade direct adaptivefuzzycontrol (DAFC) scheme for a two-axis inverted-pendulum servomechanism. Because the dynamic characteristic of the two-axis inverted-pendulum servomechanism is a nonlinear unstable nonminimum-phase underactuated system, it is difficult to design a suitable control scheme that simultaneously realizes real-time stabilization and accurate tracking control, and it is not easy to directly apply
The fuzzy logic adaptivecontroller 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.
In this study an adaptivefuzzy-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
We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer
This paper proposes a novel methodology for autonomous mobile robot navigation utilizing the concept of tracking control. Vision-based path planning and subsequent tracking are performed by utilizing proposed stable adaptive state feedback fuzzy tracking controllers designed using the Lyapunov theory and particle-swarm-optimization (PSO)-based hybrid approaches. The objective is to design two self-adaptivefuzzycontrollers, for $x$-direction and $y$-direction movements, optimizing
Kaushik Das Sharma; Amitava Chatterjee; Anjan Rakshit
An improved algorithm design methodology of vehicle airbag deployment decision is proposed in this paper. Firstly, the vehicle impact severity is analyzed to get four characteristic factors utilized as fuzzy inputs. From these four characteristics factors, the ‘two stage fuzzy algorithm’ is developed and used as the airbag deployment algorithm for identifying the vehicle impact severity. Finally, the adaptive-network-based fuzzy
In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.
In this paper, an adaptivefuzzy fault accommodation (FA) control design with a guaranteed L(?)-gain performance is developed for a class of nonlinear time-delay systems with persistent bounded disturbances. Using the Lyapunov technique and the Razumikhin-type lemma, the existence condition of the L(?) -gain adaptivefuzzy FA controllers is provided in terms of linear matrix inequalities (LMIs). In the proposed FA scheme, a fuzzy logic system is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown fault function; a continuous-state feedback control strategy is adopted for the control design to avoid the undesirable chattering phenomenon. The resulting FA controllers can ensure that every response of the closed-loop system is uniformly ultimately bounded with a guaranteed L(?)-gain performance in the presence of a fault. Moreover, by the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(?)-gain. Finally, the achieved simulation results on the FA control of a continuous stirred tank reactor (CSTR) show the effectiveness of the proposed design procedure. PMID:21177158
This paper proposes an adaptivefuzzy sliding mode control (AFSMC) to synchronize two different uncertain fractional-order time-delay chaotic systems, which are infinite dimensional in nature, and time delay is a source of instability. Because modeling the behavior of dynamical systems by fractional- order differential equations has more advantages than integer- order modeling, the adaptive time-delay fuzzy-logic system is constructed to
In this paper, the proposed observer-based robust adaptivefuzzy sliding mode control scheme can deal with the problem of robust stability for a class of uncertain multi-input multi-output (MIMO) nonlinear systems whose states are not available. The fuzzy basis function is used to approximate an unknown nonlinear function according to some adaptive laws, and then the state observer is designed
In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the
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 adaptivecontrol 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 fuzzycontrol methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzycontrol versus conventional control.
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 adaptivecontrol 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 fuzzycontrol methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzycontrol versus conventional control.
A fuzzy cognitive map is a graphical means of representing arbitrarily complex models of interrelations between concepts.\\u000a The purpose of this paper is to describe an adaptivefuzzy cognitive map based on the random neural network model. Previously,\\u000a we have developed a random fuzzy cognitive map and illustrated its application in the modeling of processes. The adaptive\\u000a fuzzy cognitive map
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 adaptivefuzzycontrol 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 adaptivefuzzycontrol algorithms can significantly enhance the control performance.
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
This paper presents a new robust adaptivecontrol 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
\\u000a This paper address the kinematic variables control problem for the low-speed manoeuvring of a low cost and underactuated underwater\\u000a vehicle. Control of underwater vehicles is not simple, mainly due to the non-linear and coupled character of plant equations,\\u000a the lack of a precise model of vehicle dynamics and parameters, as well as the appearance of internal and external perturbations.\\u000a The
For the attitude stabilization of networked flexible spacecraft during large angle slew maneuver, a novel type of adaptivefuzzy sliding mode control (AFSMC) method for solving the dynamic model with network-induced delay, nonlinear and uncertain parameters is proposed in this paper. A novel and systematic sliding mode control (SMC) scheme, which integrates a time-advanced nonlinear predictor, is proposed to compensate for the network-induced delay and to overcome the negative effect of uncertainties. Then, an adaptivefuzzy system is used to approximate the strong coupling nonlinear dynamics between rigid hub and flexible appendages. Following that, the designed adaptive algorithms are developed in the sense of the Lyapunov stability theorem, so that system-tracking stability can be guaranteed. Finally, simulation results show that, with the application of the proposed method, not only high-precision attitude stabilization of flexible spacecraft is achieved, but also the elastic vibration of flexible spacecraft during maneuver is suppressed effectively, and the system is robust against system uncertainties, network-induced delays and any outer disturbances.
Chaos is a nonlinear behavior of chaotic system with the extreme sensitivity to the initial conditions. Chaos control is so complicated that solutions never converge to a specific numbers and vary chaotically from one amount to the other next. A tiny perturbation in a chaotic system may result in chaotic, periodic, or stationary behavior. Modern controllers are introduced for controlling the chaotic behavior. In this research an adaptiveFuzzy Logic Controller (AFLC) is proposed to control the chaotic system with two equilibrium points. This method is introduced as an adaptive progressed fashion with the full ability to control the nonlinear systems even in the undertrained conditions. Using AFLC designers are released to determine the precise mathematical model of system and satisfy the vast adaption that is needed for a rapid variation which may be caused in the dynamic of nonlinear system. Rules and system parameters are generated through the AFLC and expert knowledge is downright only in the initialization stage. So if the knowledge was not assuring the dynamic of system it could be changed through the adaption procedure of parameters values. AFLC methodology is an advanced control fashion in control yielding to both robustness and smooth motion in nonlinear system control.
In this paper, an observer-based robust adaptivefuzzy sliding mode controller (RAFSMC) for an unknown nonlinear dynamical system with dead-zone input is presented. First, the fuzzy models are used to estimate the unknown function of the nonlinear dynamical system. Next, an observer is employed to estimate the tracking error. By the strictly-positive-real (SPR) Lyapunov stability theorem, it is shown that
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
Describes a general method for adaptive model-based control of non-linear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing (SC) techniques with the concept of the fractal dimension for the domain of non-linear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show
This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.
A direct adaptivecontrol algorithm, based on neural networks (NN) is presented for a class of single input single output (SISO) nonlinear systems. The proposed controller is implemented without a priori knowledge of the nonlinear systems; and only the output of the system is considered available for measurement. Contrary to the approaches available in the literature, in the proposed controller, the updating signal used in the adaptive laws is an estimate of the control error, which is directly related to the NN weights instead of the tracking error. A fuzzy inference system (FIS) is introduced to get an estimate of the control error. Without any additional control term to the NN adaptivecontroller, all the signals involved in the closed loop are proven to be exponentially bounded and hence the stability of the system. Simulation results demonstrate the effectiveness of the proposed approach. PMID:23037773
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 adaptablefuzzy force control scheme has been proposed to improve the performance. The ability of the adaptable force control
In this paper, a robust adaptive tracking control problem is discussed for a general class of strict-feedback uncertain nonlinear systems. The systems may possess a wide class of uncertainties referred to as unstructured uncertainties, which are not linearly parameterized and do not have any prior knowledge of the bounding functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate
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.
This paper proposes a on-line modeling and control approach through Takagi-Sugeno (T-S) fuzzy-neural model for a class of generalized multiple input multiple output (MIMO) nonlinear dynamic systems with external disturbances. Nonlinear systems are exactly formed a linearized system via the mean value theroem, and then the T-S fuzzy-neural model can approximate the linearized system. Then on-line identificatoin algorithm and an
In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptivefuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptivefuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.
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.
A discrete event simulation that uses a modified expert system as a controller is described. Fuzzy logic concepts from analog controllers are applied in the expert system controller to mimic human control of an airport, modeled with a combined discrete and continuous state space. The controller is adaptive so rule confidences are automatically varied to achieve near optimum system performance.
John R. Clymer; Philip D. Corey; Judith A. Gardner
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
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
This chapter proposes a novel control strategy for overhead cranes. The controller includes both position regulation and anti-swing\\u000a control. Since the crane model is not exactly known, fuzzy cerebellar model articulation controller (CMAC) is used to compensate\\u000a friction, and gravity, as well as the coupling between position and anti-swing control. Using a Lyapunov method and an input-to-state\\u000a stability technique, the
This paper proposes ANFIS logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic\\u000a modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude\\u000a of the air vehicle is controlled. The implementation framework utilizes MATLAB’s standard configuration and the Aerosim Aeronautical\\u000a Simulation Block Set which
In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzycontrol environment by using neural network learning principles. This is an extension to our work. We solve this...
\\u000a Fuzzy Cognitive Maps constitute an attractive modeling approach that encompasses advantageous features. The most pronounces\\u000a are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of\\u000a complex systems. The main deficiencies of FCMs are the critical dependence on the expert’s opinion and the potential convergence\\u000a to undesired steady states. In order to overcome these
A controller that uses fuzzy rules provides better response than a conventional linear controller in some applications. The rules are best implemented as a breakpoint function. A level control example illustrates the technique and advantages over proportional-integral (PI) control. In numerous papers on fuzzycontroller development, emphasis has been primarily on formal inferencing, membership functions, and steps in building a fuzzy relation, as described by Zadeh. The rationale used in formulating the required set of rules is usually neglected, and the interpretation of the final controller as an input-output algorithm is overlooked. Also, the details of fuzzy mathematics are unfamiliar to many engineers and the implementation appears cumbersome to most. Process description and control instrumentation. This article compares a fuzzycontroller designed by specifying a breakpoint function with a traditional PI controller for a level control system on a laboratory scale. In this discussion, only setpoint changes are considered.
Stoll, K.E.; Ralston, P.A.S.; Ramaganesan, S. (Univ. of Louisville, KY (United States))
In this paper, a nonlinear dynamic system is first approximated by N fuzzy-based linear state-space subsystems. To track a trajectory dominant by a specific frequency, the reference models with desired amplitude and phase features are established by the same fuzzy sets of the system rule. Similarly, the same fuzzy sets of the system rule are employed to design robust fuzzy
This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic. PMID:19497816
The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex\\u000a dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for\\u000a human operator and sport modeling: fixed fuzzy-logic inference control and adaptivefuzzy-logic control, including neuro-fuzzy-fractal\\u000a control. As an application of the presented fuzzy strategies,
Tijana T. Ivancevic; Bojan Jovanovic; Sasa Markovic
It is known that the performance of a fuzzycontrol system may be significantly improved if the fuzzy reasoning model is s upplemented b y a genetic-based learning mechanism. In this paper an adaptive genetic search p rocedure for optimization of membership functions' ( MF) shape forming parameters based on d irect definition of the search ranges or certainty degrees
Alexander P. Topchy; Victor V. Miagkikh; Roman N. Kononenko; Ascold N. Melikhov
Fuzzycontrollers provide adequate control which is inexpensive, easy to implement, robust and most suitable for systems which are complex, non-linear, or do not have mathematical models. Building robots and machines to act within a fuzzy environment is a problem featuring complexity and ambiguity. In order to avoid obstacles, or move away from it, the robot has to perform functions
We describe a computer program based on the use of neural networks and fuzzy logic for controlling bacteria growth in biochemical reactors for the food industry. This computer program is an implementation of a new method for control using neural networks techniques and a new method for automated mathematical modelling using fuzzy logic techniques. Biochemical processes are often highly non-linear
Crane operation for handling a heavy load inherently causes a swinging motion at the load due to acceleration or deceleration of the crane. This swing not only diminishes handling safety but also makes the load-handling time longer because the swing should be fully damped before proceeding to the next step of operation. Recently, the iron and steel industries have demanded new transportation technologies for the efficient and safe handling of heavy material such as hot coils. The Nuclear Environment Management Center (NEMAC) subsidiary institute of Korea Atomic Energy Research Institute (KAERI) has developed several types of antiswing controllers for this demand. One of these controllers, which adopts fuzzy logic, has been transferred to these industries, including POSCO, the largest iron and steel company in Korea, and several other commercial product companies. In this paper, design procedures of the fuzzycontroller and its implementation results are described. The fuzzycontroller consists of three sequential stages in which fuzzy variables are automatically changed as follows: (1) The fuzzy acceleration controller is designed to rapidly reach the desired initial velocity. As input fuzzy variables, the velocity error (between the desired velocity and the actual velocity) and the distance error (difference between the desired distance at the acceleration stage and actual distance) are adopted. (2) The fuzzy antiswing controller is designed to rapidly damp out the swing angle. In this controller the error and error change between the actual swing angle of the load and the pre-specified swing angle are adopted as input variables. (3) The fuzzy stop position controller is designed for precise positioning of the crane while damping out the residual swings before and after crane stops. In this controller all of the preceding variables, velocity, position, and swing errors are adopted as input fuzzy variables.
Yoon, Ji Sup; Park, Byung Suk; Lee, Jae Sol; Park, Hyun Soo
It is presented an adaptive scheme controlling a nonlinear model inspired in the dragonfly-like robot. It is proposed a hybrid adaptive law for adjusting the parameters combining adaptivefuzzy identification and adaptivefuzzycontrol. It is compared the performance of the direct and the hybrid adaptivefuzzycontroller of the nonlinear model based on the dragonfly dynamics. The simulation results confirm the superiority of the hybrid adaptive law showing a fast tracking error convergence and parameter convergence.
Couceiro, Micael S.; Ferreira, Nuno M. F.; Machado, J. A. Tenreiro
This paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capabi- lity of these systems to be applied as function approxima- tors. The
S. Sánchez-Solano; A. Barriga; C. J. Jiménez; J. L. Huertas
This paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capability of these systems to be applied as function approximators
S. Sanchez-Solano; A. Barriga; C. J. Jimenez; J. L. Huertas
In this paper, an observer-based fuzzy neural sliding mode control (OFNSMC) scheme for interconnected unknown chaotic systems is developed. The OFNSMC system is composed of a computation controller and a robust controller. The computation controller containing a self-structuring fuzzy neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L2 tracking performance. The SFNN
An adaptivefuzzycontrol strategy is proposed for the hypersonic aircraft. Fuzzy logic system is used to compensate the unknown nonlinearities and modeling errors caused by changes of flight conditions. The DSC method eliminates the \\
The injection pressure control plays a critical role in modern diesel engines equipped with common rail injection systems. As a consequence, a considerable amount of research and development activities exists aimed at improving the control strategies. This paper first introduces a relatively high precision, real-time capable model of the common rail diesel injection system. Then, a novel adaptivecontrol algorithm
Many real-world applications involve the filtering and estimation of process variables. This study considers the use of interpretable Sugeno-type fuzzy models for adaptive filtering. Our aim in this study is to provide different adaptivefuzzy filtering algorithms in a deterministic setting. The algorithms are derived and studied in a unified way without making any assumptions on the nature of signals
When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptivecontrol for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in
\\u000a Fuzzy Set Theory, introduced by Zadeh in 1965 , has been the subject of much controversy and debate. In recent years, it has found many applications in a variety of fields.\\u000a Among the most successful applications of this theory has been the area of Fuzzy Logic Control (FLC) initiated by the work\\u000a of Mamdani and Assilian . FLC has had
It has been proved that fuzzycontrollers are capable of approximating any real continuous control function on a compact set to arbitrary accuracy. In particular, any given linear control can be achieved with a fuzzycontroller for a given accuracy. The aim of this paper is to show how to automatically build this fuzzycontroller. The proposed design methodology is
This paper addresses the analysis and design of a fuzzycontroller 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 fuzzycontroller and the fuzzy observer can be independently designed. A numerical simulation and an experiment on an inverted pendulum
This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzycontrol methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper. PMID:20858584
The conventional two-stage training algorithm of the fuzzy/neural architecture called FALCON may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this correspondence, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm as twice as fast and produced a lower steady-state error by an order of magnitude. PMID:18252518
Today, home appliance applications require more and more features such as motor speed adaptations to multipurpose accessories, user friendly interfaces, and security features. Such new requirements can be achieved through a low-end microcontroller-based electronic control using the fuzzy logic approach. Nowadays, most of fuzzy logic-based controls are only limited to a complicated ranking management of user interfaces, sensors, and actuators,
This paper aims to develop an adaptivecontrol system for process monitoring, identification, and control in micro wire electrical\\u000a discharge machining (wire-EDM). The proportion of short circuits in the total sparks defined as short circuit ratio is chosen\\u000a as a control parameter for the adaptivecontrol system. Pulse interval of each spark is adjusted in real-time according to\\u000a the discrimination
This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.
Computational intelligence that utilizes adaptivefuzzy 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.
This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any
This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classification performance. That is, when a pattern
The use of a learning control system to maintain adequate performance of a cargo ship autopilot when there are process disturbances or variations is examined. The objective is to make an initial assessment of what advantages a fuzzy learning control approach has over conventional adaptivecontrol approaches. The simulation results indicate that the fuzzy model reference learning controller (FMRLC) has
This paper discusses an application of fuzzycontrol to an unmanned helicopter. The authors design a fuzzycontroller to achieve semi-autonomous flight of a helicopter by giving macroscopic flight commands from the ground. The fuzzycontroller proposed in...
Fuzzy logic controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with. However, the design of the fuzzy logic controllers depends to a large extent on the expert's knowledge or on trial and error. Moreover, due to the linguistic expression of the fuzzycontroller, it has not been easy to guarantee the stability
This paper presents a new genetic operator called adaptive operator to improve local portions of chromesomes. This new operator is implemented in a pseudo-bacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm (GA) with a local improvement mechanism inspired by a process in bacterial genetics. The PBGA was applied for
N. E. Nawa; T. Hashiyama; T. Furuhashi; Y. Uchikawa
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 classic...
For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives `and' and `also', and fuzzy inference mechanisms, are investigated. Defuzzification strategies, are discussed. Some of the representative applications of the FLC, from
Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the Radial Basis Function neural network (RBF) with Gaussian
The FUSICO (Fuzzy Signal Control)-research project was started in 1996 at the Helsinki University of Technology. The main goals of the project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules using linguistic variables, va...
A new pattern recognition model has been designed for ECG signal classification in general and acute myocardial infarction in specific. This model combines a fuzzy logic inference system with neural network adaptive learning. In this paper, we compare the performance of the proposed system to a neural network only model and a previously designed ECG interpretation program. The initial classification
This paper presents a neuro-fuzzy approach to the development of high-performance real-time intelligent and adaptivecontrollers for nonlinear plants. Several paradigms derived from cognitive sciences are considered and analyzed in this work, such as neural networks, fuzzy inference systems, genetic algorithms, etc. The different control strategies are also integrated with finite-state automata, and the theory of fuzzy-state automata is derived
Beatrice Lazzerini; Leonardo M. Reyneri; Marcello Chiaberge
Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and
Stepping motors are widely used in robotics and in the numerical control of machine tools, where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and
This paper addresses the design strategy of a decoupled fuzzy sliding mode controller and a fuzzy sliding mode observer on the basis of the Takagi-Sugeno's (T-S) fuzzy model. A class of fourth-order systems such as a cart-pole system could be well controlled and observed in both the pole hyperplane and the cart-hyperplane simultaneously by the proposed fuzzycontroller and observer
Using visual tracking technology by CCD sensor instead of high speed computational resources to measure the fast dynamic systems is not easy. This paper proposes a simple and effective method to do the image processing, catching this dynamic movement in real-time and controlling the overhead crane. Visual tracking based on color histograms will compare the color in a model image
Traffic policing mechanism schemes over high speed networks become significant whenever congestion arises at the entrance of buffer prior to the network. If congestion is not suitably controlled, the networks may not be able to cope their quality-of-service (QoS) requirements. Another approach must drop incoming frames from which buffer is not available and has to rely on the end-to-end protocols
A fuzzy-sliding mode controller, which is designed by the techniques of the fuzzy logic controller and the sliding mode controller (or called variable structure control), is proposed in this work. Like the sliding mode of the sliding mode control system, the fuzzy-sliding mode control system\\u000a has a fuzzy-sliding mode. The reason for calling “fuzzy-sliding mode” is that the sliding surface
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 fuzzycontrol 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.
This paper presents a novel measurement-based connection admission control (CAC) which uses fuzzy set and fuzzy logic theory. Unlike conventional CAC, the proposed CAC does not use complicated analytical models or a priori traffic descriptors. Instead, traffic parameters are predicted by an on-line fuzzy logic predictor (Qiu et al. 1999). QoS requirements are targeted indirectly by an adaptive weight factor.
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 syst...
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 dynamic fuzzy logic based adaptive algorithm is proposed for reducing the effect of stick slip friction present in 1-DOF (one degree of freedom) mechanical mass system. The control scheme proposed is an online identification and indirect adaptivecontrol, in which the control input is adjusted adaptively to compensate the effect of nonlinearity. Lyapunov stability analysis is used to ensure
In this paper, an adaptive robust Fuzzy Cerebellar Model Articulation Proportional-Integral-Derivative controller for nonlinear complicated system is presented. Embedding robust control assures the stability of system, the compensated controller eliminates the noise of environment and the uncertain. Because of the chattering control signal, the optimization and redesign adaptive laws are presented. All of them are based on Lyapunov function such
An additive fuzzy system can control the throttle of cars in single lane platoons. The system used fuzzycontrollers for velocity control and gap control. Fuzzycontrollers create, maintain, and divide platoons on the highway. Each car's controller uses data from its car and the car in front of it. Cars drop back during platoon maneuvers to avoid the “slinky
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 stochastic automata (FSA) are proposed for the control of autonomous vehicles. FSA merge the concept of sliding-mode control with fuzzy logic and have interesting robustness properties. Sufficient conditions for the convergence of the FSA control are provided.
The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzycontrol rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior
Francisco Herrera; Manuel Lozano; José L. Verdegay
Two new variable structure fuzzycontrol algorithms are presented in this paper for controlling the reactive component of the STATCOM current in a power system. The control signal is obtained from a combination of generator speed deviation and STATCOM bus voltage deviation fed to the variable structure fuzzycontroller. The parameters of these fuzzycontrollers can be varied widely by
A novel fuzzy logic secondary voltage controller based on polar fuzzy logic rule is designed in this paper. The controller uses the voltage of the pilot node as a feedback signal and adjusts voltage level of the pilot node through the excitation control with the rules of fuzzy logic. The parameter regulation, performance and principle of the controller are briefly
This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzycontrollers. Careful examination of the fuzzycontrollers designed by various engineers reveals distinctive classes of...
Special features: Fuzzycontrol engineering (Preface to special issue on fuzzycontrol engineering, Recent trends in fuzzycontrol engineering, Shell system for fuzzycontrol, Fuzzycontrol of autonomous robot, Fuzzy-control system for transportation, Fuz...
In this paper, the principle of sliding mode control (SMC) is used as a basis to develop an adaptivefuzzycontroller for uncertain time-delay systems with sector nonlinearities. The control method provides a simple way to achieve asymptotic stability of the uncertain dynamic system. Other attractive features of the method include a minimal realization of the adaptivefuzzycontroller and
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 adaptivelycontrolled 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 fuzzyadaptive 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.
Fuzzycontrol at the executive level can be interpreted as an approximation technique for a control function based on typical, imprecisely specified input-output tuples that are represented by fuzzy sets. The imprecision is characterized by similarity relations that are induced by transformations of the canonical distance function between real numbers. Taking this interpretation of fuzzycontrollers into account, in order
The ultrasonic motor has a nonlinearity, which varies with the driving conditions and possesses variable dead-zone in the control input that is associated with the applied load torque. The dead-zone is a problem in accurate positioning for actuators. To improve the control performance of the ultrasonic motor the dead-zone nonlinearity should be eliminated. This paper proposes a new position control
Tomonohu Senjyu; Tomohiro Yoshida; Katsumi Uezato; T. Funabashi
In this paper, an improved Takagi-Sugeno (T-S) Fuzzy Neural Network (FNN) based on modified learning is proposed for the motion control of Autonomous Underwater Vehicles (AUV). Aiming to improve the control precision and adaptability of T-S fuzzy model, a fuzzy objective is used to update the fuzzy rules and the proportion factor on-line. A modified learning of network is developed
Epsilon Serializability (ESR) has been proposed to manage and control inconsistency in extending the classic transaction processing. ESR increases system concurrency by tolerating a bounded amount of inconsistency. In this paper, we present multiversion divergence control (mvDC) algorithms that support ESR with not only value but also time fuzziness in multiversion databases. Unlike value fuzziness, accumulating time fuzziness is semantically
Calton Pu; Miu K. Tsang; Kun-Lung Wu; Philip S. Yu
Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic approach for steering and speed control, a neuro-fuzzy approach for ultrasound sensing (not discussed in this paper) and an overall expert system. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised by a 486 computer through a multi-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. This micro- controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected by a vision tracking device that transmits the X, Y coordinates of the lane marker to the control computer. Simulation and testing of these systems yielded promising results. This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.
Since the first fuzzy logic control system was proposed by Mamdani, many studies have been carried out on industrial process and real-time controls. The key problem for the application of fuzzy logic control is to find a suitable set of fuzzycontrol rules. Three common modes of deriving fuzzycontrol rules are often distinguished and mentioned: (1) expert experience and knowledge; (2) modeling operator control actions; and (3) modeling a process. In cases where an operator's skill is important, it is very useful to derive fuzzycontrol rules by modeling an operator's control actions. It is possible to model an operator's control behaviors in terms of fuzzy implications using the input-output data concerned with his/her control actions. The authors use the model obtained in this way as the basis for a fuzzycontroller. The authors use a finite number of fuzzy or approximate control rules. To control a robot in a cluttered reactor environment, it is desirable to combine all the methods. In this paper, the authors describe a general algorithm for a mobile robot control system with fuzzy logic reasoning. They discuss the way that knowledge of fuzziness will be represented in this control system. They also describe a simulation program interface to the K2A Cybermation mobile robot to be used to demonstrate the control system.
Hai Quan Dai; Dalton, G.R.; Tulenko, J. (Univ. of Florida, Gainesville (United States))
This paper concerns the design of robust control systems using sliding-mode control that incorporates a fuzzy tuning technique. The control law superposes equivalent control, switching control, and fuzzycontrol. An equivalent control law is first designed using pole placement. Switching control is then added to guarantee that the state reaches the sliding mode in the presence of parameter and disturbance
Q. P. Ha; Q. H. Nguyen; D. C. Rye; H. F. Durrant-Whyte
Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzycontroller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is
In this paper, a new fuzzycontroller is proposed by introducing nonlinear functions into the analytical fuzzycontroller, and the methods of to on-line adjust the rules of this fuzzycontroller is present. The application of this new fuzzycontroller in generator excitation systems is studied. The structures of the fuzzy logic controller for generator excitation control are established, through
A fuzzy power control algorithm is presented for automatic reactor power control in a pressurized water reactor (PWR). Automatic power shape control is complicated by the use of control rods with a conventional proportional-integral-differential controller because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability needed for load-following operations including frequency control. In an attempt to achieve automatic power shape control without any design modifications to the core, a fuzzy power control algorithm is proposed. For the fuzzycontrol, the rule base is formulated based on a multiple-input multiple-output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to Yonggwang Nuclear Unit 3. The simulation results show that the fuzzycontrol can be adapted as a practical control strategy for automatic reactor power control of PWRs during the load-following operations.
Hah, Y.J. (Korea Atomic Energy Research Inst., Daejon (Korea, Republic of)); Lee, B.W. (Korea Advanced Inst. of Science and Technology, Daejon (Korea, Republic of))
A data set of 412 olfactory compounds, divided into animal, camphoraceous, ethereal and fatty olfaction classes, was submitted to an analysis by a Fuzzy Logic procedure called AdaptiveFuzzy Partition (AFP). This method aims to establish molecular descriptor\\/chemical activity relationships by dynamically dividing the descriptor space into a set of fuzzily partitioned subspaces. The ability of these AFP models to
In this paper, we consider a fundamental theoretical question on why does fuzzycontrol have such a good performance for a wide variety of practical problems. We try to answer this fundamental question by proving that for each fixed fuzzy logic belonging to a wide class of fuzzy logics, and for each fixed type of membership function belonging to a
The development of an electromechanically driven total artificial heart (Helmholtz-TAH) was initiated in 1990. Anatomical fitting, biocompatibility, and automatic physiologic adaptation of pump output are the basic requirements that characterize the overall TAH concept. For evaluation of these features, a TAH labtype was developed. It provides most features of the conceptual artificial heart and supports in vitro testing of energy conversion, pump behavior, structural parts, sensors, and control concepts. A fuzzycontroller has been implemented for adaptation of the pump rate to body perfusion demand by left pump chamber filling detection. This controller will be an important element of a future extensive TAH control system. The implementation is supported by a professional fuzzycontrol development tool that allows on-line and real time optimization of control strategies for dynamic processes. The first experiments proved the feasibility and the advantages of this fuzzycontrol concept. The first in vitro test results are presented. PMID:7598656
In this paper, a hybrid neural fuzzycontrol scheme is proposed for the control of flexible-joint robot manipulators with unknown dynamics. The control strategy is based on a feedforward artificial neural network to partially approximate the manipulator's inverse dynamics. A fuzzy sliding mode feedback controller is also used for the online adaptation of the neural network-based controller. Simulation results of
Hicham Chaoui; Wail Gueaieb; M. C. E. Yagoub; Pierre Sicard
Control law partitioning is a widely used concept that incorporates a mathematical model of the plant into the control system. This is both an advantage and disadvantage. With an accurate model, the system control is much more robust and easy to manage. However, with a complex nonlinear system, an accurate mathematical model can be very difficult to obtain. A fuzzy
Adaptive Neuro-Fuzzy system for automatic multilevel image segmentation and edge detection. This system consists of a multilayer perceptron (MLP)-like network that performs image segmentation using thresholds automatically pre selected by Fuzzy C-means clustering algorithm. The learning technique employed is self supervised allowing, therefore, automatic adaptation of the neural network. This system does not require a priori assumptions whatsoever are made
B. R. Vikram; M. A. Bhanu; S. C. Venkateswarlu; M. R. Babu
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 fuzzycontrol system (400) operably connected to the chemical loop. The hierarchical fuzzycontrol system (400) includes a plurality of fuzzycontrollers (330). The hierarchical fuzzycontrol 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.
This paper presents the application of a fuzzy logic controlled particle swarm optimization (FLCPSO) to reactive power and voltage control (Volt\\/VAR control or VVC) considering voltage stability. An improved particle swarm optimization with three fuzzycontrollers based on some heuristics is proposed to adaptively adjust the parameters of PSO, such as the inertia weight and learning factors, during the optimization
A nonlinear speed controller for doubly-fed induction motor (DFIM) based on backstepping control and fuzzy sliding mode approach was designed to counter the effects of the coupling between the electromagnetic torque and the flux. The proposed strategy using fuzzy switching control could enhance the robustness adaptively compare with conventional sliding mode control. A simulation model for the doubly-fed induction motor
As the brush less DC motor is more and more widely used, relating control algorithm need more and more precise and intelligence. In this paper, after studying the fuzzycontrol and the neural network theory, a single neuron fuzzy self-adaptive PID control algorithm is presented for speed control of the brush less DC motor. The Matlab software simulation results show
Weibing Chen; Guanghua Zeng; Haojie Zou; Hongbo Zhang; Chuanwu Tan
This paper proposes a trajectory tracking scheme which belongs to the sliding mode control (SMC) for the 4-degree-of-freedom\\u000a (DOF) parallel robots. Two fuzzy logic systems (FLS) are first put forward to replace the constant switching control gain\\u000a and the width of the boundary layer. The fuzzyadaptive supervisory controller (FASC) is combined with the fuzzy sliding mode\\u000a control (FSMC) to
Many fuzzycontrol schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy
Han-xiong Li; Lei Zhang; Kai-yuan Cai; Guanrong Chen
Lateral vehicle control is a nonlinear control problem. Because there exists many uncertain factors (e.g., imprecision of vehicle modeling and information measurement), it is limited to take advantage of the traditional control method based on a precise mathematical model to design a controller for lateral vehicle control. During the past several years, fuzzycontrol has emerged as one of the most active areas for research in dealing with the nonlinear control problem and has made many achievements. We designed a fuzzy logic controller for the road following of the autonomous land vehicle. The main sensor we used is the video camera that can provide the environment information around the vehicle. We have implemented this controller in the simulation. The controller drove the vehicle to follow the curved road and we got good tracking accuracy. In comparing with the PID controller, the fuzzy logic controller does not need a precise vehicle model and has better adaptability to parameter variation of the vehicle than the PID.
This paper presents a novel adaptivefuzzy logic controller (FLC) equipped with an adaptive algorithm to achieve H(?) synchronization performance for uncertain fractional order chaotic systems. In order to handle the high level of uncertainties and noisy training data, a desired synchronization error can be attenuated to a prescribed level by incorporating fuzzycontrol design and H(?) tracking approach. Based on a Lyapunov stability criterion, not only the performance of the proposed method is satisfying with an acceptable synchronization error level, but also a rather simple stability analysis is performed. The simulation results signify the effectiveness of the proposed control scheme. PMID:21741648
The paper describes the design of the fuzzycontrol system for an autonomous robot car which operates in unknown, unpredictable, and dynamic environment. The fuzzycontrol 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
This paper presents hybrid control strategy for robust trajectory tracking control for a class of uncertain nonlinear mechanical systems. The design combines adaptivefuzzy system with robust adaptivecontrol algorithm. Adaptivefuzzy system approximates unknown nonlinear system dynamics while a robustifying adaptivecontrol term is used to cope with uncertainties due to the presence of external disturbance, modeling error and
Fluidized bed dryers are utilized in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzycontrol inherently compensates for process nonlinearities and exhibits more robust behavior. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzycontroller exhibits a remarkable dynamic behavior, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzycontroller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.
Taprantzis, A.V.; Siettos, C.I.; Bafas, G.V. [National Technical Univ., Athens (Greece). Dept. of Chemical Engineering
The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzycontrol, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzycontroller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzycontrol systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzycontrollers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented.
An adaptivefuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A
In this paper we describe a neuro-fuzzy system with adaptive capability to extract fuzzy If Then rules from input and output sample data through learning. The proposed system, called radial basis function (RBF) based adaptivefuzzy system (AFS), employs the Gaussian functions to represent the membership functions of the premise part of fuzzy rules. Three architectural deviations of the RBF
A discrete-time fuzzycontrol system which is composed of a dynamic fuzzy model and a fuzzy-state feedback controller is proposed. Stability of the fuzzycontrol system is discussed and two sufficient conditions to guarantee the stability of the system are given in terms of uncertain linear system theory. An algorithm is developed to check the stability condition. The controller design
\\u000a The so-called Educational Software for the Introduction to FuzzyControl (SDICD in Spanish acronym) has been developed in\\u000a the University of Málaga, Spain, with the purpose of extending fuzzy logic and fuzzycontrol to any interested person. This\\u000a software is more than an electronic book. It includes a good set of chapters, animated graphics, some interactive examples,\\u000a and auto-evaluation tests,
The purpose of this paper is to propose adaptivefuzzy sliding mode control (SMC) based on radial basis function neural network (RBFNN) for trajectory tracking problem of three link robot manipulator. A RBFNN is used to compute the equivalent control of sliding mode control. A Lyapunov function is selected for the design of the SMC and an adaptive algorithm is
This paper is devoted to the development of a fast industrial design of an embedded fuzzycontroller for an unstable plant starting from an initial fuzzy system developed in conjunction with an expert. Through prototyping and implementation on a general-purpose microprocessor the plant can then be tested and used to iteratively identify and control the plant on the desired control
Since the pioneering work of Zadeh and Mamdani and Assilian, fuzzy logic control has emerged as one of the most active and fruitful research areas. The applications of fuzzy logic control can be found in many fields such as control of stream generators, a...
This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology,
The authors discuss robust stability for fuzzy systems with premise parameter uncertainty and the design problem for robust fuzzycontrollers. They derive four conditions for ensuring stability of fuzzycontrol systems by weakening a stability condition proposed by K. Tanaka and M. Sugeno (1990). The concept of stability margin is introduced and a design problem of robust controllers for fuzzy
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 fuzzycontrol schemes. Moreover, a measure of robustness is proposed that can be used to evaluate and possibly redesign a
Conventional PID is difficult to satisfy the high principles requirement on an Air Compressor as controlled object because of its delay and nonlinear features. The FIR model of Air Compressor System was built based on fuzzy predictive control theory, which selected the outlet pressure as a fuzzy premise variable and tuned by the operating rules. The result of step disturbance
Keping Liu; Xuesheng Feng; Min Yang; Chonghe Tang; Changhong Jiang
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 fuzzycontroller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme
Niels J. Schouten; Mutasim A. Salman; Naim A. Kheir
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 Micro WEDM pulse generator is designed in this paper. It can adapt to the needs of various processing conditions. In additional, fuzzycontrol theory was applied to the pulse generator, which can automatically adjust the processing parameters to improve machining accuracy. So a more powerful Micro WEDM, which can achieve better processing results, is achieved.
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input–output fuzzy membership functions can be optimally tuned from training examples by a hybrid
A study of the different roles played by the fuzzy operators in fuzzycontrol is developed in this paper. The behavior of a very large amount of fuzzy operators is analyzed and a comparison of the accuracy of many fuzzy logic controllers designed by means of these operators is carried out. In order to do that, a comparison methodology is
We propose a design method for a global optimal fuzzycontroller to control and stabilize a continuous fuzzy system with free- or fixed-end point under finite or infinite horizon (time). A linear-like global system representation of continuous fuzzy system is first proposed by viewing a continuous fuzzy system in global concept and unifying the individual matrices into synthetical matrices. Based
We combined fuzzy pattern recognition and fuzzycontrol in the testing program of a fuzzy ANN. In pattern recognition for each input vector the network provides a spectrum of “object to a class belongingness” with respect to all classes. A fuzzycontroller using some of the components of the same input vector generates a numerical output by simulating an equation.
In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptivecontrol scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the
The main goal of this paper is to analyze the fuzzy mathematical programming (FMP) and to show how to apply it in an optimal problem. A general form of fuzzy numbers is proposed, and then by using this, the problem is rewritten with no fuzzy components by...
Traditional three-dimensional fuzzycontroller for over more fuzzy rules is difficultly designed and used, a mixed controller combined Fuzzycontrol with PID control is proposed in this paper. This strategy uses three one-dimensional fuzzycontrollers respectively being deviation, deviation change and deviation's deviation change by weighting fusion for forming the M-type controller. It solves the problem of multivariable fuzzycontrol
The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the
An efficient genetic reinforcement learning algorithm for designing fuzzycontrollers is proposed in this paper. The ge- netic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzycontroller design, comple- ments the local mapping property of a fuzzy rule. Using this Symbi- otic-Evolution-based FuzzyController (SEFC) design method, the number of control trials,
Synthesis of a control system using a fuzzy inference is proposed for switching regulators. A computer simulation result is given, as an example, where an output voltage of a Cuk converter is regulated by using the proposed fuzzy inference. The simulated waveforms of the output voltage at start-up and the responses to disturbances (variations of the load resistance and of
We describe in this paper a hybrid method for adaptive model-based control of non-linear dynamic systems using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro–fuzzy–fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic System Control. The new method for adaptive model-based control has been implemented as a
A decoupled fuzzy sliding-mode controller design is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear systems with only five fuzzycontrol rules. The ideas behind the controller are as follows. First, decouple the whole system into two second-order systems such that each subsystem has a separate control target expressed in
The nature of the multirate dynamics of a process makes it very attractive for applications, since the multirate phenomena are complex. This paper presents a research methodology for describing a two-rate stochastic control system as state-space (SS) type decomposed models of multi-input\\/multi-output (MIMO) stochastic control subsystems with \\
This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the Supervisor-FCM
This paper presents a fuzzy logic generator controller with emergency control loop. The controller consists of two blocks: a stability monitoring block, by which the stability of the generator is monitored in real time; and a control block, by which both the excitation control signal and the speed governing control signal are generated through a simple fuzzy logic and by
Takashi Hiyama; J. Hayashi; N. Suzuki; T. Funakoshi
This paper describes a multivariable controller developed at the Idaho National Engineering Laboratory (INEL) that incorporates both fuzzy logic rules and a neural network. The controller was implemented in a laboratory demonstration and was robust, producing smooth temperature and water level response curves with short time constants. In the future, intelligent control systems will be a necessity for optimal operation of autonomous reactor systems located on earth or in space. Even today, there is a need for control systems that adapt to the changing environment and process. Hybrid intelligent control systems promise to provide this adaptive capability. Fuzzy logic implements our imprecise, qualitative human reasoning. The values of system variables (controller inputs) and control variables (controller outputs) are described in linguistic terms and subdivided into fully overlapping value ranges. The fuzzy rule base describes how combinations of input parameter ranges determine the output control values. Neural networks implement our human learning. In this controller, neural networks were embedded in the software to explore their potential for adding adaptability.
A control system to improve the efficiency of machining a workpiece with varying thickness in the wire electrical discharge\\u000a machining (WEDM) process is proposed. The abnormal ratio R\\u000a \\u000a ab\\u000a defined by the proportion of abnormal sparks in a sampling period is taken as the controlled variable. It is allowed to reduce\\u000a temporarily as the cutting thickness is changing. A gain
In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing\\u000a approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary\\u000a knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then,\\u000a an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC)
This paper presents a backward movement control of an articulated vehicle via a model-based fuzzycontrol technique. A nonlinear dynamic model of the articulated vehicle is represented by a Takagi-Sugeno fuzzy model. The concept of parallel distributed compensation is employed to design a fuzzycontroller from the Takagi-Sugeno fuzzy model of the articulated vehicle. Stability of the designed fuzzycontrol
In this paper, we propose a simple and novel method for background modeling and foreground segmentation for visual surveillance applications. This method employs histogram based median method using HSV color space and a fuzzy k-means clustering. A histogram for each pixel among the training frames is constructed first, then the highest bin of the histogram is chosen and the median
This paper presents a novel interval type-2 fuzzy logic control architecture for flocking system when the system has noisy sensor measurements. The traditional type-1 fuzzy logic controller (FLC) using precise fuzzy sets cannot fully model and handle the uncertainties of sensor data. However, type-2 FLC using type-2 fuzzy sets with a footprint of uncertainty (FOU) produce better performances under noisy
This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the input resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptivefuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based
We have proposed to design and implement an image recognition system in software and hardware using features extracted from\\u000a the wavelet transform (WT) of the image as input to a pattern classifier. The wavelet transform will be computed via an adaptive\\u000a neural network while the pattern classification will be carried out by an adaptivefuzzy neural network. Thus, the system
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 fuzzycontrol 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 fuzzycontrol function is completely characterized and a numerical solution for the optimality system is computed.
Zarei, Hassan; Kamyad, Ali Vahidian; Heydari, Ali Akbar
Fuzzycontrollers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzycontrollers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzycontrollers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzycontroller are explored.
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
In this paper, we propose a new adaptive synchronization method, called a fuzzyadaptive delayed feedback synchronization (FADFS) method, for time-delayed chaotic systems with uncertain parameters. An FADFS controller that is based on the Lyapunov-Krasovskii theory, Takagi-Sugeno (T-S) fuzzy model, and delayed feedback control is developed to guarantee adaptive synchronization. The proposed controller can be obtained by solving the linear matrix inequality (LMI) problem. A numerical example using a time-delayed Lorenz system is discussed to assess the validity of the proposed FADFS method.
Power system stabilizers (PSS) must be capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbances. Traditional PSS rely on robust linear design methods. In an attempt to cover a wider range of operating conditions, expert or rule-based controllers have also been proposed. Recently, fuzzy logic as a novel robust control design method has shown promising results. The emphasis in fuzzycontrol design centers around uncertainties in system parameters and operating conditions. Such an emphasis is of particular relevance as the difficulty of accurately modelling the connected generation is expected to increase under power industry deregulation. Fuzzy logic controllers are based on empirical control rules. In this paper, a systematic approach to fuzzy logic control design is proposed. Implementation for a specific machine requires specification of performance criteria. This performance criteria translates into three controller parameters which can be calculated off-line or computed in real-time in response to system changes. The robustness of the controller is emphasized. Small signal and transient analysis methods are discussed. This work is directed at developing robust stabilizer design and analysis methods appropriate when fuzzy logic is applied.
Hoang, P.; Tomsovic, K. [Washington State Univ., Pullman, WA (United States). School of Electrical Engineering and Computer Science
A new approach about design of controller (GL) for integrated self-adaptive genetic annealing algorithm (SGAA) and sliding mode control (SGAA-SMC) was proposed in this study. The main objective of this paper is to improve the non-minimum phase system performance by continuously updating the parameters of the sliding surface by SGAA. Firstly, Based on traditional fuzzy logic sliding mode control (FLSMC),
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, fuzzycontrol rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzycontrollers 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 fuzzycontrol law performs well compared to the exact controller. In fact, the fuzzycontroller demonstrates improved robustness to noise.
In this paper, an EMG-based fuzzy-neuro control method is proposed for a three degree of freedom (3 DOF) human forearm and wrist motion assist exoskeleton robot (W-EXOS). The W-EXOS assists human forearm pronation\\/supination motion, wrist flexion\\/extension motion and ulnar\\/radial deviation. The paper presents the EMG-based fuzzy-neuro control method with multiple fuzzy-neuro controllers and the adaptation method of controllers. The skin
Thw paper presents the design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for asynchronous transfer mode (ATM) networks. The fuzzy traflic controller is a fuzzy implementation of the two-threshold congestion control method and the equivalent capacity admission control method extensively studied in the literature. It is an improved, intelligent implementation that not only
In this paper, the fuzzycontroller is designed for static synchronous compensator (STATCOM) to enhance interconnected power system stability. The power frequency model for STATCOM with conventional controllers is presented first. Fuzzycontrollers are then designed for both main and supplementary controllers of the STATCOM. The fuzzy main control is constant voltage control with voltage regulation which aims at providing
This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptivecontrol for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptivecontrol is used for linearized dynamics to minimize tracking error. The
Cheng-Hung Chen; D. Subbaram Naidu; Alba Perez-Gracia; Marco P. Schoen
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 a...
This paper deals with the problem of active fault-tolerant control (FTC) for time-delay Takagi-Sugeno (T-S) fuzzy systems\\u000a based on a fuzzyadaptive fault diagnosis observer (AFDO). A novel fuzzy fast adaptive fault estimation (FAFE) algorithm for\\u000a T-S fuzzy models is proposed to enhance the performance of fault estimation, and sufficient conditions for the existence of\\u000a the fault estimator are given
Fuzzy logic controllers are rapidly becoming a viable alternative for classical controllers. The reason for this is that a fuzzycontroller can imitate human control processes closely. Fuzzy logic technology enables the use of engineering experience and experimental results in designing an embedded system. In many applications, this circumvents the use of rigorous mathematical modeling to derive a control solution.
In this paper, a parallel structure of fuzzy PID control systems is proposed. It is associated with a new tuning method which, based on gain margin and phase margin specifications, determines the parameters of the fuzzy PID controller. In comparison with conventional PID controllers, the proposed fuzzy PID controller shows higher control gains when system states are away from equilibrium
A kind of electricity saving fuzzycontroller (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
Song Jiancheng; Li Haiying; Hao Junfang; Zhai Shengqin; Xie Hengkun
A fuzzy logic attitude controller has been developed for Cassini spacecraft. Feedback control issues such as tracking capability, thruster on\\/off time and cycle have been investigated and compared with conventional bang\\/bang control. A discrete nonlinear simulation was set up to assess the system performance with different controllers
Concerns the design of a DSS to improve the efficiency of fuzzy supervision of SPC. SPC is widely used in industry to control manufacturing processes, and therefore to control product quality. Using control charts, two statistical parameters (mean and standard deviation) are regularly checked to maintain the process under control and to improve its performance. However, the theoretical and practical
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
George Vachtsevanos; Shehu S. Farinwata; Dimitrios K. Pirovolou
Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.
A mixed analog-digital fuzzy logic inference processor chip, designed in a 0.35-m CMOS technology, is presented. The analog fuzzy engine is based on a novel current-mode CMOS circuit used for the implementation of fuzzy partition membership functions. The architecture consists of a 3 inputs—1 output analog fuzzy engine, internal digital registers to store the parameters of the fuzzycontroller, and
A predictive fuzzycontrol including control rules to achieve desirable conditions has been proposed and applied to an automatic train operation (ATO) system. The capability of the fuzzy ATO to control train operation as skilfully as experienced operators was confirmed by a trial run made in the Sendai municipal subway system. The control rules of the fuzzy ATO and results
In this paper, a fuzzy logic controller is proposed for trajectory tracking of underwater-manipulator systems (UVMS). The controller is designed based on VD control method with gain tuning based on the fuzzy logic heuristics. The proposed fuzzycontroller has the advantages that it does not require a model of the vehicle\\/manipulator system dynamics, and ensures robust performance in the presence
Bin Xu; Norimitsu Sakagami; S. R. Pandian; F. Petry
There are two main drawbacks in fuzzycontrol: 1) the design of fuzzycontrollers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters; and 2) the fuzzycontroller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. In this paper we
Vivek G. Moudgal; Waihon Andrew Kwong; Kevin M. Passino; Stephen Yurkovich
This paper develops a method to tune fuzzycontrollers 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 de...
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-fuzzycontrol 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
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called AdaptiveFuzzy-K-means (AFKM) clustering for image segmentation which could be applied on general images and\\/or specific
We present a mixed-mode VLSI chip performing unsupervised clustering and classification, implementing models of FuzzyAdaptive Resonance Theory (ART) and Learning Vector Quantization (LVQ), and extending to variants such as Kohonen Self-Organizing Maps (SOM). The parallel processor classifies analog vectorial data into a digital code in a single clock, and implements on-line learning of the analog templates, stored locally and
This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using
Mohammad Fazle Azeem; Madasu Hanmandlu; Nesar Ahmad
This work addresses a fuzzy sliding-mode controller, which is mainly composed of the sliding mode controller and the fuzzy inference mechanism, for a mini unmanned air vehicle (UAV) with propellers to follow the predetermined trajectory. In this paper, a sliding-mode controller with a sliding surface is designed. And a fuzzy sliding-mode controller is proposed, such that a simple fuzzy inference
A high-accuracy and high-resolution fuzzycontroller 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 fuzzycontrol theory with the optimal control theory. The fuzzycontrol rules of a double-inverted pendulum are given and a powerful fuzzy decision way
Gunshots produce bruise patterns on persons who wear soft body armor when shot even though the armor stops the bullets. An adaptivefuzzy system modeled these bruise patterns based on the depth and width of the deformed armor given a projectile's mass and momentum. The fuzzy system used rules with sinc-shaped if-part fuzzy sets and was robust against random rule
In this paper we propose a fuzzy optimization of the bang-bang control using a fuzzycontroller in the feedback loop. The nonlinear characteristic of the fuzzycontroller is designed to minimize the output voltage ripple of the buck switching voltage regulator. Feedback signal is output voltage error gain by a value which is nonlinear dependent by output voltage ripple. Comparing
The paper deals with vibration suppression control of flexible structures applying a fuzzycontroller. Due to the complexity of the mathematical model related to the flexible structures, due to the restriction imposed by the actuator (force stroke limitation) as well as economical aspects, a fuzzy logic based approach of the control system is proposed. The experimental results using a fuzzy
A robust satellite tracking antenna is designed to cope with the sensor imprecision and the highly noisy sea environment. Fuzzy logic is utilized for the controller imprecision and the highly noisy sea environment. Fuzzy logic is utilized for the controller design as well as inaccurate data interpretation. The fuzzy-rule based controller eliminates the need to model the nonlinear and noisy
The paper deals with the fuzzy logic control of a switched reluctance motor (SRM) drive. The fundamentals of the fuzzy logic are first illustrated, pointing out the aspects related to the control under consideration. A fuzzy logic controller (FLC) of the motor speed is then designed and simulated. The results show that the use of an FLC in the speed
The design of fuzzy logic controllers encounters difficulties in the selection of optimized membership functions and a fuzzy rule base, which is traditionally achieved by a tedious trial-and error process. This paper develops genetic algorithms for the automatic design of high-performance fuzzy logic controllers using sophisticated membership functions that intrinsically reflect the nonlinearities encountered in many engineering control applications. The
A general connectionist model, called neural fuzzycontrol network (NFCN), is proposed for the realization of a fuzzy logic control system. The proposed NFCN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The NFCN can be constructed from supervised training examples
This paper analyzes the number of operations and parameters of general fuzzy logic control algorithms. And limitations of loop controllers to implement the fuzzy logic control are investigated in terms of computation time and required memory. Using analysis of general fuzzy logic control algorithms, it is shown that general fuzzy logic control algorithms are not suitable for loop controllers. A
This paper proposes an evolutionary Q-learning algorithm for the design of a fuzzy logic controller. By defining Q-values as a functional value of state and each fuzzy logic controller, Q-learning is easily applied to the group of fuzzy logic controllers. An evolutionary algorithm which uses Q-values for the evaluation of the fitness value is proposed to extract the best fuzzy
The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzycontrollers, called a Pittsburgh Fuzzy
Fuzzy set theory was used to formulate a control system for beam rider guidance of a vehicle launched from a moving platform against an evasive contact. Control of the vehicle entails use of a time-varying data input stream from the launching platform sen...
One of the ways to overcome traffic problems in large cities is through the development of an intelligent monitoring and control of traffic lights system. This paper addresses the design and implementation of a n intelligent traffic lights controller based on fuzzy logic technology. A software has been developed to simulate the situation of an isolated traffic junction based on
A fuzzy logic controller (FLC) is designed and implemented in real time on a Toyota Celica test vehicle to achieve control of the lateral motion of the vehicle. The structure of FLC is modularized as feedback, preview, and gain scheduling rule bases. The parameters of FLC are tuned manually using information from the characteristics of human driving operation and existing
An intelligent controller, which consists of an intelligent planner and an adaptivefuzzy 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
\\u000a In this paper a method is proposed for performance evaluation of road traffic control strategies. The method is designed to\\u000a be implemented in an on-line simulation environment, which enables optimisation of adaptive traffic control. Performance measures\\u000a are computed using a fuzzy cellular traffic model, formulated as a hybrid system combining cellular automata and fuzzy calculus.\\u000a Experimental results show that the
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 of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity. PMID:17926711
This paper describes a control strategy for the control of an acrobot. The strategy combines a model-free fuzzycontrol, a fuzzy sliding-mode control and a model-based fuzzycontrol. The model-free fuzzycontroller designed for the upswing ensures that the energy of the acrobot increases with each swing. Then the fuzzy sliding-mode controller is employed to control the movement that the
A nonlinear fuzzy backstepping controller is presented for set-point regulation of a pneumatic muscle actuator. Inflation and deflation are the two possible modes of operation of this actuator. Three fuzzy sets, one for each of both modes and one for the transition region, are considered. Each fuzzy set is associated with a local backstepping controller. At the inflation and deflation
This paper proposes a useful algorithm approach to hybrid control systems combining fuzzy logic and predictable control techniques for network infrastructure. The given model employs two rules to Fuzzy logic rate prevention based on queue length and buffer frequency. The learning rule for this network must then be modified accordingly. The implementation of the fuzzy model was carried out in
In this paper, intelligent Fuzzycontrol system has been applied for biogas processes in a hydrophobic permeable polymer that is proposed to be used in landfills as a medium for biogas collection. Once having experimental information about biogas transport in the polymer within different variables, the information could be modeled by fuzzy system. When fuzzycontroller is built with its
This paper presents controller design conditions based on a switching Lyapunov function in terms of linear matrix inequalities (LMIs) for switching fuzzy systems. In our previous papers, we proposed switching fuzzy model construction and derived switching fuzzycontroller design conditions. However, the conditions have been given in terms of bilinear matrix inequalities (BMIs), so it is difficult to design the
A method for solving an inventory control problem, of which input data are described by triangular fuzzy numbers will be presented here. The continuous review model of the inventory control problem with fuzzy input data will be focused in, and a new solution method will be presented. For the reason that the result should be a fuzzy number because of
This paper proposes using fuzzy logic techniques to dynamically control parameter settings of ge- netic algorithms (GAs). We describe the Dy- namic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parame- ters. We then introduce a technique for automati- cally designing and tuning the fuzzy knowledge- base system using GAs. Results from initial experiments show
This paper presents a comparative study on fuzzy rule-base of fuzzy logic speed control with vector-controlled permanent magnet synchronous motor (PMSM) drive. Fuzzy rule-base design is viewed as control strategy. All fuzzy rules contribute to some degree in obtaining the desired performance. However, some rules fired weakly do not contribute significantly to the final result and can be eliminated. dasiaStandard
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.
Human motion simulation is an ill-posed problem. In order to predict unique lifting motion trajectories, a motion simulation model based on fuzzy-logic control is presented. The human body was represented by a 2-D five-segment model, and the neural controller was specified by fuzzy logic. Fuzzy rules were defined with their antecedent part describing the fuzzy variables of scaled positional error
|This paper presents a novel scheme called "Laboratory Testbed for Embedded FuzzyControl 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'…
Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.
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...
Viewgraphs on fuzzycontrol/space station automation are presented. Topics covered include: Space Station Freedom (SSF); SSF evolution; factors pointing to automation & robotics (A&R); astronaut office inputs concerning A? flight system automation and ground operations applications; transition definition program; and advanced automation software tools.
In a complicated expert reasoning system, it is inefficient for commonly fuzzy production rules to depict the vague and modified knowledge. Fuzzy Petri nets are more accurate for dynamic knowledge proposition in describing expert knowledge. However, the bad learning ability of fuzzy Petri net constrains its application in dynamic knowledge expert system. In this paper, an advanced self-adaptation learning way based on error back-propagation is proposed to train parameters of fuzzy production rules in fuzzy Petri net. In order to enhance reasoning and learning efficiency, fuzzy Petri net is transformed into hierarchy model and continuous functions are built to approximate transition firing and fuzzy reasoning. Simulation results show that the designed advanced learning way can make rule parameters arrive at optimization rapidly. These techniques used in this paper are quite effective and can be applied to most practical Petri net models and fuzzy expert systems.
In this paper we propose a novel CPU Scheduler based on Adaptive Neuro Fuzzy Inference System (ANFIS), to support the execution of multimedia applications along with conventional applications in multimedia operating system. Adaptive Neuro-Fuzzy Inference System (ANFIS) can be used to solve highly non-linear dynamic problems. This paper shows how an ANFIS can be used to optimize CPU scheduling in
Fuzzycontrollers have been proven to be uni- versal, that is, they can provide any control sur- face (1). Their microelectronic implementation is very suitable to achieve high-speed (real-time operation), and low area and power consump- tion. This paper focuses on discussing the two basic approaches that can be employed to design programmable universal controller inte- grated circuits. Analog, mixed-signal
This paper presents the design of a digital CMOS integrated circuit implementing a type-2 fuzzy logic controller. The proposed architecture is suitable for serial processing of fuzzy rules combined with parallel processing of upper and lower membership functions of type-2 fuzzy sets. The parameterized VHDL model allows to synthesize the circuit of the required size for a particular application. Moreover,
The architecture and required building blocks of CMOS fuzzy chips capable of performing as adaptivefuzzycontrollers are described in this paper. The building blocks are designed with mixed-signal current-mode cells that contain low-resolution A\\/D and D\\/A converters based on current mirrors. These cells provide the chip with analog or digital programmability and long-term local storage. They also perform as
I. Baturone; S. Sanchez-Solano; A. Barriga; J. L. Huertas
A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called a B-spline membership function (BMF). By using the local control property
In general, due to the interactions among subsystems, it is difficult to design an H infin-decentralized output-feedback controller for nonlinear interconnected systems. This study introduces H infin-decentralized fuzzy-observer-based fuzzycontrol design, where the premise variables depend on the state variables estimated by a fuzzy observer, for nonlinear interconnected systems via T-S fuzzy models. The fuzzycontrol design for this case
This paper presents the use of fuzzy logic control (FLC) of a variable speed induction machine wind generation system. The generation system uses three fuzzy logic controllers (FLC's), first fuzzy logic controller tracks the generator speed with wind velocity to extract maximum power. Second fuzzy logic controller programs the machine flux for light load efficiency improvement. Third fuzzy logic controller
The problem of crane control system is addressed in the paper to gain scheduling system created using the fuzzycontroller with Takagi-Sugeno-Kang (TSK) fuzzy implications. The method of fuzzy robust crane control system designing was based on closed-loop crane control systems with conventional proportional-derivative (PD) controllers of crane position and the load swing derived for fixed rope length and mass
To solve the problems existed in vehicle starting with wet dual clutch transmission, the starting strategy was developed on the basis of clutch evaluation and dynamics analysis about transmission. The wet clutch target pressure of starting phases is determined by fuzzycontrol method. Parameter adaptive PID controller was used to realize clutch pressure's accurate control. In order to verify the
Wang Yinshu; Cheng Xiusheng; Li Xuesong; Xiang Jitao
This paper presents a unified approach to controlling chaos via a fuzzycontrol system design based on linear matrix inequalities (LMI's). First, Takagi-Sugeno fuzzy models and some stability results are recalled. To design fuzzycontrollers, chaotic systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel distributed compensation is employed to determine structures of fuzzycontrollers from the Takagi-Sugeno
This paper proposes an evolving fuzzy associative memory neural network model based on the fuzzy CMAC (FCMAC). FCMAC is an auto-associate memory feed forward neural network with attractive properties of fast learning and simple computation. Evolving techniques aim at building adaptive intelligent systems that evolve both their structure and parameters through incremental online learning. During fuzzification phase, the proposed ESOFCMAC
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.
In accordance with the higher and higher tension control accuracy, using fuzzycontroller in tension control system is proposed. The tension control model is deduced based on an aluminum cold rolling mill, and the fuzzycontroller for tension control system is designed. The analysis in dynamic performance between traditional PID controller and fuzzy self-tuning PID controller is done by MATLAB
This paper describes the design of a fuzzy logic controller using voltage output as feedback for significantly improving the dynamic performance of boost dc-dc converter by using MATLAB@Simulink software. The objective of this proposed methodology is to develop fuzzy logic controller on control boost dc-dc converter using MATLAB@Simulink software. The fuzzy logic controller has been implemented to the system by
N. F Nik Ismail; I. Musirin; R. Baharom; D. Johari
This paper presents a learning fuzzycontroller which can adapt with changing performance requirements. During the past decade we have witnessed a rapid growth in the number and variety of applications of fuzzy logic ranging from consumer electronics and industrial process control to decision support system and financial systems. The fuzzycontroller designer faces the challenge of choosing the appropriate
Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzycontrol in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzycontroller demonstrated clinically acceptable performance, suggesting that further study is warranted. PMID:19963562
Moore, Brett L; Pyeatt, Larry D; Doufas, Anthony G
In order to satisfy the higher control performance requirement of the induction heating supply, a fuzzy logic control technology for induction heating power supply power control system is researched. This study presents the composition and design of the induction heating control system based on the fuzzy logic controller. In this paper, a complete simulation model of induction heating systems is
A design method for a fuzzycontroller based on emulating an existing controller is presented. In this method, the human operator is not required to provide a set of explicit linguistic fuzzycontrol rules. Training information can be obtained by observing the input-output behavior of the human operator, where the target controller output for a given input is the control
This paper presents two examples for the deployment of fuzzy signatures in the field of intelligent mobile robots. The first\\u000a shows a complex lateral drift control method base on fuzzy signatures. This method considers the motion system of the robot\\u000a as a whole, unlike as simple parts of a complex system. The state space is written down by fuzzy signatures
This paper presents an algorithm to identify T-S fuzzy models and design fuzzy model based controllers (FMBC) for a class of nonlinear plant. First, the algorithm using fuzzy c-regression models (FCRM) clustering to find the functional relationships in the product space of the input-output data. A new cluster validity criterion is proposed to calculate overall compactness and separateness of the
This paper proposes an INTEL 8051 microcontroller based fuzzycontroller for closed loop control of dc drive fed by dc\\/dc converter. The controller designed has two loops with an inner current controller and an outer fuzzy speed controller. Computer simulations evaluate (tested) the designed fuzzycontroller. The controller is used to change the duty cycle of the converter and thereby,
The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzycontroller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzycontroller. For a simple demonstration of the easy design and control capability of a fuzzycontroller, a proportional-derivative (PD) fuzzycontrol system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzycontrol systems by considering the key features of a fuzzycontrol system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.
This paper describes stable switching fuzzycontrol and its application to a hovercraft type vehicle (HTV). A switching fuzzy system is employed to represent the nonlinear dynamics of the HTV that is a typical nonholonomic system. A switching fuzzycontroller is constructed by mirroring the structure of the switching fuzzy system. LMI stability conditions to design the switching fuzzycontroller
This paper presents an intelligent adaptive neurofuzzy inference system (ANFIS) based fuzzy Mamdani controller for a multifingered prosthetic hand. The objective of the controller is to move the finger joint angles along predetermined paths representing a grasping motion. The initiation of the grasping task is evaluated via EMG-entropy data, measured at the forearm of the prosthetic user. In addition to
Chandrasekhar Potluri; Parmod Kumar; Madhavi Anugolu; Steve Chiu; Alex Urfer; Marco P. Schoen; D. Subbaram Naidu
A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the challenges of the modern advanced process industry. PMID:19775972
To make the living space more convenient for humans, how to facilitate network technology to achieve remote control becomes an important issue. In this work, the congestion control for networks has been investigated on Linux platform. We suggest new methods for TCP congestion control based on T-S fuzzy model and typical fuzzy logic system. We realize the resulting congestion control
We have studied how to generate a fuzzy logic controller whose output is identical to that of a given PI controller. Based on this study, we have analyzed what makes the fuzzy logic controller perform better than the PI controller. We have also designed a...
B. S. Moon B. S. Lee K. S. Han J. S. Moon S. B. Hong
This paper reports on the application of Fuzzy Reference Gain-Scheduling Control (FRGS) to control a thermal-vacuum unit that emulates space environmental conditions for satellite and space device qualification. FRGS is a variation of fuzzycontrol that changes the controller gain surface in accordance to distinct operational conditions established by the reference (goal). This system allows to incorporate the experience of
In order to meet the requirements of driving safety and energy-saving of lighting in tunnel, a fuzzycontrol method is adopted to design the tunnel lighting control system. A fuzzycontrol model for the tunnel lighting control system is established with tunnel exterior environment luminance, traffic volume and vehicle speed information as inputs and tunnel interior light luminance as output.
The paper gives an introduction of knowledge modelling techniques i.e. fuzzy models suitable for diesel engine diagnosis and control. Two examples are illustrated for engine faulty condition diagnosis and two simulated examples are given for fuzzycontrol of diesel engine process: 1. diesel oil viscosity control (Mamdani model used) and 2. shaft speed control (T-S model used). É incomplete and
This paper proposes an adaptive chaos quantum honey bee algorithm (CQHBA) for solving chance-constrained programming in random\\u000a fuzzy environment based on random fuzzy simulations. Random fuzzy simulation is designed to estimate the chance of a random\\u000a fuzzy event and the optimistic value to a random fuzzy variable. In CQHBA, each bee carries a group of quantum bits representing\\u000a a solution.
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
This paper proposes the application of Neuro-Fuzzy (NF) hybrid system for Sumo Robot (SR) control. This robot is frequently designed by engineering students for robotic competition. As the relation between sensors output signals and motors control pulses is highly nonlinear in SR, soft computing techniques can be used to define this nonlinear relation and control of the robot in a
The design of advanced controllers for many industrial processes is heavily dependent on the availability of a model for the process. Construction of appropriate models is often not possible due to the complexity and nonlinearity of the process. Fuzzy logic can be used for different tasks within intelligent control systems because they represent general, nonlinear relationships that can be initialized
An energy-compensated fuzzy swing-up and balance control is investigated for the planetary-gear-type inverted pendulum (PIP) in this paper. The proposed control scheme consists of a fuzzy swing-up controller (FSC), a fuzzy sliding balance controller (FSBC), and a fuzzy compensation mechanism. The PIP with the designed FSC can upswing the pendulum quickly and have the controlled system be stable in the
Yeong-Hwa Chang; Chia-Wen Chang; Jin-Shiuh Taur; Chin-Wang Tao
A neural fuzzy system learning with linguistic teaching signals is proposed. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptivefuzzy expert system or as an adaptivefuzzycontroller. First, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The
Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consideration. This paper investigates adaptive
Nikola K. Kasabov; Jaesoo Kim; Michael J. Watts; Andrew R. Gray
Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach
A novel neural network with adaptivefuzzy logic rule is proposed for image restoration as required for quantitative imaging using a nuclear gamma camera. The overall aims is to compensate for image degradation due to photon scattering and photon penetration through the collimated gamma camera to allow more accurate measurement of radiotracers in vivo. In this work, fuzzy rules are
Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of
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.
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
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.
Proposes a systematic and theoretically sound way to design a global optimal discrete-time fuzzycontroller 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.
The paper presents a fuzzy modeling of a power plant generator that is defined by a well-known set of differential equations used for transient control. Its main motivations are (1) to demonstrate the validity of the identification method based on fuzzy inference rules and (2) because a power plant has strong non-linearity and many conventional, non-fuzzycontrol tends to linearize
Neutralization is a technique widely used as a part of wastewater treatment processes. Due to the importance of this technique, extensive study has been devoted to its control. However, industrial wastewater neutralization control is a procedure with a lot of problems--nonlinearity of the titration curve, variable buffering, changes in loading--and despite the efforts devoted to this subject, the problem has not been totally solved. in this paper, the authors present the development of a controller based in fuzzy logic (FLC). In order to study its effectiveness, it has been compared, by simulation, with other advanced controllers (using identification techniques and adaptivecontrol algorithms using reference models) when faced with various types of wastewater with different buffer capacity or when changes in the concentration of the acid present in the wastewater take place. Results obtained show that FLC could be considered as a powerful alternative for wastewater neutralization processes.
Garrido, R.; Adroer, M.; Poch, M. [Univ. de Girona (Spain)
In this paper, we present the design of a new type of fuzzycontrollers for controlling complex single-input–single-output systems by incorporating sliding mode control theory with fuzzycontrol technology. First, a fuzzy model of the given nonlinear system is constructed to represent the local dynamic behaviors of the given nonlinear system. A global controller is then constructed by combining all
Wook Chang; Jin Bae Park; Young Hoon Joo; Guanrong Chen
This article describes a new approach to re- ducing vertical vibrations in a 70-ton railcar using a neuro- fuzzycontroller and a magnetorheological (MR) damper. A semiactive control technique is developed for a two- degree-of-freedom quarter car model of the railcar that has an installed MR damper. A fuzzycontroller in real time continuously updates damping properties of the de-
In this paper we propose an approach to improve the direct torque control (DTC) of an induction motor (IM). The proposed DTC is based on fuzzy logic technique switching table, is described compared with conventional direct torque control (DTC). To test the fuzzycontrol strategy a simulation platform using MATLAB\\/SIMULINK was built which includes induction motor d-q model, inverter model,
This paper presents the application of fuzzy logic active control of surge in constant speed centrifugal compressors based on the Moore-Greitzer (MG) model. A compression system equipped with a close-coupled valve (CCV) and a throttle control valve (TCV) is investigated. Two fuzzycontrollers are developed, one for each valve. The combination of the two valves proves helpful in suppressing surge
Raef S. Shehata; Hussein A. Abdullah; Fayez F. G. Areed
The paper suggests a fuzzycontrol solution for a class of servo systems. Simplified mathematical models of second-order integral type characterize the controlled plants. The design is done using the linear case results on the basis of the extended symmetrical optimum (ESO) method and Iterative Learning Control and by the transfer of these results to the fuzzy case. The sensitivity
This paper addresses the problem of regulating wind energy conversion system by using fuzzy output feedback controller. First, a Takagi-Sugeno fuzzy model is employed to represent the nonlinear dynamics of the wind energy conversion system. Then, based on the fuzzy model and utilizing the concept of parallel distributed compensation, a fuzzy observer based fuzzycontroller is developed to stabilize the
Fuzzy logic--an artificial intelligence technique--can be employed to exploit the wealth of information human experts have learned about complex systems while attempting to control them. This information is usually of a qualitative nature that is unusable by rigid conventional control techniques. Fuzzy logic, uses as a control method, manipulates linguistically expressed, heuristic knowledge from a human expert to derive control actions for a described system. As an alternative approach to classical controls, fuzzy logic is examined for start-up control and normal regulation of a bubbling fluidized bed combustor. To validate the fuzzy logic approach, the fuzzycontroller is compared to a classical proportional and integral (PI) controller, commonly used in industrial applications, designed by Ziegler-Nichols tuning.
Koffman, S.J. [Purdue Univ., West Lafayette, IN (United States). School of Mechanical Engineering; Brown, R.C. [Iowa State Univ., Ames, IA (United States). Dept. of Mechanical Engineering; Fullmer, R.R. [Utah State Univ., Logan, UT (United States). Dept. of Mechanical and Aerospace Engineering
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 fuzzycontrol 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
The increasing diffusion of distributed generation plants in recent years highlights problems concerning voltage regulation in medium voltage (MV) radial distribution networks. Among various possible control techniques able to regulate voltage profiles, intelligent systems based ones seems to be very promising. In particular, fuzzycontrol techniques are very interesting for a wide range of applicative fields like power distribution systems
Vito Calderaro; Vincenzo Galdi; Antonio Piccolo; Giovanni Massa
Fuzzycontroller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a
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.
A heuristic controller is presented that takes the form of a set of fuzzy linguistic rules. Simulations show that the fuzzy logic controller (FLC) yields better results than the conventional PD controller. A self-paced fuzzy tracking controller (SPFTC) designed for two-dimensional path tracking is also presented. The SPFTC adjusts the tracking speed in accordance with contour conditions such as curvature;
Fuzzycontrollers are increasingly being accepted by engineers and scientists alike as a viable alternative for classical controllers. The processes involved in fuzzycontrollers closely imitate human control processes. Human responses to stimuli are not governed by transfer functions and neither are those from fuzzycontrollers. This study involves the design and application of fuzzycontrol to the problem of
Abul R. Hasan; Thomas S. Martis; A. H. M. S. Sr. Ula
Image segmentation remains one of the major challenges in image analysis. Many segmentation algorithms have been developed for various applications. Unsatisfactory results have been encountered in some cases, for many existing segmentation algorithms. In this paper, we introduce three modified versions of the conventional moving k-means clustering algorithm called the fuzzy moving k-means, adaptive moving k-means and adaptivefuzzy moving
This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set. A connectionist topology of fuzzy basis functions with their universal approximation capability is served as a fundamental SANFIS architecture that provides an
Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzycontrollers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzycontroller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzycontroller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.
This paper presents two methods of estimation of the rotor resistance in the indirect vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed using a PI controller and a fuzzy logic. The performance of both estimators and torque and flux responses of the drive are investigated with simulations for variations
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
The operating range of aerodynamic compressors is usually limited by a phenomenon known as surge. Active surge control has showed the ability to extend the operating range significantly. This study presents a solution to this problem based on the fuzzy logic approach. A simple fuzzycontroller is designed to suppress the surge instability on a given compressor model. Simulation studies
The purpose of this paper is to develop an expert system for control system design (ESCSD), with a unique set of fuzzy evaluation rules. The authors' investigation not only uses expert systems for control system design but also proposes a practical way to use a unique set of fuzzy evaluation rules to suggest a better design method for a given
The purpose of this paper is to present the devolvement of a fuzzycontrol applied for a hospitals and health care centres mobile concept robot, the i-MERC. The robot and fuzzycontroller models are presented as well as simulation results. The simulation application that was developed included the possibility to change de bending radius of the curves. To analyze the
Fernando Carreira; Tomé Canas; Joao Sousa; Carlos Cardeira
This paper describes an improved approach to design a Takagi-Sugeno zero-order type fast parameterized digital fuzzy logic controller (DFLC) processing only the active rules (rules that give a non- null contribution for a given input data set), at high frequency of operation, without significant increase in hardware complexity. To achieve this goal, an improved method of designing the fuzzycontroller
K. M. Deliparaschos; F. I. Nenedakis; Spyros G. Tzafestas
This work addresses the analysis and the design of an adaptivefuzzy sliding-mode observer applied to vector control of an induction motor. The conception steps for the conventional sliding-mode speed observer (SMO) are laid down clearly. In this work, the drawbacks of SMO in terms of high observer gains and chattering phenomenon due of switching surface are overcome by merging
K. Kouzi; M.-S. Nait-Said; M. Hilairet; E. Berthlot
The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzycontrol law to ensure a stable nonlinear system. This is done by using an intelligent fuzzycontroller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzycontrol. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzycontroller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.
Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzycontrol in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the
Brett L. Moore; Larry D. Pyeatt; Anthony G. Doufas
In this paper, a fuzzy logic controller design for optimal reactor temperature control is presented. Since fuzzy logic controllers rely on an expert's knowledge of the process, they are hard to optimize. An optimal controller is used in this paper as a reference model, and a Kalman filter is used to automatically determine the rules for the fuzzy logic controller. To demonstrate the robustness of this design, a nonlinear six-delayed-neutron-group plant is controlled using a fuzzy logic controller that utilizes estimated reactor temperatures from a one-delayed-neutron-group observer. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation.
Ramaswamy, P.; Edwards, R.M.; Lee, K.Y. (Pennsylvania State Univ., University Park (United States))
|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)|
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)
Abstract—The optimal control is one of the possible controllers for a dynamic system, having a linear quadratic regulator and using the Pontryagin’s principle or the dynamic,programming,method,. Stochastic disturbances may,affect the coefficients (multiplicative disturbances) or the equations (additive disturbances), provided that the shocks are not too great . Nevertheless, this approach encounters difficulties when,uncertainties are very important or when,the prob- ability
Fuzzy logic appears a promising approach to address many important aspects of networks, particularly the traffic control in ATM (Asynchronous Transfer Mode) network. In this paper we first investigate a fuzzy logic based model for traffic control in ATM. ATM traffic model and traffic control using fuzzycontrollers are first simulated using MatLab. Then, an application specific fuzzycontroller is
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
The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too. PMID:20945520
Robustness is a major consideration in any controls application. A controller that goes unstable with only a slight disturbance is useless especially in the harsh environment of an automobile. Therefore any scheme that increases the robustness without harming response time is very welcome. Adaptation is one such scheme and in our case we have two slowly varying values that lend
Since Lotfi Zadeh's introductory paper in 1965, the fuzzy set theory and the applications of fuzzy systems have come a long way. The initial hesitation, even the hostile reaction to fuzzy set theory has left its place to enthusiasm, or at least tolerance ...
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 fuzzycontrol scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)
Yu, Zhen; Dexter, Arthur [Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ (United Kingdom)
In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzycontrol (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor
Pawel Z. Grabowski; Marian P. Kazmierkowski; Bimal K. Bose; Frede Blaabjerg
In the radiation treatment of moving targets with external surrogates, information on tumor position in real time can be extracted by using accurate correlation models. A fuzzy environment is proposed here to correlate input surrogate data with tumor motion estimates in real time. In this study, two different data clustering approaches were analyzed due to their substantial effects on the fuzzy modeler performance. Moreover, a comparative investigation was performed on two fuzzy-based and one neuro-fuzzy-based inference systems with respect to state-of-the-art models. Finally, due to the intrinsic interpatient variability in fuzzy models' performance, a model selectivity algorithm was proposed to select an adaptivefuzzy modeler on a case-by-case basis. The performance of multiple and adaptivefuzzy logic models were retrospectively tested in 20 patients treated with CyberKnife real-time tumor tracking. Final results show that activating adequate model selection of our fuzzy-based modeler can significantly reduce tumor tracking errors. PMID:23318386
Esmaili Torshabi, Ahmad; Riboldi, Marco; Imani Fooladi, Abbas Ali; Modarres Mosalla, Seyed Mehdi; Baroni, Guido
We present a clustering technique for fuzzy rules based on Hilbert space-filling curves (SFC). SFC scans an n-dimensional space and reduces it to a curve, i.e. a one-dimensional line. We first introduce the Hilbert space-filling curves, and outline the algorithms for clustering and adaptive clustering which demonstrate SFC efficient self-organizing features. We then propose a SFC fuzzy inference model based
\\u000a Besides the classical control theories the method of fuzzycontrol gains increasing interest. A great number of publications\\u000a deals with this topic describing it as very efficient concept. In this paper the development of control systems based upon\\u000a fuzzycontrol theory for distillation columns with and without sidestreams is pointed out. The performance of this control\\u000a application is compared to
This paper describes a soft computing technique for modelling and controlling systems: fuzzy cognitive maps (FCM). The description, representation and models of FCM are examined in detail. A FCM model is proposed, its characteristics and advantages are presented, and a development algorithm is described. Fuzzy cognitive maps and similar soft computing techniques may contribute to the development of more sophisticated
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
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
Francisco R. Rubio; Manuel Berenguel; Eduardo F. Camacho
This paper introduces a new approach utilizing a fuzzy classifier and a modular temporal neural network to predict wind speed and direction for advanced wind turbine control systems. The fuzzy classifier estimates wind patterns and then assigns weights accordingly to each module of the temporal neural network. The finite-duration impulse response multiple-layer structure of the temporal network makes it possible
Purpose – This paper presents a VHDL-AMS based genetic optimisation methodology for fuzzy logic controllers (FLCs) used in complex automotive systems and modelled in mixed physical domains. A case study applying this novel method to an active suspension system has been investigated to obtain a new type of fuzzy logic membership function with irregular shapes optimised for best performance. Design\\/methodology\\/approach
Analog circuits are attractive for fuzzy VLSI, as they allow an easy implementation of the arithmetic functions which are needed in a fuzzycontroller. Analog circuits are efficient in areas allowing parallelism to be employed. However, they suffer from noise disturbances and interferences. Also, they are susceptible to process variations and mismatching. On the other hand, digital circuits are robust
Electro-chemical honing (ECH) is a hybrid electrolytic precision micro-finishing technology that, by combining physico-chemical actions of electro-chemical machining and conventional honing processes, provides the controlled functional surfaces-generation and fast material removal capabilities in a single operation. Process multi-performance optimization has become vital for utilizing full potential of manufacturing processes to meet the challenging requirements being placed on the surface quality, size, tolerances and production rate of engineering components in this globally competitive scenario. This paper presents an strategy that integrates the Taguchi matrix experimental design, analysis of variances and fuzzy inference system (FIS) to formulate a robust practical multi-performance optimization methodology for complex manufacturing processes like ECH, which involve several control variables. Two methodologies one using a genetic algorithm tuning of FIS (GA-tuned FIS) and another using an adaptive network based fuzzy inference system (ANFIS) have been evaluated for a multi-performance optimization case study of ECH. The actual experimental results confirm their potential for a wide range of machining conditions employed in ECH.
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 fuzzycontrol 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
Kumbasar, Tufan; Eksin, Ibrahim; Guzelkaya, Mujde; Yesil, Engin
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 metho...
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 ...
A controller for DC-DC converters based on fuzzy logic is proposed. Being free of complex equations and heavy computation, the controller is expected to control converters that operate at high frequencies. This paper presents the derivation of fuzzycontrol rules for the basic converter circuits and simulations of the performance of the closed-loop converters in respect of start-up transient, load
In this paper, a general-purpose fuzzycontroller for DC-DC converters is investigated. Based on a qualitative description of the system to be controlled, fuzzycontrollers are capable of good performances, even for those systems where linear control techniques fail, e.g., when a mathematical description is not available or is in the presence of wide parameter variations. The presented approach is
Paolo Mattavelli; Leopoldo Rossetto; Giorgio Spiazzi; Paolo Tenti
The authors acknowledge certain errors in their recently published paper titled "PI and fuzzy logic controllers for shunt active power filter--A report.The ambiguity in band width calculation of adaptive hysteresis controller and control aspects of dc-link voltage issues are addressed. The shunt APF system is validated through extensive simulation and the results are support features of the proposed technique. PMID:23012711
The constant turning force adaptivecontrol system design is a key link of machining automation. Because of the nonlinearities, time-varying parameters and uncertainties in turning processes, it is difficult to obtain satisfied performances for modeling based control methods. In this paper, the incremental neuron model-free control method with fuzzy self-tuning gain is proposed for cutting processes. In order to reach
The paper develops a combined genetic algorithm and fuzzy logic approach to path planning for a mobile robot operating in rough environments. Path planning consists of a description of the environment using a fuzzy logic framework, and a two-stage planner. A global planner determines the path that optimizes a combination of terrain roughness and path curvature. A local planner uses
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
The above paper gives a sufficient condition for the existence of a Takagi-Sugeno (T-S) fuzzy H (infinity) tracking controller for a class of nonlinear networked control systems. The aim of this paper is to show that if there exists a T-S fuzzy H (infinity) tracking controller, then there exists a linear H (infinity) tracking controller that guarantees the same prescribed H (infinity) tracking performance. PMID:19884086
Abstract This paper presents an estimator\\/subtractor narrow,band,interference (NBI) canceller based on fuzzy logic and applied to DS-CDMA systems. The designed fuzzy canceller introduces the slow varying nature of the NBI in its rule base. In this way, we obtain an interference suppressor with the following features: (i) fusion of different NBI models in its rule base; (ii) fast adaptation capability;
Joan Bas; Ana I. Pérez-neira; Miguel Angel Lagunas
\\u000a In this paper, we present a novel algorithm for adaptivefuzzy segmentation of MRI data and estimation of intensity inhomogeneities\\u000a using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated\\u000a with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with\\u000a conventional intensity-based
Mohamed N. Ahmed; Sameh M. Yamany; N. A. Mohamed; Aly A. Farag
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
An automatic design method for hierarchical fuzzycontrollers using genetic algorithms isproposed. A reorder operator for the genetic algorithm is introduced. We applied the methodto the problem of controlling an autonomous vehicle with the task to reach a given locationand avoiding obstacles on the way.Keywords: FuzzyControl, Genetic Algorithms, Autonomous Vehicle1 IntroductionDesign of linguistic variables and rules of a rule-based
Additive fuzzy systems can control the velocity and the gap between cars in single-lane platoons. The overall system consists of throttle and brake controllers. We first designed and tested a throttle-only fuzzy system on a validated car model and then with a real car on highway 1–15 in California. We used this controller to drive the “smart” car on the
A generalized control strategy that enhances fuzzycontrollers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented. This methodology, termed temporal backpropagation, is model-sensitive 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
In this paper a fuzzy-control system has been designed, implemented and embedded in an open CNC. The integration process, design steps and results of applying an embedded fuzzy-control system are shown through the example of real machining operations. The controller uses internal CNC signals (i.e. spindle-motor current) that are gathered and mathematically processed by means of an integrated application. The
R. E. Haber; J. R. Alique; A. Alique; J. Hernández; R. Uribe-Etxebarria
A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this\\u000a paper. Interval type-2 fuzzycontrollers can outperform conventional type-1 fuzzycontrollers when the problem has a high\\u000a degree of uncertainty. However, designing interval type-2 fuzzycontrollers is more difficult because there are more parameters\\u000a involved. In this paper, interval type-2 fuzzy systems are
Oscar Castillo; Patricia Melin; Arnulfo Alanis Garza; Oscar Montiel; Roberto Sepúlveda
The flexibility demand of marine nuclear power plant is very high, the multiple parameters of the marine nuclear power plant with the once-through steam generator are strongly coupled, and the normal PID control of the turbine speed can’t meet the control demand. This paper introduces a turbine speed Fuzzy-PID controller to coordinately control the steam pressure and thus realize the demand for quick tracking and steady state control over the turbine speed by using the Fuzzycontrol’s quick dynamic response and PID control’s steady state performance. The simulation shows the improvement of the response time and steady state performance of the control system.
Advances in model predictive control using fuzzy tools are presented. Research results are aggregated to present a complete approach based on data-driven fuzzy tools. A fuzzy model of the system is identified from sampled data using supervised fuzzy clustering for rule extraction. This model is used in model predictive control. The non-convex optimization problem introduced by a nonlinear plant model
In this paper we present an approach to designing a novel type of fuzzycontroller. B-spline basis functions are used for input variables and fuzzy singletons for output variables to specify linguistic terms. "Product" is chosen as the fuzzy conjunction, and "centroid" as the defuzzification method. By appropriately designing the rule base, a fuzzycontroller can be interpreted as a
Advanced intelligent control design cannot entirely replace the application of conventional controller in robotic system. On the other hand, intelligent controllers can be implemented into conventional controller design to increase its performance and ability. Most of intelligent control in movement control involves fuzzy logic and neural network system. This study features the influence of fuzzy logic controller upon the performance
Azura Che Soh; Erny Aznida Alwi; Ribhan Zafira Abdul Rahman; Li Hong Fey
Since the pioneering works of Zadeh and Mamdani and Assilian the conventional fuzzy logic controller has been successfully implemented in many industrial applications As the number of system variables increase, the number of rules in a conventional comple...
The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control
Attachment of stormwater treatment areas (STAs) or constructed wetlands to stormwater retention reservoirs can achieve substantial reductions in pollutant loadings if properly operated and maintained. Besides water quality improvement, optimally operated reservoir-assisted STAs provide support for ecosystem remediation, flood control, and supplemental water supply. An adaptive, multiobjective real-time control model is developed for reservoir-assisted STA systems that incorporates fuzzy rule-based
This paper continues the investigation of a “shoulder-elbow-like” single flexible robot arm model with damping, previously discussed by the authors (1996), to develop a new design of a fuzzy (PI+D)2 control scheme for both vibration suppression and set-point tracking. Computer simulation results are included to show that the fuzzy-logic-based controllers perform very well for this flexible-link model described by a
Controlling the movement of an autonomous mobile robot requires the ability to pursue strategic goals in a highly reactive way. The authors describe a fuzzycontroller for such a mobile robot that can take abstract goals into consideration. Through the use of fuzzy logic, reactive behavior, e.g., avoiding obstacles on the way, and goal-oriented behavior, e.g., trying to reach a
Obtaining active control over the dynamics of quantum-mechanical systems is a fascinating perspective in modern physics. A promising tool for this purpose is available with femtosecond laser technologies. The intrinsically broad spectral distribution and the phase function of femtosecond laser pulses can be specifically manipulated by pulse shapers to drive molecular systems coherently into the desired reaction pathways . The approach of adaptive femtosecond quantum control follows the suggestion of Judson and Rabitz , in which a computer-controlled pulse shaper is used in combination with a learning algorithm  and direct feedback from the experiment to achieve coherent control over quantum-mechanical processes in an automated fashion, without requiring any model for the system's response. This technique can be applied to the control of gas-phase photodissociation processes . Different bond-cleaving reactions can be preferentially selected, resulting in chemically different products. Prior knowledge about molecular Hamiltonians or reaction mechanisms is not required in this automated control loop, and this scheme works for complex systems. Adaptive pulse-shaping techniques can be transferred to the control of photoprocesses in the liquid phase as well, motivated by the wish to achieve control at particle densities high enough for (bimolecular) synthetic-chemical applications. Chemically selective molecular excitation is achieved by many-parameter adaptive quantum control , despite the failure of typical single-parameter approaches (such as wavelength control, intensity control, or linear chirp control). This experiment demonstrates that photoprocesses in two different molecular species can be controlled simultaneously. Applications are envisioned in bimolecular reaction control where specific educt molecules could selectively be "activated" for purposes of chemical synthesis. A new technological development further increases the possibilities and prospects of quantum control. With the technique of femtosecond polarization pulse shaping , it is now possible to vary intensity, momentary frequency, and light polarization (i.e., the degree of ellipticity as well as the orientation of the principal axes) as functions of time within a single femtosecond laser pulse.  T. Brixner, N. H. Damrauer, and G. Gerber, Femtosecond quantum control, In Advances in Atomic, Molecular, and Optical Physics (B. Bederson and H. Walther, Eds.), Vol. 46, pp. 1-54, Academic Press (2001)  R. S. Judson and H. Rabitz, Teaching lasers to control molecules, Phys. Rev. Lett. 68, 1500 (1992)  T. Baumert, T. Brixner, V. Seyfried, M. Strehle, and G. Gerber, Femtosecond pulse shaping by an evolutionary algorithm with feedback, Appl. Phys. B 65, 779 (1997)  A. Assion, T. Baumert, M. Bergt, T. Brixner, B. Kiefer, V. Seyfried, M. Strehle, and G. Gerber, Control of chemical reactions by feedback-optimized phase-shaped femtosecond laser pulses, Science 282, 919 (1998)  T. Brixner, N. H. Damrauer, P. Niklaus, and G. Gerber, Photoselective adaptive femtosecond quantum control in the liquid phase, Nature, Vol. 414, 57 (2001)  T. Brixner and G. Gerber, Femtosecond polarization pulse shaping, Opt. Lett. 26, 557 (2001)
The fuzzyadaptive back-propagation (FABP) algorithm which combines fuzzy theory with artificial neural network techniques is applied to the identification of restoring forces in non-linear vibration systems. Simulated results show that the FABP algorithm is effective for the identification of dynamic systems. The FABP algorithm not only increases the training speed of the network, but also decreases the artificial interference of network parameters to a certain extent. Based upon the FABP algorithm, an improved scheme with a mutation mechanism is presented in this paper. The improved fuzzyadaptive BP (IFABP) algorithm extends the effectiveness and adaptivity of the FABP algorithm still further. The successful estimation of simulated systems show that a feasible method of identification is provided, which can be used to estimate the restoring forces in non-linear vibrating systems quickly and effectively.
The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature conver- gence problem and improve the final results. One of
This paper describes the design, implementation and operational performance of a fuzzycontroller as part of the automatic generation control (AGC) system in Eskom's National Control Centre. The fuzzycontroller was implemented in the control ACE (area control error) calculation, which determines the shortfall or surplus generation that has to be corrected. This paper sets out the problems associated with
An integrated fuzzy logic controller is proposed in this paper for the generator excitation and speed governing control. The proposed controller has three control loops: the first one is the voltage control loop which has the function of automatic voltage regulator (AVR), the second one is the damping control loop which has the function of power system stabilizer (PSS), and
A Neuro-fuzzycontrol 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
An adaptive feedforward control loop is provided to stabilize accelerator beam loading of the radio frequency field in an accelerator cavity during successive pulses of the beam into the cavity. A digital signal processor enables an adaptive algorithm to generate a feedforward error correcting signal functionally determined by the feedback error obtained by a beam pulse loading the cavity after the previous correcting signal was applied to the cavity. Each cavity feedforward correcting signal is successively stored in the digital processor and modified by the feedback error resulting from its application to generate the next feedforward error correcting signal. A feedforward error correcting signal is generated by the digital processor in advance of the beam pulse to enable a composite correcting signal and the beam pulse to arrive concurrently at the cavity.
Eaton, Lawrie E. (Los Alamos, NM); Jachim, Stephen P. (Los Alamos, NM); Natter, Eckard F. (Santa Fe, NM)
Type-II fuzzy model is useful to handle the influence of uncertainties. This paper presents an algorithm of Type-II T-S fuzzy (T2TSF) modeling based on data clustering and two approaches to design T2TSF model-based predictive controllers. As the T2TSF model is an extension of T1TSF (Type-I T-S fuzzy) model, the T2TSF modeling algorithm divides the input-output data set into several Type-I
The conventional cerebellar model articulation controllers (CMAC) learning scheme equally distributes the correcting errors into all addressed hypercubes, regardless of the credibility of those hypercubes. This paper presents the adaptive fault-tolerant control scheme of non-linear systems using a fuzzy credit assignment CMAC neural network online fault learning approach. The credit assignment concept is introduced into fuzzy CMAC weight adjusting to
To improve limitations of fuzzy PI controller especially when applied to high order systems, we propose two types of fuzzy logic controllers that take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional fuzzy PI controllers. The structures of the proposed controller were motivated by the problems
Based on the dynamics nonlinearities of a nonholonomic mobile robot, an advanced fuzzy immune PED-type control algorithm is proposed for robot path tracking. The novel tracking controller combines fuzzycontrol, immune feedback mechanism of organism with conventional PID control. In the algorithm, fuzzy immune PID controller is improved through the mixed connection of conventional PID and P-type immunity feedback controller.
An automatic design method is proposed for fuzzycontrol and decision\\/diagnosis systems. Thismethod extends traditional fuzzy systems by a learning ability without changing the fuzzy rule framework.The fuzzy rules and linguistic variables are extracted from a referential data set by a self-organizing process.A genetic algorithm is used to find optimal input\\/output membership functions.
Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy
Since the dynamic response trajectory of a traditional fuzzycontroller can not be quantitatively controlled, a fuzzy model following controller is proposed in this paper. In the proposed controller, an output feedback linear model following controller (LMFC) is first designed according to the roughly estimated plant model to let its response follow the output generated by a reference model. Then
This paper describes the design, implementation and operational performance of a fuzzycontroller as part of the automatic generation control (AGC) system in Eskom's National Control Centre. The fuzzycontroller was implemented in the area control error calculation, which determines the shortfall or surplus generation that has to be corrected. This paper sets out the problems associated with secondary frequency
The way engineers use fuzzycontrol in real world applications is often not coherent with an understanding of the control rules as logical statements or implications. In most cases fuzzycontrol can be seen as an interpolation of a partially specified control function in a vague environment, which reflects the indistinguishability of measurements or control values. In this paper the
Fuzzycontroller of temperature was designed in the inverter air-condition with dual purpose both cold and hot. The control system of inverter air conditioner was divided two parts, that is, indoor machine and the outdoor machine. The frequency control of indoor and outside machine was realized by control software in the foundation of hardware circuit design. Fuzzycontrol program was
In order to design a fuzzycontroller for complex nonlinear systems, the work of this paper deals with developing the relaxed stability conditions for continuous-time affine Takagi-Sugeno (T-S) fuzzy models. By applying the passivity theory and Lyapunov theory, the relaxed stability conditions are derived to guarantee the stability and passivity property of closed-loop systems. Based on these relaxed stability conditions, the synthesis of fuzzycontroller design problem for passive continuous-time affine T-S fuzzy models can be easily solved via the Optimal Convex Programming Algorithm (OCPA) and Linear Matrix Inequality (LMI) technique. At last, a simulation example for the fuzzycontrol of a nonlinear synchronous generator system is presented to manifest the applications and effectiveness of proposed fuzzycontroller design approach. PMID:19389667
Chang, Wen-Jer; Ku, Cheung-Chieh; Huang, Pei-Hwa; Chang, Wei
Development of adaptive and robust controllers for nonlinear coupled systems is inspired by new possibilities of hardware-realization, It concerns each kind of artificial intelligence: “classical” knowledge-based systems (KBSs), and artificial neural network- (ANN) and fuzzy set- (FS) based formulations. While common and general roots of these approaches became transparent, in each case complexity of the control rules, number of ANN
I. J. Rudas; F. Pereszlenyi; J. K. Tar; J. F. Bito
We investigate an observed-state feedback stabilization problem involving sampled-data fuzzy systems arising from rapid growth of digital controller implementations. The underlying closed-loop fuzzy system is shown to be asymptotically stable when intersampling effects are taken into account. Being a periodically time-varying hybrid discrete\\/continuous system, the Riccati inequality associated with the sampled-data system poses difficulties for stabilization analysis using LMI convex
In this paper, an intelligent robust fractional surface sliding mode control for a nonlinear system is studied. At first a sliding PD surface is designed and then, a fractional form of these networks PD?, is proposed. Fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. To reduce the chattering phenomenon in sliding mode control (SMC), a fuzzy logic controller is used to replace the discontinuity in the signum function at the reaching phase in the sliding mode control. For the problem of determining and optimizing the parameters of fuzzy sliding mode controller (FSMC), genetic algorithm (GA) is used. Finally, the performance and the significance of the controlled system two case studies (robot manipulator and coupled tanks) are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results signify performance of genetic-based fuzzy fractional sliding mode controller.
Delavari, H.; Ghaderi, R.; Ranjbar, A.; Momani, S.
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
It is well known that robotic manipulators are highly nonlinear coupling dynamic systems. It is difficult to establish an appropriate mathematical model for the design of a model-based controller. Although fuzzy logic control has a model-free feature, it still needs time-consuming work for the rules bank and fuzzy parameters adjustment. In this paper, a stable self-organizing fuzzycontroller (SOFC) is
Active queue management (AQM) is aimed at achieving the tradeoff between link utilization and queuing delay to enhance TCP\\u000a congestion control and is expected to perform well for a wider-range of network conditions. Static AQM schemes despite their\\u000a simplicity, often suffer from long response time due to conservative parameter setting to ensure stability. Adaptive parameter\\u000a settings, which might solve this
Xinping Guan; Bo Yang; Bin Zhao; Gang Feng; Cailian Chen
This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuous-time linear PD controller. Then, the fuzzification, control-rule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the
To the problem that ball mill of cement course is a complex nonlinear multivariable process with strongly coupling and time-delay, the traditional PID control is difficult to apply to such system. Incorporating the grey prediction, fuzzycontrol, a design method of the grey prediction fuzzycontrol is presented. At first, this control design a fuzzycontroller, and then, put the
Wang Xiaohong; Xie Haiyang; Jing Shaohong; Yuan Zhugang
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 fuzzycontrol
By relating to the conventional PID control theory, we propose a new fuzzycontroller structure, namely PID type fuzzycontroller. In order to improve further the performance of the transient state and the steady state of the PID type controller, we develop a method to tune the scaling factors of the PID type fuzzycontroller on line. Simulation of the
This paper proposes a kind of fuzzy logic control scheme according to the time-varying, lagging and nonlinear characteristics of temperature control and the proplem of more complicated control system. It includes the selection of control variables, the definitions of fuzzy sets, the division of domain levels, the choice of membership functions and setting the fuzzy logic control rules. MATLAB was
The permanent magnet synchronous motor (PMSM) drive system using adaptive backstepping fuzzy neural network (ABFNN) control is investigated for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMSM servo drive. Then, an adaptive backstepping approach is proposed to compensate the uncertainties in the motion control system. With the proposed
This paper proposes a new sliding mode design concept, namely adaptive seeking sliding mode control for a class of nonlinear systems. While reserving the properties of the sliding mode control, like insensitivity to parameter variations and complete rejection of disturbances, adaptive seeking sliding mode control offers a promising robust sliding mode control solutions for real-life engineering applications with simple control
This paper introduces a neuro-fuzzycontroller (NFC) for the speed control of a PMSM. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performance of the proposed controller is verified by both simulations and
A novel control strategy of Fuzzy-Immune PID based on GA is proposed in this paper. The strategy learns from the immune feedback principle of biological immune system and takes advantage of the good approaching ability of fuzzycontrol to achieve nonlinear function of the immune controller, and then the GA algorithm is used to optimize parameters of PID controller and
Yuzhen Yu; Fengshan Du; Xinyi Ren; Shangbin Zhang; Wenxu Hao
The solar cell has an optimum operating point to be able to get the maximum power. To obtain the maximum power from a photovoltaic array, the photovoltaic power system usually requires a maximum power point tracking controller. This paper proposes a maximum power point tracking control of photovoltaic array using fuzzycontrol; the controller only uses the output power. Therefore,
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
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) and Principle Component Analysis(PCA), for classification of electroencephalogram (EEG) signals. Different mental tasks have been used to understand the process in our mind and we have chosen relaxation and imagination for our study. As well as normal conscious state, we have considered mental tasks performed in hypnosis which is
|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…
Inferring genetic networks from gene expression data is the most challenging work in the postgenomic era. However, most studies tend to show their genetic network inference ability by using artificial data. Here, we developed the fuzzyadaptive resonance theory associated matrix (F-ART matrix) method to infer genetic networks and applied it to experimental time series data, which are gene expression
Since last decade advanced data simulations help to identify hidden trends in a time series. Our purpose is to identify uncertainties during recession period using statistical analysis, econometrical analysis and Adaptive Neural-Fuzzy networks. In this paper, initially through computational analysis we are testing financial data using correlation tests, likelihood tests, heteroscedastic characteristics analysis and hypothesis tests. These statistical and econometrical
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
Use of wind energy as a renewable source of energy for electric utility systems is increasing around the world. The major challenges of wind energy generation are natural intermittency, unpredictability, and uncertainty due to wind variations. In this paper, five different adaptive neuro-fuzzy wind predictors are proposed and compared to forecast the speed of wind blowing in the East Coast
O. M. Salim; M. A. Zohdy; H. T. Dorrah; A. M. Kamel
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of
The grinding performance is influenced and determined by the disc dressing conditions due to effects of dressing process on the wheel surface topography. In this way, prediction of the grinding specific energy helps to optimize the disc dressing conditions to increase grinding performance. The objective of this study is the design of adaptive neuro-fuzzy inference system (ANFIS) for estimation of
This paper presents a modified design of a sliding mode controller based on fuzzy logic for a dc-dc boost converter. Here a proportional - integral (PI)-type current mode control is employed and a sliding mode controller is designed utilizing fuzzy algorithm. Sliding mode controller ensures robustness against all variations and fuzzy logic helps to reduce chattering phenomenon introduced by sliding
Tackles some critical issues in diagnostic problem solving by addressing the conceptual design of automatic diagnostic systems able to cope with complex dynamic processes. The dynamic switching fuzzy system model introduced by M.H. Smith (1994) is used as a generalized framework which dynamically changes the reasoning method by tuning the operators and\\/or defuzzification methods. This technique is applied to tune
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.
In this paper, we first present mathematical analysis of what causes the fuzzy logic controllers perform better than PI controllers and an experimental verifications of it by using a model nuclear steam generator. Next, we developed a fuzzy algorithm for ...
In this paper. we study the controllability for the impulsive semilinear fuzzy integrodifferential control system with nonlocal conditions in EN by using the concept of fuzzy number whose values are normal, convex, upper semicontinuous and compactly supported interval in EN.
Kwun, Y. C.; Hwang, J. S.; Park, J. S.; Park, J. H.
In this article, a control design concept using fuzzy sets for an induction motor is presented. The aim of the proposed modelling approach is to provide a fuzzy set-based representation of the cascade sliding mode control of an induction motor fed by PWM voltage source inverter, which operates in a fixed reference frame. For this purpose, a new decoupled and
We investigated the analytical structure of the Takagi-Sugeno (TS) type of fuzzycontrollers, which was unavailable in the literature. The TS fuzzycontrollers we studied employ a new and simplified TS control rule scheme in which all the rule consequent use a common function and are proportional to one another, greatly reducing the number of parameters needed in the rules.
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
This work presents the implementation of a conventional PID and a fuzzy PI control schemes in a 16 bits microcontroller. These schemes are designed for the speed control of a DC brush motor. This article makes emphasis in the handling of the floating point for the first case. For the fuzzycontrol, the data is obtained directly from the digital
J. J. Muoz-Cesar; E. A. Merchan-Cruz; L. H. Hernandez-Gomez; E. Guerrero-Guadarrama; A. Jimenez-Ledesma; I. Jaidar-Monter
Fuzzy Cognitive Maps (FCMs) is a new approach in modelling the behaviour and operation of complex systems. FCMs are proposed to be used in the modelling of control systems and particularly in the modelling of the upper part or supervisor of a hierarchical control system. The description and the formulation of FCM are examined, moreover a process control problem is
This paper describes a generalized predictive control (GPC) algorithm based on fuzzy models and its application to the tailing grade control in a mineral flotation plant. The control strategy is evaluated using a dynamic process simulator and their results are compared with those obtained with a conventional GPC
Electric power steering system (EPS) has been developing rapidly and becomes one of the safe-critical systems in modern control system of cars. The tracking performance of control system is an important factor that affects the assistance of electric power steering system. In order to improve the overall performance of electric power steering, fuzzy logic control theory was applied to the
Zhanfeng Gao; Wenjiang Wu; Jianhua Zheng; Zhanpeng Sun
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
Changbum Kim; Kyoung A. Seong; Hyung Lee-kwang; Jeong O. Kim
In this paper a new approach to tune the control gains of a PD controller has been outlined. The proposed novel fuzzy logic controller uses the functional relation between the rule premises and consequences. By introducing the possibility measure of the rules a method for generating the rule base is given. Simulation has been carried out in order to evaluate