Tracking control via switching and learning for a class of uncertain flexible joint robots with variable stiffness actuators q

This paper is devoted to the tracking control of a class of uncertain flexible joint robots with variable stiffness actuators (VSAs). Remarkably, disturbances exist in both the link motor and the stiffness actuator but are regardless of or severely restricted in the related literature which result into the incapability of the existing control design schemes. For this, learning mechanism combined with switching method is introduced to compensate the serious uncertainties contained in the disturbances, and in turn to give a novel control design framework for the control problem under investigation. First, by using backstepping method and the disturbance learning mechanism, a state-feedback controller is explicitly constructed in which certain key controller parameters to be updated online are included. Then, a pivotal switching mechanism is designed to online tune the parameters in the controller. It is shown that the switching occurs only a finite number of times, and then the designed controller guarantees that all the states of the resulting closed-loop system are bounded while the system outputs asymptotically converge to the given reference signals, respectively. Finally, simulation is provided to validate the effectiveness of the proposed theoretical results. (c) 2021 Elsevier B.V. All rights reserved.