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

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.

In-Depth Review of Advanced Control Strategies and Cutting-Edge Trends in Robot Manipulators: Analyzing the Latest Developments and Techniques

“…It is noted that the robot can be operated without the skill level of a human controller, making it an effective tool for minimally invasive surgery. Niu et al [106] suggested a broad learning control method for a two-link flexible manipulator with prescribed performance (PP) and actuator faults using a neural network-based adaptive dynamic programming algorithm. The broad learning theory combined with improved RBFNN was constructed in order to mimic the dynamics of flexible robotic manipulators' uncertain model.…”