An Active Control Method for a Lower Limb Rehabilitation Robot with Human Motion Intention Recognition

作     者：Song, Zhuangqun Zhao, Peng Wu, Xueji Yang, Rong Gao, Xueshan 

作者机构：Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou 535011 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Minist Civil Affairs Key Lab Intelligent Control & Rehabil Technol Beijing 100176 Peoples R China 

出 版 物：《SENSORS》 (Sensors)

年 卷 期：2025年第25卷第3期

页      面：713-713页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Province Key R&D Program of Guangxi Key Laboratory of Intelligent Control and Rehabilitation Technology of the Ministry of Civil Affairs AB22035006 

主　　题：human motion intention recognition follow-up lower extremity exoskeleton rehabilitation robot machine learning algorithm intelligent optimization algorithm dual radial basis function neural network adaptive sliding mode controller 

摘      要：This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle-machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle-machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional-integral-derivative (PID) control algorithm.