Predictive Control and Experimental Study of Knee Ankle Rehabilitation Orthosis Based on Improved High-order Unscented Kalman Filters

doi:10.3901/JME.2025.11.057

Abstract

Abstract: To solve the problems of motion behavior estimation and trajectory tracking control for human-manchine collaborative motion of rehabilitation robots, a torque estimation and model predictive control algorithm for knee ankle rehabilitation orthotics is proposed. Firstly, a robot joint dynamics model based on Lagrange function and load equation is established, and a high-order unscented Kalman filter (UKF) algorithm is proposed to achieve joint torque estimation without relying on torque sensors. Secondly, the H_∞ filter is designed to reduce estimation errors caused by uncertain noise in the UKF algorithm and improves the robustness of the estimation algorithm. Then, the model predictive controller is improved by using the posterior estimation value of nonlinear filtering as the output feedback of the system, which improve the accuracy of robot torque tracking and converged the error range. Finally, a physical prototype is constructed for no-load experiments and human-machine collaborative motion experiments, verifying the accuracy of the torque estimation method and the effectiveness of the control algorithm.

Key words: rehabilitation robot, state estimation, nonlinear filtering, model predictive control

CLC Number:

TP242

LI Yuan, SUN Zhi, ZI Bin, CHEN Bing. Predictive Control and Experimental Study of Knee Ankle Rehabilitation Orthosis Based on Improved High-order Unscented Kalman Filters[J]. Journal of Mechanical Engineering, 2025, 61(11): 57-71.

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