Abstract
This paper presents the application of adaptive neural networks to robot manipulator control. The main methodologies, on which the approach is based, are recurrent neural networks and Lyapunov functions methodology and Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID approach. The proposed new control scheme is applied via simulations to control a robot manipulator two-link model. Experimental results in two degrees of freedom of the robot arm shown the usefulness of the proposed approach. To verify the analytical results, an example of dynamical network is simulated and a theorem is proposed to ensure the tracking of the nonlinear system.
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