Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators

Authors:
Fuchun Sun;Zengqi Sun;Lei Li;Han-Xiong Li
Affiliations:
Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Beijing and Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, ...;Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Beijing 100084, People's Republic of China;School of Public Policy and Management of Tsinghua University, Institute of Software, Chinese Academy of Science, 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
Venue:
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Year:
2003

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Abstract

This paper presents a stable neuro-fuzzy (NF) adaptive control approach for the trajectory tracking of the robotic manipulator with poorly known dynamics. Firstly, the fuzzy dynamic model of the manipulator is established using the Takagi-Sugeno (T-S) fuzzy framework with both structure and parameters identified through input/output data from the robot control process. Secondly, based on the derived fuzzy dynamics of the robotic manipulator, the dynamic NF adaptive controller is developed to improve the system performance by adaptively modifying the fuzzy model parameters on-line. The dynamic NF system aims to approximate the whole robot dynamics rather than its nonlinear components as is done by static neural networks. The dynamic inversion introduced for the controller design is constructed by the dynamic NF system and will help the NF controller design because it does not require the assumption that the robot states should be within a compact set. It is generally known that the compact set cannot be specified before the control loop is closed. Thirdly, the system stability and the convergence of tracking errors are guaranteed by Lyapunov stability theory, and the learning algorithm for the dynamic NF system is obtained thereby. Finally, simulation studies are carried out to show the viability and effectiveness of the proposed control approach.