Parameter Learning of Neuro-Fuzzy Controller Using Particle Swarm Optimization for Chaotic System Control (original) (raw)
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Adaptive control of nonlinear system using neuro-fuzzy learning by PSO algorithm
2012 16th IEEE Mediterranean Electrotechnical Conference, 2012
This paper proposes the optimization of parameters of neuro-fuzzy system using the particle swarm optimization. Neuro-fuzzy techniques have emerged from the fusion of neural networks and fuzzy inference systems. They could serve as a powerful tool for system modeling and control. These fuzzy systems are optimized by adapting the antecedent and consequent parameters. Among them, the ANFIS use the least square to optimize the consequent parameters and retropropagation to train the antecedent parameters. Several learning algorithms of fuzzy models have been proposed, e.g. evolutionary algorithms, such as particle swarm optimization. These different methods have been developed to learn the parameters of neuro-fuzzy system and to test them in the on-line control of nonlinear system. I.
2013
Chaotic particle swarm optimization (CPSO) is a newly developed optimization technique which combines the benefits of particle swarm optimization (PSO) and the chaotic optimization. This combination aims at avoiding the premature convergence of the PSO and the shortcomings of the chaotic optimization, in particular, the slow searching speed and the low accuracy when applied in optimizing a large search space. In addition, unlike conventional artificial neural networks (ANNs), the radial basis function neural network (RBFNN) has a more compact structure and consequently, it requires less training time compared with other ANNs and neuro-fuzzy systems. In this paper, an adaptive CPSO technique is utilized to train a RBFNN to act as a controller for nonlinear dynamical systems. Since the CPSO is a derivative-free optimization method, there is no need for a teaching signal to train the RBFNN to operate as a controller. The adaptive CPSO is employed to optimize all the modifiable paramete...
2016
In this study, the control of a non-linear system was realized by using a linear system control strategy. According to the strategy and by using the controller coefficients, system outputs were controlled for all reference points with the same coefficients via focused references. In the framework of this study, the Lorenz chaotic system as non-linear structure, and the discrete-time PI algorithm as the control algorithm has selected. The genetic algorithm and particle swarm optimization methods have used in the optimization process, and the success of both methods has been discussed among themselves. Closed-loop control system has run simultaneously under the Matlab / Simulink programmer. The results have discussed by using the ISE, IAE, ITAE error criteria, and improved dTISDSE purpose functions.
Optimal Learning of Fuzzy Neural Network Using Particle Swarm Optimization Algorithm
2005
Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.
International Journal of Intelligent Systems and Applications in Engineering
In this study, the control of a non-linear system was realized by using a linear system control strategy. According to the strategy and by using the controller coefficients, system outputs were controlled for all reference points with the same coefficients via focused references. In the framework of this study, the Lorenz chaotic system as non-linear structure, and the discrete-time PI algorithm as the control algorithm has selected. The genetic algorithm and particle swarm optimization methods have used in the optimization process, and the success of both methods has been discussed among themselves. Closed-loop control system has run simultaneously under the Matlab / Simulink programmer. The results have discussed by using the ISE, IAE, ITAE error criteria, and improved dTISDSE purpose functions.
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, 2010
This study deals with the control of chaos using Generalized Backstepping Method. This new method to control nonlinear systems was called Generalized Backstepping method because of its similarity to Backstepping but its abilities to control more systems than Backstepping. The General Backstepping Method could achieve better performance than Backtepping Method in respect of lower signal control, short settling time and overshoot, control ability of MIMO systems and non strict feedback systems. The Generalized Backstepping approach consists of parameters which accept positive values. The parameters are usually chosen optional. The system responded differently for each value. This paper introduce a novel adaptive neuro fuzzy control method which trained by different error data to achieve optimal parameters. Hence with optimal parameters controller can stabilize the Chen chaos in much quicker than general backstepping method.
International Journal of Computer and Electrical Engineering, 2012
In this paper, an intelligent backstepping controller, tuned using a chaotic particle swarm optimization (CPSO), is proposed to control of chaos in Genesio-Tesi chaotic system. Thebackstepping method consists of parameters with positive values. The parameters are usually chosen optional by trial and error method. The improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. The proposed intelligent backstepping controller without trial and error determines the parameters of backstepping controller automatically and intelligently by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output. Finally, the efficiency of the proposed intelligent backstepping controller (IBSC) is illustrated by controlling the chaos of Genesio-Tesi chaotic system.
IET Science, Measurement & Technology, 2013
This study deals with the problem of controlling a class of uncertain non-linear systems in the presence of external disturbances. To achieve this goal, a novel optimal Type-2 fuzzy proportional integral derivative (OT2FPID) controller is introduced. In the proposed controller, a novel heuristic algorithm namely particle swarm optimisation with random inertia weight (RNW-PSO) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input and output membership functions are optimised simultaneously by RNW-PSO. To evaluate the performance of the proposed controller, the results are compared with those obtained by optimal H ∞ adaptive proportional integral derivative controller, which is the latest research in the problem in hand. Simulation results show the effectiveness of the OT2FPID controller.
This paper presents a TSK-type recurrent fuzzy neural network (TRFNN) system to identify and control nonlinear uncertain systems. The TRFNN is modified from the previous result RFNN: the consequent part is replaced by linear combination of input variables to obtain generalization and fast convergence rate; the internal variable-fire strength is feedforward to output to increase the network ability. In addition, a hybrid learning algorithm (GA_BPPSO) is proposed to have a fast convergent rate, which combines the genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO). Several simulation results including system identification and control are proposed to show the effectiveness of TRFNN system and GA-BPPSO algorithm.
Control of continuous stirred tank reactor (CSTR) using nature inspired algorithms
Journal of Information and Optimization Sciences, 2019
In this paper optimization algorithms name as particle swarm optimization (PSO) and teacher learning based optimization (TLBO) has been implemented on continuous stirred tank reactor (CSTR). Conventional PID controller, PSO based PID control scheme and TLBO based PID control scheme are used to control the system concentration and temperature. Optimization algorithms are used to improve system performance by minimizing mean square error (MSE) and optimize the controller parameters like K p , K i , K d .The Simulation results shows that TLBO based PID controller gives better performance, optimized values of controller parameters and minimum values of MSE in less iteration as compare to PSO based PID controller and Conventional PID controller.