Output Constrained Control of Lower Limb Exoskeleton Based on Knee Motion Probabilistic Model With Finite-Time Extended State Observer
Zhenlei Chen, Qing Guo, Tieshan Li, Yao Yan
Abstract
Due to randomness and uncertainty of individual walking gait, this study uses the sparse Gaussian process (SGP) to construct a knee motion probabilistic model related to its corresponding hip motion. Since the SGP can quantify output uncertainty, a time-varying constrained boundary of knee position is presented to guarantee the operator's safety and comfort in human-exoskeleton cooperative motion. First, a low-cost gait acquisition system (called CJD-1) composed of inertial measurement unit is constructed to collect the operator's gait information. Then, a virtual interaction torque field and an output-dependent universal barrier function are designed to realize the coordinated transformation of the time-varying constrained boundary generated by the SGP. Hence, the exoskeleton joint position is constrained by the desired output constrained boundary, which is complied with the operator's gait law. Then, a backstepping controller with a finite-time extended state observer is proposed to estimate and compensate unmeasured joint velocity and lumped uncertainty. The effectiveness of the proposed control scheme is verified by both simulations and experimental results of human-exoskeleton cooperative motion.