Design PID Neural Network Controller for Trajectory Tracking of Differential Drive Mobile Robot Based on PSO

Mohamed Mohamed,Mohammed Hamza

doi:10.30684/etj.37.12a.12

Abstract

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.

Highlights

There are many applications need to use mobile robots in order to facilitate doing some jobs
In [11] a new control algorithm using a PID neural network (NN) controller is introduced, this algorithm is a type of MultiLayer Perceptron (MLP) but the hidden layers of the network are different from traditional layers
In order to verify the ability of the PID NN controller to force the mobile robot tracking any path, two paths were taken as test in order to check

Summary

Introduction

There are many applications need to use mobile robots in order to facilitate doing some jobs. One of the disadvantages of traditional neural network is time consuming in learning because of the random initialization weights of the network connections. This property leads the algorithm to unable achieve fast response of the control system and does not give a stationary performance. The rest of this paper is arranged as follows: in section two, the kinematic model equations of the differential wheeled nonholonomic mobile robot are derived; in section three, the proposed structure of the PID neural network controller is depicted as well as the main steps of applying the proposed PSO are explained, in section four the simulations and the

Methods

Results

Conclusion