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Broad learning control of a two-link flexible manipulator with prescribed performance and actuator faults

Published online by Cambridge University Press:  14 February 2023

Wenkai Niu ,
Linghuan Kong ,
Yifan Wu ,
Haifeng Huang  and
Wei He Open the ORCID record for Wei He [Opens in a new window]
Wenkai Niu
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Linghuan Kong
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Yifan Wu
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Haifeng Huang
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Wei He*
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
*
*Corresponding author. E-mail: weihe@ieee.org
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Abstract

In this paper, we present a broad learning control method for a two-link flexible manipulator with prescribed performance (PP) and actuator faults. The trajectory tracking errors are processed through two consecutive error transformations to achieve the constraints in terms of the overshoot, transient error, and steady-state error. And the barrier Lyapunov function is employed to implement constraints on the transition state variable. Then, the improved radial basis function neural networks combined with broad learning theory are constructed to approximate the unknown model dynamics of flexible robotic manipulator. The proposed fault-tolerant PP control cannot only ensure tracking errors converge into a small region near zero within the preset finite time but also address the problem caused by actuator faults. All the closed-loop error signals are uniformly ultimately bounded via the Lyapunov stability theory. Finally, the feasibility of the proposed control is verified by the simulation results.

Keywords

broad learningflexible manipulatorprescribed performanceactuator faultsneural networks
Type
Research Article
Information
Robotica , Volume 41 , Issue 5 , May 2023 , pp. 1371 - 1388
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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