Design PID Neural Network Controller for Trajectory Tracking of Differential Drive Mobile Robot Based on PSO
This paper introduces a nonlinear (Proportional-Integral-Derivative Neural Network) (PID NN) controller for a differential wheeled mobile robot trajectory tracking problem. This neural controller is built based on the principles of neural network (NN) and the equation of conventional structure of PID controller and is applied on kinematic model of the mobile robot. The particle swarm optimization algorithm (PSO) is utilized to find the best values of three PID NN parameters and connection weights that minimize the error between the reference path and the actual path. The results illustrate that the PID NN controller has a satisfied ability to make the mobile robot tracking any path with good performance, high accuracy and acceptable robustness.
How to Cite
The author assigns to Engineering and Technology Journal with full title guarantee, all copyrights, rights in the nature of copyright, and all other intellectual property rights in the article throughout the world (present and future, and including all renewals, extensions, revivals, restorations and accrued rights of action). The Author represents that he/she is the author and proprietor of this Article and that this Article has not heretofore been published in any form. The Author warrants that he/she has obtained written permission and paid all fees for use of any literary or illustration material for which rights are held by others. The author agrees to hold the editor(s)/publisher harmless against any suit, demand, claim or recovery, finally sustained, by reason of any violation of proprietary right or copyright, or any unlawful matter contained in the submitted article.