Disturbance observer-based control for cable-driven parallel robots with external disturbance using deep reinforcement learning

被引:0
作者
Lu, Yanqi [1 ]
Yao, Weiran [1 ]
Wu, Chengwei [1 ]
Li, Xiaolei [1 ]
Sun, Guanghui [1 ]
机构
[1] Harbin Inst Technol, Dept Control Sci & Engn, Harbin, Peoples R China
关键词
cable-driven parallel robots; disturbance estimation; reinforcement learning; TRAJECTORY TRACKING CONTROL; TIME; SPACECRAFT; STABILITY; DESIGN; STATE;
D O I
10.1002/asjc.3353
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In cable-driven parallel robots (CDPRs) practical applications, external disturbances inevitably influence the actuator performance, leading to low positioning accuracy of end-effector (EE), and even system crashes. This paper addresses a disturbance observer-based control scheme using deep reinforcement learning (RL) to suppress the effect of external disturbances. A controller via the non-singular terminal sliding mode (NTSM) is proposed to enhance the system robustness. The estimation of the disturbance is investigated, and the deep RL is employed to enhance the model-based disturbance observer, namely, the RLDO, and a corresponding controller via NTSM (RLDO-NTSMC) is proposed. Simulations and experimental results validate that the RLDO effectively improves the estimation accuracy, and the RLDO-NTSMC significantly increases the control accuracy of CDPRs. The root mean square errors of the EE's positioning errors controlled by the RLDO-NTSMC decreased by over 93% compared with the classical augmented proportional-derivative (APD) controller.
引用
收藏
页码:2523 / 2538
页数:16
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