Optimal Predictive Impedance Control in the Presence of Uncertainty for a Lower Limb Rehabilitation Robot

As an innovative concept, an optimal predictive impedance controller (OPIC) is introduced here to control a lower limb rehabilitation robot (LLRR) in the presence of uncertainty. The desired impedance law is considered to propose a conventional model-based impedance controller for the LLRR. However, external disturbances, model imperfection, and parameters uncertainties reduce the performance of the controller in practice. In order to cope with these uncertainties, an optimal predictive compensator is introduced as a solution for a proposed convex optimization problem, which is performed on a forward finite-length horizon. As a result, the LLRR has the desired behavior even in an uncertain environment. The performance and efficiency of the proposed controller are verified by the simulation results.