Iterative learning control algorithm for optimal path industrial manipulator

Issue title: Meta-Heuristic Techniques for Solving Computational Engineering Problems: Challenges and New Research Directions

Guest editors: Suresh Chandra Satapathy, Rashmi Agrawal and Vicente García Díaz

Article type: Research Article

Authors: Lu, Zhengjie^{; *}

Affiliations: School of Mechanical and Electrical Engineering, Hechi University, Yizhou, Guangxi, China

Correspondence: [*] Corresponding author. Zhengjie Lu, E-mail: [email protected].

Abstract: In this paper, a two-degree-of-freedom manipulator is taken as the research object, and the relevant dynamic model is established, the iterative learning controller is designed, and the trajectory tracking control of the manipulator is carried out by using the iterative learning control algorithm. Iterative learning control (ILC) has a better control effect on a two-degree-of-freedom manipulator with repetitive motion characteristics for its non-linear system. In the case of disturbance, a PD-type iterative learning control law is designed. With the increasing number of iterations of the system, the required correction interval is shortened by modifying the gain matrix in real time in the interval, so as to accelerate the convergence speed. The simulation results show that the convergence speed of PD-type ILC is faster than that of P-type ILC, and the convergence effect of PD-type ILC with disturbance is better than that of traditional disturbance-type ILC. The industrial robot system is guaranteed to have good dynamic performance.

Keywords: Two-degree-of-freedom manipulator, PD learning law, iterative learning algorithm

DOI: 10.3233/JIFS-189427

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 5875-5882, 2021