Adaptive neural control for pure feedback nonlinear systems with uncertain actuator nonlinearity

Conference paper (2017)

Authors

Maolong Lv Air Force Engineering University
Ying Wang Air Force Engineering University
S. Baldi Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Zongcheng Liu Air Force Engineering University
Chao Shi Air Force Engineering University
Chaoqi Fu Air Force Engineering University
Xiangfei Meng Air Force Engineering University
Yao Qi Air Force Engineering University
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
Robust control Adaptive neural control Actuator input nonlinearity Non-affine function
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Published Date

2017

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

Abstract

For the pure feedback systems with uncertain actuator nonlinearity and non-differentiable non-affine function, a novel adaptive neural control scheme is proposed. Firstly, the assumption that the non-affine function must be differentiable everywhere with respect to control input has been canceled; in addition, the proposed approach can not only be applicable to actuator input dead zone nonlinearity, but also to backlash nonlinearity without changing the controller. Secondly, the neural network (NN) is used to approximate unknown nonlinear functions of system generated in the process of control design and a nonlinear robust term is introduced to eliminate the actuator nonlinearity modeling error, the NN approximation error and the external disturbances. Semi-globally uniformly ultimately boundedness of all signals in the closed loop system is analytically proved by utilizing Lyapunov theory. Finally, the effectiveness of the designed method is demonstrated via two examples.