Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer

2013 ◽  
Vol 43 (4) ◽  
pp. 1213-1225 ◽  
Author(s):  
Mou Chen ◽  
Shuzhi Sam Ge
Keyword(s):  
Nonlinear Systems ◽  
Disturbance Observer ◽  
Neural Control ◽  
Adaptive Neural Control ◽  
Nonaffine Nonlinear Systems

scholarly journals Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chaojiao Sun ◽  
Bo Jing ◽  
Zongcheng Liu
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Tracking Error ◽  
Nonlinear Function ◽  
Mean Value ◽  
Mean Value Theorem ◽  
Adaptive Neural Control ◽  
Control Gain ◽  
Control Scheme ◽  
Nonaffine Nonlinear Systems

An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

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