ADAPTIVE CONTROL FOR ROBOTIC MANIPULATOR USING DISTURBANCE COMPENSATOR

Control Engineering

This paper proposes an adaptive control strategy for robotic manipulators subject to parametric and external uncertainties. A neural network is employed to approximate unknown nonlinear dynamics over a compact input space. An adaptive control law is developed to compensate for approximation errors and enhance robustness. The controller effectively handles both structured uncertainties, such as payload variation, and unstructured disturbances, including friction and external forces. Simulation results on a two-degree-of-freedom robotic manipulator, implemented in MATLAB, demonstrate the tracking performance and robustness of the proposed method.

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