Adaptive Inverse Optimal Control for Rehabilitation Robot Systems Using Actor-Critic Algorithm

The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e.g., tracking trajectory) based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid control (AHC) combining inverse optimal control and actor-critic learning is proposed. Specifically, an uncertain nonlinear rehabilitation robot model is firstly developed that includes human motor behavior dynamics. Then, based on this model, an open-loop error system is formed; thereafter, an inverse optimal control input is designed to minimize the cost functional and a NN-based actor-critic feedforward signal is responsible for the nonlinear dynamic part contaminated by uncertainties. Finally, the AHC controller is proven (through a Lyapunov-based stability analysis) to yield a global uniformly ultimately bounded stability result, and the resulting cost functional is meaningful. Simulation and experiment on rehabilitation robot demonstrate the effectiveness of the proposed control scheme.