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Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(6): 1478-1488
Yu Cao and Jian Huang, "Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1478-1488, Nov. 2020. doi: 10.1109/JAS.2020.1003351
Citation: Yu Cao and Jian Huang, "Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1478-1488, Nov. 2020. doi: 10.1109/JAS.2020.1003351
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Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton

doi: 10.1109/JAS.2020.1003351
Funds:  This work was supported in part by the National Natural Science Foundation of China (U1913207), the International Science and Technology Cooperation Program of China (2017YFE0128300), and the Fundamental Research Funds for the Central Universities (HUST: 2019kfyRCPY014)
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    Abstract
  • Pneumatic muscle actuators (PMAs) are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries, such as strokes, spinal cord injuries, etc., to accomplish rehabilitation tasks. However, because PMAs have nonlinearities, hysteresis, and uncertainties, etc., complex mechanisms are rarely involved in the study of PMA-driven robotic systems. In this paper, we use nonlinear model predictive control (NMPC) and an extension of the echo state network called an echo state Gaussian process (ESGP) to design a tracking controller for a PMA-driven lower limb exoskeleton. The dynamics of the system include the PMA actuation and mechanism of the leg orthoses; thus, the system is represented by two nonlinear uncertain subsystems. To facilitate the design of the controller, joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP. A gradient descent algorithm is employed to solve the optimization problem and generate the control signal. The stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system dynamics. Simulations and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

     

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    Highlights

    • This paper proposed a tracking controller for pneumatic muscle actuators driven exoskeleton.
    • Based on the neural network approximation model, this controller is a data-driven strategy.
    • The controller is proven to be asymptotically stable.
    • Experimental results indicate the effectiveness and robustness of this controller.

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