Brief Robot discrete adaptive control based on dynamic inversion using dynamical neural networks

Authors:
Fu-Chun Sun;Han-Xiong Li;Lei Li
Affiliations:
Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, People's Republic of China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, People's Republic of China;Institute of Software, Chinese Academy of Sciences, Beijing 100080, People's Republic of China
Venue:
Automatica (Journal of IFAC)
Year:
2002

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Abstract

A stable discrete time adaptive control approach using dynamic neural networks (DNNs) is developed in this paper for the trajectory tracking of a robotic manipulator with unknown nonlinear dynamics. By using dynamic inversion constructed by a DNN, the assumption under which the system state should be on a compact set can be removed. This assumption is usually required in neuro-adaptive control. The NN-based variable structure control is designed to guarantee the stability and improve the dynamic performance of the closed-loop system. The proposed control scheme ensures the global stability and desired tracking as well.