Barrier function-based prescribed performance trajectory tracking control of wheelchair upper-limb exoskeleton robot under actuator fault and external disturbance: Experimental verification.

Journal: ISA transactions
PMID:

Abstract

This paper presents an innovative control strategy for the trajectory tracking of wheelchair upper-limb exoskeleton robots, integrating sliding mode control with a barrier function-based prescribed performance approach to handle actuator faults and external disturbances. The dynamic model of the exoskeleton robot is first extended to account for these uncertainties. The control design is then divided into two phases. In the first phase, the sliding mode control technique is applied to ensure robust trajectory tracking by defining the tracking error between the robot's states and desired trajectories. A sliding surface is constructed based on this error, and to further enhance tracking performance, a prescribed performance control scheme is incorporated, which ensures fast error convergence and improves transient behavior. In the second phase, an advanced barrier function technique is introduced to mitigate the impact of actuator faults and disturbances, enhancing the overall robustness of the system. Stability and tracking accuracy are rigorously verified through Lyapunov theory, ensuring the system's resilience to uncertainties. The combined approach not only guarantees rapid error convergence but also prevents performance degradation due to excessive control action, maintaining system stability. Finally, the effectiveness of the proposed method is demonstrated through extensive simulations and hardware-in-loop experiments, highlighting its practical applicability for real-world exoskeleton systems.

Authors

  • Huan-Chung Li
    Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 11031, Taiwan. Electronic address: d610105003@tmu.edu.tw.
  • Omid Mofid
    Department of Computer Science, University of Tulsa, Tulsa, Oklahoma, USA. Electronic address: omm7090@utulsa.edu.
  • Saleh Mobayen
    Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou 640301, Yunlin, Taiwan. Electronic address: mobayens@yuntech.edu.tw.
  • Khalid A Alattas
    Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia. Electronic address: kaalattas@uj.edu.sa.
  • Telung Pan
    Department of Information Management, College of Management, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan. Electronic address: telung@yuntech.edu.tw.
  • Hung-Wen Chiu
    Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei City, Taiwan. Electronic address: hwchiu@tmu.edu.tw.