Neural network–based direct adaptive robust control of unknown MIMO nonlinear systems using state observer

2019 ◽  
Vol 34 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Cheng Cheng ◽  
Songyong Liu ◽  
Hongzhuang Wu ◽  
Ying Zhang
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
State Observer ◽  
Adaptive Robust Control ◽  
Mimo Nonlinear Systems

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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