摘要：AbstractIn this paper, a robust iterative learning switching controller that uses optimal virtual constraint is designed for a hybrid walking exoskeleton that uses functional electrical stimulation and a powered exoskeleton. The synthesis of iterative learning control with sliding-mode control improves tracking performance and accuracy. The motivation for designing this switching controller was to obtain joint torques either from functional electrical stimulation or electric motor. A generalized switching controller is utilized to switch based on the stimulated muscle fatigue state. For achieving stability in walking cycle, the controller is used to force the system to follow the designed virtual constraints. The combination of sequential quadratic programming and genetic-particle swarm optimization algorithm is used for deriving the virtual constraints. The effectiveness of the new iterative learning control for output tracking is verified in a simple model of walking (3-link) that has active actuation at the hip joints.
