Neural network based adaptive non linear PID controller for non-holonomic mobile robot

this paper presents an approach for velocity and orientation tracking control of a nonholonomic mobile robot based on an adaptive controller. The developed PID controller is based on analog neural networks. The feed forward neural networks controller is trained on-line to find the inverse kinematical model, which controls the outputs of the mobile robot system. The proposed controller has a better performance because of its capability of continuous online learning due to neural network. The simulation results for a differentially driven nonholonomic mobile robot are presented to establish the better performance of the proposed adaptive controller.