Robust Motion Tracking Controllers for Uncertain Robot Manipulators

Abstract

This paper considers the problem of designing a class of robust algorithms for the trajectory tracking control of an uncertain robot manipulator. The general control structure consists of two parts: the primary control law is first introduced to stabilize the nominal system and then a class of robust non-linear control laws are adopted to compensate for the system uncertainties by using the deterministic approach. The uncertainties assumed are bounded by higher-order polynomials in the Euclidean norms of system states with known (or unknown) bounding coefficients. The possible bounds of uncertainties are assumed to be known for the robust non-linear control with less computational burden. If no information on these bounds is available, then the adaptive bound of the robust controller is presented to overcome possible time-varying uncertainties, that is a decentralized adaptive control scheme. With a feasible class of desired trajectories, the proposed control laws guarantee that all possible responses of the corresponding closed-loop systems are at least uniformly ultimately bounded (UVB) by Lyapunov stability theory. The effectiveness of the proposed control algorithms are verified through numerical simulations. Finally, it is shown that the presented controllers are evaluated to be robust with respect to a given class of uncertainties.