Adaptive Robust Constraint-Following Control for Lower Limbs Rehabilitation Robot

This paper proposes a new control method from the view of constraint-following for lower limbs rehabilitation robot (LLRR) working under passive training mode. The uncertainties existing in LLRR system are also considered. The uncertainties, including modeling error, initial condition deviation, muscle spasm, friction force and other external disturbances, are (possibly) time-varying. They are unknown but bounded. However, the bounds are also unknown. At first, the unilateral man-machine dynamical model with uncertain parameter of the LLRR and a pre-specified trajectory constraints are given. Based on the model and the constraints, a nominal control can be given by Udwadia and Kalaba theory with no uncertainties. Then, considering the uncertainties existing in the LLRR system, we propose a class of adaptive robust control to compensate the uncertainties. Finally, the effectiveness of the control is shown by numerical simulation.