Fuzzy Adaptive Robust Control for Stochastic Switched Nonlinear Systems With Full-State-Dependent Nonlinearities

2020 ◽  
Vol 28 (9) ◽  
pp. 2035-2047
Author(s):  
Zhanjie Li ◽  
Jun Zhao
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Adaptive Robust Control ◽  
Switched Nonlinear Systems ◽  
State Dependent ◽  
Full State ◽  
Stochastic Switched Nonlinear Systems ◽  
Fuzzy Adaptive

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

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