Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation
This work introduces a control framework for a soft lower-limb rehabilitation robot using a Deep Koopman Network. It models human-robot interaction dynamics by incorporating joint angles, PWM inputs, and muscle EMG signals. The resulting Model Predictive Control (KMPC) strategy effectively tracks desired movements, showing improved accuracy and performance over traditional methods.