Adaptive neural network tracking control for switched uncertain non-linear systems with actuator failures and time-varying delays

2019 ◽  
Vol 13 (12) ◽  
pp. 1929-1939 ◽  
Author(s):  
Qinghua Hou ◽  
Jiuxiang Dong ◽  
Changjiang Xi
Keyword(s):  
Neural Network ◽  
Linear Systems ◽  
Tracking Control ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Actuator Failures ◽  
Non Linear ◽  
Non Linear Systems

Robust adaptive neural network control for a class of non-linear systems

1997 ◽  
Vol 211 (3) ◽  
pp. 171-181
Author(s):  
S S Ge ◽  
T H Lee
Keyword(s):  
Neural Network ◽  
Linear Systems ◽  
Sliding Mode ◽  
Reference Signal ◽  
Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Non Linear ◽  
Non Linear Systems ◽  
Robust Adaptive

In this paper, a general framework for robust parallel adaptive neural network (NN) control design is presented for a class of non-linear systems motivated by the work in references (14) and (15). The controller is based on applying direct adaptive techniques to an additional parallel neural network to provide adaptive enhancements to a basic fixed controller and incorporating a sliding mode term for robustness. It is shown that if bounded basis function (BBF) networks are used for the additional parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the reference signal is achieved. Because of the introduction of the GL (Ge-Lee) matrices and operator, the results presented here are more general than the existing results.

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