Novel neuromuscular controller application method to improve the balance recovery ability of lower limb exoskeletons |
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| Affiliation: | 1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, Beijing, China;2. Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, Zhejiang, China;3. School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China;4. Department of Geriatric Rehabilitation, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310052, Zhejiang, China;5. The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, China;1. College of Science, Hohai University, Nanjing 210098, PR China;2. School of Mathematics and Informational Science, Yantai University, Yantai 264005, PR China;3. School of Mathematics, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;5. School of Automation and Electrical Engineering, Linyi University, Linyi 276005, China;1. Department of Automation, Xiamen University, Xiamen, Fujian 361005, China;2. School of Systems Design and Intelligent Manufacturing, South University of Science and Technology, Shenzhen Guangdong 518000, China;1. College of Electrical Engineering and Automation, FuZhou University, Fuzhou 350108, China;2. Central South University, Changsha 410083, China;1. School of Artificial Intelligence, Chongqing University of Education, Chongqing 400065, China;2. College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China |
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| Abstract: | In recent years, several biological frameworks have been proposed to imitate human motion and control lower limb exoskeletons. Compared with position or impedance tracking of defined joint trajectories, this kind of strategy can reproduce human walking dynamics, and tolerate external disturbance. The output of this kind of strategy is anticipatory feedforward torque, which means that the controller needs to have sufficient prior knowledge on neurology. Moreover, lower limb exoskeletons are fundamentally different from humans, which weakens the ability of the controller to resist external internal interference. Thus, pure feedforward control hardly achieves a flexible movement like that of a human. In this study, we propose a feedback control framework based on repetitive learning control (RLC) to enhance the anti-interference capability of a neuromuscular controller. The controller consists of two parts: (1) A data-driven morphed nonlinear phase oscillator is used as a state observer to learn the changing law of an exoskeleton’s posture and center of mass and to construct a stable limit cycle in the state space. (2) A posture and centroid tracker based on RLC is utilized to track the output of oscillators and achieve a natural balance recovery process. Simulation and experimental results show that the integrated control system has a better control effect than the simple biological control method. |
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