A Discrete-Time Neural Network Control Method Based on Deterministic Learning for Upper-Limb Rehabilitation Robot

• Published in: IEEE transactions on automation science and engineering Vol. 22; pp. 9753 - 9766
• Main Authors: Zhang, Na, Zhang, Fukai, Li, Yibin, Wang, Cong, Li, Ke
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2025
• Subjects: Accuracy; Adaptive neural network control; Adaptive systems; Artificial neural networks; Assistive robots; deterministic learning; discrete-time dynamics; Robots; System dynamics; Training; Trajectory; Trajectory tracking; Uncertainty; upper-limb rehabilitation robot
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2024.3511996

Accurate trajectory training is a challenging issue of upper-limb rehabilitation robots. This paper presents a novel discrete-time neural network control method to address the problems of system uncertainties and tracking accuracy in repetitive trajectory training. This control method consists of both an adaptive neural network controller and a learning controller. The adaptive neural network controller satisfying persistent excitation condition enables not only stable tracking control, but also accurate learning for closed-loop system dynamics. The learning controller utilizes the learned knowledge to provide high-performance control. In order to examine the effectiveness of the proposed control method, a series of simulation and real-world experiments with system uncertainties were conducted, in comparison of proportion integration differentiation control, sliding mode control and event-triggered adaptive neural control. Results substantiate that the proposed control method can precisely learn the unknown dynamics of human-robot system along the subject-specific reference trajectories, and control the robot to assist the arm for accurate and fast trajectory tracking with small control gains by reutilizing the learned knowledge. This control method may play a role in accurate trajectory training for upper-limb rehabilitation robots. Note to Practitioners-This work is motivated by the practical requirements of rehabilitation robots in repetitive motor training. Trajectory tracking is a fundamental but efficient training mode of rehabilitation robots. However, uncertainty and nonlinearity of the human-robot system dynamics may increase the difficulty of controlling the robots for accurate, efficient and reliable trajectory tracking training. To this end, this paper proposes a learning-based control method, which could learn the uncertain and nonlinear system dynamics by utilizing an elaborately designed neural network controller and thus achieve superior control performance using the learned knowledge. This control method can be potentially applied in variety of rehabilitation robots, showing advantages for repetitive trajectory training. First, it can accurately mode the uncertain dynamics of human-robot system and achieve personalized rehabilitation. Second, it does not need any parameter adaptation in the similar repeated motions, and can be more easily designed with digital implementations, thereby achieving better performance in the aspects of time saving. Third, it can ensure the tracking accuracy of the rehabilitation robot for rehabilitation efficiency and avoid secondary injury.