A new control for the pneumatic muscle bionic legged robot based on neural network. Issue 4 (9th October 2022)

• Record Type: Journal Article
• Authors: Xu, Chaoyue; Qin, Feifei; Zhou, Kun; Wang, Binrui; Jin, Yinglian
• Other Names: Zhu Qiuguo guestEditor.; Song Rui guestEditor.; Wu Jun guestEditor.; Masaki Yamakita guestEditor.; Yu Zhangguo guestEditor.
• Is Part Of: IET cyber-systems and robotics. Volume 4:Issue 4(2022)
• Page Start: 339
• Page End: 355
• Publication Date: 2022-10-09
• Subjects: double closed‐loop control -- legged robot -- neural network -- pneumatic muscle; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/csy2.12065
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract: The bionic joints composed of pneumatic muscles (PMs) can simulate the motion of biological joints. However, the PMs themselves have non‐linear characteristics such as hysteresis and creep, which make it difficult to achieve high‐precision trajectory tracking control of PM‐driven robots. In order to effectively suppress the adverse effects of non‐linearity on control performance and improve the dynamic performance of PM‐driven legged robot, this study designs a double closed‐loop control structure based on neural network. First, according to the motion model of the bionic joint, a mapping model between PM contraction force and joint torque is proposed. Second, a control strategy is designed for the inner loop of PM contraction force and the outer loop of bionic joint angle. In the inner control loop, a feedforward neuron Proportional‐Integral‐Derivative controller is designed based on the PM three‐element model. In the control outer loop, a sliding mode robust controller with local model approximation is designed by using the radial basis function neural network approximation capability. Finally, it is verified by simulation and physical experiments that the designed control strategy is suitable for humanoid motion control of antagonistic PM joints, and it can satisfy the requirements of reliability, flexibility, and bionics during human–robot collaboration.