Model-free PID control tuning methods have been widely reported for the case with the time-varying uncertain systems. A model-free control system is introduced in this paper with PID controller. This control design co...
详细信息
ISBN:
(纸本)9781479917624
Model-free PID control tuning methods have been widely reported for the case with the time-varying uncertain systems. A model-free control system is introduced in this paper with PID controller. This control design could be considered as a contribution to kernel wavelet neural network PID controllers in model-free system with a straightforward tuning by the simultaneous perturbation stochastic approximation algorithm. It is particularly well suited to problems involving the measurement data without prior inner structure knowledge of the unknown system. On the other hand, neural network activation functions are bounded which cause the outputs of neural network to be limited. Therefore, the T-S Fuzzy control method on basis of kernel wavelet neural network (kwnn) is introduced to improve the control parameter tuning adaptivity in terms of boundedness of the kwnn excitation function. Finally, simulation results are presented to exhibit the effectiveness of the proposed IPWR control system.
Model-free PID control tuning methods have been widely reported for the case with the time-varying uncertain systems. A model-free control system is introduced in this paper with PID controller. This control design co...
详细信息
ISBN:
(纸本)9781479917631
Model-free PID control tuning methods have been widely reported for the case with the time-varying uncertain systems. A model-free control system is introduced in this paper with PID controller. This control design could be considered as a contribution to kernel wavelet neural network PID controllers in model-free system with a straightforward tuning by the simultaneous perturbation stochastic approximation algorithm. It is particularly well suited to problems involving the measurement data without prior inner structure knowledge of the unknown system. On the other hand, neural network activation functions are bounded which cause the outputs of neural network to be limited. Therefore, the T-S Fuzzy control method on basis of kernel wavelet neural network (kwnn) is introduced to improve the control parameter tuning adaptivity in terms of boundedness of the kwnn excitation function. Finally, simulation results are presented to exhibit the effectiveness of the proposed IPWR control system.
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