A New Adaptive Robust Control Scheme for Trajectory Tracking of Robot Manipulators With Uncertain Dynamics Model

Abstract

This paper introduces a semi-model-free adaptive backstepping dynamical sliding mode control scheme for trajectory tracking of robot manipulators subject to uncertain dynamics. The proposed methodology synthesizes backstepping control and dynamical sliding mode control paradigms through Lyapunov stability theory to derive an innovative dynamic control law coupled with an adaptation mechanism. A key advantage of this approach is its dependence solely on the nominal inertia matrix, thereby circumventing the requirement for a comprehensive dynamic model. In contrast to conventional model-based adaptation laws, which depend on precise knowledge of system dynamics, and model-free approaches that often rely on the restrictive assumption of zero time-derivative for uncertain terms, the proposed adaptive law bypasses both limitations. Instead, this adaptive mechanism estimates the aggregate effects of uncertain dynamic components—encompassing centripetal and Coriolis forces, gravitational effects, external disturbances, and unmodelled dynamics—and incorporates these estimates within the dynamic control framework. Through rigorous stability analysis, we demonstrate that the integration of these control techniques ensures global uniform boundedness of both tracking and estimation error trajectories, thereby establishing robust convergence properties. The efficacy of the proposed control architecture is validated through comprehensive numerical simulations conducted on a 6-degree-of-freedom Universal Robots UR5 manipulator platform, implemented within both MATLAB and the Gazebo simulation environment interfaced with the robot operating system framework. Simulation results demonstrate the closed-loop system's superior performance in tracking predefined trajectories despite significant model uncertainties. An integrated motion planner further optimizes performance by reducing peak torque during goal-to-goal positioning tasks.