Robust Repetitive Learning-Based Trajectory Tracking Control for a Leg Exoskeleton Driven by Hybrid Hydraulic System

For the purpose of reducing power consumption of a leg exoskeleton for augmenting human performance, a novel hybrid hydraulic system (HHS) which includes a unidirectional servo valve and a solenoid on-off valve is excogitated, and its energy saving control is studied in this paper. Inspired by the varieties of contact force between human leg and ground during walking, the unidirectional servo valve and the solenoid on-off valve are only activated in the stance phase and the swing phase, respectively. In the stance phase, a robust repetitive learning scheme is presented by using the backstepping technique for the unidirectional servo valve, aiming to track the periodic human leg movement, and in the swing phase, an on-off control is proposed for the solenoid valve to release the pressure in the hydraulic cylinder so that the exoskeleton leg is bent by the human leg passively. The proposed control strategy is implemented in an ARM-based embedded microprocessor and the control performance is verified via experiment on the developed exoskeleton robot. The experimental results show that the power consumption of the proposed system is almost 30% less than that of systems with bidirectional hydraulic system.