This study presents a multimodal human-exoskeleton cooperative control method to realize different control modes smoothly switching each other with satisfactory stable performance. Considering existed mismatch gaits of the operator comparison with the exoskeleton, the corresponding operator's gait is planned by central pattern generators (CPGs) to reduce human-exoskeleton impedance and generate real-time desired trajectory, which are used as the trajectory demand input of the exoskeleton control. Then, the admittance modulation factors is proposed to realize three motion control modes of lower limb exoskeleton, i.e., active, passive, and assist-as-needed. Meanwhile, an adaptive backstepping controller with the radial basis function neyral network estimation law is designed to guarantee the position tracking errors in uniformly ultimately boundedness under model uncertainty. Finally, the experimental studies are performed with an able-bodied operator by regulating the CPGs model parameters and modulation factors to verify the proposed multimodal human-exoskeleton cooperative control.

This study presents an observer-based adaptive fuzzy dynamic surface force control strategy for pneumatic polishing system with uncertain contact environment model and full-state constraints. First, a fuzzy logic system is employed to approximate the uncertain nonlinear dynamics of the pneumatic polishing system end-effector, and a fuzzy state observer is designed to estimate the unmeasured states. Further, to enhance the controller’s response speed, a variable separation method employing fuzzy basis functions and the dynamic surface control technique are adopted to address algebraic loop issues and prevent computational complexity explosion. A logarithmic-type barrier Lyapunov function is embedded into each step of the controller design to ensure that all system states satisfy the predefined constraints. Finally, an adaptive fuzzy dynamic surface force controller with full-state constraints is developed to ensure fast and accurate control of the polishing force in an uncertain contact environment while maintaining all states within predefined constraints. The effectiveness and applicability of the proposed control algorithm are experimentally verified using a pneumatic polishing system.