Broad learning control of a two-link flexible manipulator with prescribed performance and actuator faults. Issue 5 (14th May 2023)

• Record Type: Journal Article
• Authors: Niu, Wenkai; Kong, Linghuan; Wu, Yifan; Huang, Haifeng; He, Wei
• Is Part Of: Robotica. Volume 41:Issue 5(2023)
• Page Start: 1371
• Page End: 1388
• Publication Date: 2023-05-14
• Subjects: broad learning -- flexible manipulator -- prescribed performance -- actuator faults -- neural networks; Robots -- Periodicals; 629.89205
• DOI: 10.1017/S026357472200176X
• Languages: English
• ISSNs: 0263-5747

Abstract

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.