High-accuracy robust adaptive motion control of a torque-controlled motor servo system with friction compensation based on neural network

In this paper, a high-accuracy motion control of a torque-controlled motor servo system with nonlinear friction compensation is presented. Friction always exists in the servo system and reduces its tracking accuracy. Thus, it is necessary to compensate for the friction effect. In this paper, a novel controller that combines robust adaptive control with friction compensation based on neural network observer is proposed. An improved LuGre friction model is applied into the friction compensation as it is known as a good model to express the nonlinear friction. A single hidden-layer network is utilized to observe the immeasurable friction state. Then, the robust adaptive controller is used to handle the parametric uncertainty, the parametric estimation error, friction compensation error, and other uncertainties. Lyapunov theory is utilized to analyze the stability of the closed-loop system. The experimental results demonstrate the effectiveness of the proposed algorithm.

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High-accuracy motion control of a motor servo system with dead-zone based on a single hidden layer neural network

Neural Network World ◽

10.14311/nnw.2020.30.002 ◽

2020 ◽

Vol 30 (1) ◽

pp. 27-44

Author(s):

Jian Hu ◽

Shupeng Cao ◽

Chenchen Xu ◽

Jianyong Yao ◽

Zhiwei Xie

Keyword(s):

Neural Network ◽

Motion Control ◽

Dead Zone ◽

Servo System ◽

High Accuracy ◽

Hidden Layer

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Robust Adaptive Control of PEMFC Air Supply System Based on Radical Basis Function Neural Network

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4042674 ◽

2019 ◽

Vol 141 (6) ◽

Cited By ~ 2

Author(s):

Yun-Long Wang ◽

Yong-Fu Wang ◽

Hua-Kai Zhang

Keyword(s):

Neural Network ◽

Adaptive Learning ◽

Basis Function ◽

Tracking Error ◽

Approximation Error ◽

Supply System ◽

Robust Adaptive Control ◽

Lyapunov Theory ◽

Air Supply ◽

Robust Adaptive

This technical brief emphasizes on the control of polymer electrolyte membrane fuel cell (PEMFC) air supply system. The control objective is to improve the net power output through adjusting the oxygen excess ratio within a reasonable range. In view of the problem that the PEMFC air supply system is difficult to achieve accurate modeling and stable control, a robust adaptive controller is proposed by utilizing exact linearization and radical basis function (RBF) neural network (RBFNN) system. This controller does not need the complete structure and parameters of PEMFC system. The unmodeled dynamics of PEMFC system can be approximated by RBFNN in which the adaptive learning law can be derived based on Lyapunov theory, and the external disturbance as well as the approximation error of RBFNN can be attenuated through robust control. The stability analysis shows that the system tracking error is uniformly ultimately bounded. Finally, the effectiveness and feasibility of controller are validated by hardware-in-loop (HIL) experiment.

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