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A variable magnification ratio transmission structure powered by the electric actuators is proposed to improve the flexibility and portability of the exoskeleton under heavy load carrying condition. The parameters of connecting rod size and hanging position are optimized to ensure that the output torque of active joints can fully envelope the demand load area. The control strategy based on intrinsic sensing is designed to realize the automatic human motion intention prediction and flexible trajectory tracking. The newly developed split embedded connecting rod can accurately measure the human–robot interaction (HRI) force applied to the exoskeleton and extract the human motion intention without being affected by the differences in wearing status. The force tracking control based on the zero-force following is modified by feedforward compensation with extreme learning machine (ELM), which enhances the response speed to human motion intention and reduces the HRI force by 70.6%. Based on multi-sensor information, stacked autoencoder deep neural networks (DNNs) are utilized to realize the automatic locomotion transition and the corresponding control parameters’ switching. After optimization by a hybrid algorithm of genetic algorithm and particle swarm optimization (GA_PSO), the identification accuracy is enhanced from 96.2% to 99.7%. The adaptive neural-fuzzy inference system (ANFIS) is used to analyze the plantar pressure to achieve flexible switching between the swing phase and the stance phase. The experiments under various gait motion trajectories assisted by novel weight-bearing exoskeleton are carried out for evaluation, and the performance of the proposed control strategy based on motion intention prediction, locomotion mode identification, and gait phase switching is effectively verified.

A Novel Weight-Bearing Lower Limb Exoskeleton Based on Motion Intention Prediction and Locomotion State Identification