Evolving control of human-exoskeleton system using Gaussian process with local model

Abstract Significant application breakthrough has not been achieved in human-exoskeleton systems due to the lack of transparent human-exoskeleton interaction. An evolving learning control strategy based on Gaussian process with local model (GPLM) is proposed to realize transparent control of a human-exoskeleton system. As a data driven technique, Gaussian process (GP) serves as a mathematical foundation to learn the dynamics of human lower limbs. Local model is incorporated into the GP framework to improve the prediction results and reduce the computation cost. The GPLM integrates the white-box model and block-box strategy and make use of their benefits. A knowledge base is developed to achieve evolving of the system structure and hyper-parameters. The proposed method can figure out the unspecific kinematics and dynamics of human-exoskeleton systems and cope with the emerging dynamics in new operation regions by system evolving. Surface electromyography (sEMG) signals are processed by a novel method, and are treated as one of the inputs of the model. The proposed sEMG signals processing method is expected to grasp the human muscle activation levels and improve the computational efficiency which can meet the requirements of real-time control. Experimental results show that the interactive force, human effort and interaction damping are reduced greatly compared with traditional force control. The proposed algorithm makes huge improvements on the transparent human-exoskeleton interaction by system evolving, while ensuring the safety of users by risk-based control.