Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot

An adaptive controller of nonlinear PID-based analog neural networks is developed for the velocity- and orientation-tracking control of a nonholonomic mobile robot. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. It is appropriate for a kind of plant with nonlinearity uncertainties and disturbances. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity- and orientation-tracking control of the nonholonomic mobile robot. The effectiveness of the proposed control algorithm is demonstrated through the simulation experiment, which shows its superior performance and disturbance rejection. (C) 2007 Elsevier B.V. All rights reserved.