Radial basis function neural network-based adaptive critic control of induction motors

Lin, Chih-Min

doi:10.1016/j.asoc.2010.12.007

Cited by 38 publications

(15 citation statements)

References 25 publications

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“…A nonlinear control problem example to demonstrate the convergence of the proposed probabilistic critic network is given in Section V. Further discussion on the convergence and speed of convergence of adaptive critic designs can be found in [20]. Moreover, empirical evidence on the convergence of the adaptive critic design can be found in [3], [11], [19], [21].…”

Section: Nonlinear Randomized Control Algorithm Based On Probabilmentioning

confidence: 99%

Probabilistic DHP adaptive critic for nonlinear stochastic control systems

Herzallah

2013

Neural Networks

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Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems [15], [18], this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method [15] and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work [15], [18] by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained. Index Termsnonlinear stochastic systems; fully probabilistic design; nonlinear randomized control input design; adaptive critic.

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Section: Nonlinear Randomized Control Algorithm Based On Probabilmentioning

confidence: 99%

Probabilistic DHP adaptive critic for nonlinear stochastic control systems

Herzallah

2013

Neural Networks

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“…Though the favorable control performance can be achieved using feedforward neural networks in [14][15][16][17], the usage of the long tapped delay input will result in a large network size. To deal with this problem, interest in using recurrent neural networks for processing dynamic nonlinear plants has been steadily growing [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem, many studies on neural fuzzy networks for controller development and for describing the system dynamics of unknown nonlinear systems have been published [14][15][16][17]. A main property of the neural networks regarding feedback control purpose is a universal function approximation property.…”

Section: Introductionmentioning

confidence: 99%

Intelligent complementary sliding-mode control with dead-zone parameter modification

Hsu

Kuo

2014

Applied Soft Computing

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“…Fuzzy logic controllers (FLCs) are preferred over traditional controllers because the former is less dependent on the mathematical model and system parameters [6]. Artificial neural networks [7], [8] ANFIS used as controller on the speed and torque in the vector controller. However, the above mentioned controllers encounter problems because of their huge data requirement and long training and learning time.…”

Section: Introductionmentioning

confidence: 99%

Gravitational search optimization approach to improve fuzzy logic speed controller for induction motor drive

Ali

Hannan

Mohamed

2016

ELECTROTECHNICAL REVIEW

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Fuzzy logic controller (FLC) is very useful for controlling speed and torque variables in the three-phase induction motor (TIM) operation. However, the conventional FLC has the exhaustive traditional trial and error procedure in obtaining membership functions (MFs). This paper presents an adaptive FLC design technique for TIM using a gravitational search algorithm (GSA) optimization technique. This technique provides the numerical values to limit the error and change in error of the MFs based on the evaluation results of the objective function formulated by the GSA. The root mean square error (RMSE) of the speed response is used as a fitness function. An optimal GSA-based FLC (GSAF) fitness function is also employed to tune and minimize the RMSE for improving the performance of the TIM in terms of changes speed and torque. Space vector pulse width modulation (SVPWM) technique is utilized to generate signals via voltage/frequency control strategy for variable frequency inverter. Results obtained from the GSAF are compared with those obtained through particle swarm optimization (PSO) to validate the developed controller. The robustness of the GSAF is better than that of the PSO controller in all tested cases in terms of damping capability and transient response under different load and speed. Streszczenie. W artykule zaprezentowano adaptacyjny sterownik typu fuzzy logic przeznaczony do trójfazowego silnika indukcyjnego wykorzystujący algorytm optymalizacyjny badania grawitacyjnego. Jako funkcję fitness użyto błąd rms odpowiedzi prędkości. Do zasilania silnika wykorzystano metodę modulacji szerokości impulsu. Optymalizacja typu grawitacyjne badanie jako metoda poprawy jakości sterownika fuzzy logic zastosowanego do silnika indukcyjnego

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Radial basis function neural network-based adaptive critic control of induction motors

Cited by 38 publications

References 25 publications

Probabilistic DHP adaptive critic for nonlinear stochastic control systems

Probabilistic DHP adaptive critic for nonlinear stochastic control systems

Intelligent complementary sliding-mode control with dead-zone parameter modification

Gravitational search optimization approach to improve fuzzy logic speed controller for induction motor drive

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