An intelligent sliding mode controller of robotic manipulators with output constraints and high‐level adaptation

Abstract

Abstract Fast transient responses and excellent steady‐state control performances with flexibility in control operation are core motivations to promote a vast of research in the robotic control sector nowadays. As a sequence, in this paper, we present a learning nonlinear controller for motion control of robotic manipulators with output constraints. The constrained control objectives are first transformed to new free variables using nonlinear synthetization profiles. Boundedness of the main control objectives within feasible ranges of the physical outputs is next strictly guaranteed under a simple sliding‐mode control policy. To improve the control performance, dominant terms of the system dynamics are learnt by a neural network with a nonlinear adaptation law. The systematic deviation remaining is then estimated by an adaptive disturbance observer. A novel high‐level adaptation rule is designed to maximize the learning ability and the control quality. Both an asymptotic control performance and estimation effectiveness are theoretically thoroughly proven by Lyapunov‐based analyses. Feasibility of the overall system is carefully investigated by intensive comparative simulations.