Adaptive Neural Compensator for Robotic Systems Control

In the area of robotics systems, there are numerous applications where robots are expected to move rapidly from one place to another, or follow desired trajectories while maintaining good dynamic behavior. However, certain non-linearities, uncertainties in dynamics and external perturbations make the design of ideal controllers a complicated task in many situations. In this paper, we propose a control scheme that combines a nominal feedback controller with a classical PD and a robust adaptive compensator based on artificial neural networks. Using this control scheme, it is possible to obtain a fully tuned compensation parameters and a strong robustness with respect to uncertain dynamics and different non-linearities, as well as to keep the output tracking error bounded to values close to zero. In order to show the performance of the proposed technique, a SCARA (Selective Compliant Articulated Robot Arm) type robot with two degrees of freedom is considered in this case; but this control proposal can be applied to different systems with dynamic variations. The efficiency and performance of the control law is demonstrated through simulation results and the stability analysis is carried out using Lyapunov's theory.