Control of robots using radial basis function neural networks with dead-zone

摘要

In this paper, we examine the control of robot manipulators utilizing a Radial Basis Function (RBF) neural network. We are able to remove the typical requirement of Persistence of Excitation (PE) for the desired trajectory by introducing an error minimizing dead-zone in the learning dynamics of the neural network. The dead-zone freezes the evolution of the RBF weights when the performance error is within a bounded region about the origin. This guarantees that the weights do not go unbounded even if the PE condition is not imposed. Utilizing protection ellipsoids we derive conditions on the feedback gain matrices that guarantee that the origin of the closed loop system is semi-globally uniformly bounded. Simulations are provided illustrating the techniques.