Human–machine interaction controller of upper limb based on iterative learning method with zeroing neural algorithm and disturbance observer

• Published in: Engineering applications of artificial intelligence Vol. 122; p. 106108
• Main Authors: Chai, Yuanyuan, Liu, Keping, Duan, Xiaoqin, Yi, Jiang, Sun, Ruiling, Li, Jiacong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023
• Subjects: Disturbance observer; Human–machine interaction control; Iterative learning control; Upper limb; Zeroing neural algorithm; Disturbance observer; Upper limb; Zeroing neural algorithm; Human–machine interaction control; Iterative learning control
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.106108

In this paper, an iterative learning controller with zeroing neural algorithm and disturbance observer is proposed to solve the conflicting of human–machine interaction and disturbances in system. This paper presents a theoretical framework, which is able to process rigorous stability analysis of human–machine interaction control. The iterative learning controller is suitable for repetitive upper limb rehabilitation training. Combining tracking error dependent weight vector with zeroing neural algorithm is aimed to reduce the conflicts in various operations of motions between upper limb and machine. The disturbance observer is used to deal with eliminating the disturbance. In addition, simulations of the controller can effectively and safety assist upper limb movement in different training stages.