An artificial neural network based dynamic controller for a robot in a multi-agent system

This paper proposes the modelling and simulation of an artificial neural network based computed torque controller for the trajectory planning of a robot in a multi-agent robot soccer system. The controller is designed for the tracking of the soccer robot along a dynamic Bezier path. Dynamics of the robot is formulated directly in terms of the motor torque which is more realistic than the conventional methods that are based on heading velocities. The primary feedback loop of the proposed scheme includes a kinematic controller, which generates an appropriate heading velocity command. A dynamic controller included in the inner feedforward path generates a control torque for minimizing the tracking velocity error. Another artificial neural network controller, included in a separate feedforward path, compensates for the deficiencies in the control torque due to the unknown robot dynamics. The on-line learning rules are formulated by ensuring the stability of the entire system using the Lyapunov criterion.