Prescribed performance adaptive control for an uncertain robotic manipulator with input compensation updating law

• Published in: Journal of the Franklin Institute Vol. 358; no. 16; pp. 8396 - 8418
• Main Authors: Song, Zhankui, Sun, Kaibiao
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.10.2021; Elsevier Science Ltd
• Subjects: Adaptive control; Algorithms; Closed loop systems; Closed loops; Control theory; Convergence; Feedback control; Manipulators; Performance enhancement; Robot arms; Robotics; Robustness (mathematics); Strategy; Tracking control systems; Tracking errors; Trajectory planning
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2021.08.036

This paper investigates an adaptive prescribed performance control strategy with specific time planning for trajectory tracking of robotic manipulator subject to input constraint and external disturbances. By constructing an accumulated error vector embedded with a performance enhancement function and introducing an input auxiliary function, a specified-time control framework with built-in prescribed performance is further designed to ensure that the trajectory tracking performance. More particularly, the proposed control law is compatible with the control input saturation suppression algorithm, which is capable of improving the robustness of closed loop system. Under the framework of the proposed control strategy, it is proved by theory that all the signals in the closed-loop system are bounded, and moreover the tracking error can reach the exact convergence domain in a given time. At last, a numerical example is presented to indicate the feasibility and effectiveness of the proposed method.