A Cognitive System Design for Mobile Robot Based on an Intelligent Algorithm

Abstract

This paper presents a cognitive system based on a nonlinear Multi-Input Multi-Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network(MENN) controller and the Square Road Map (SRM) method to guide the mobile robot duringthe continuous path-tracking with collision-free navigation through static obstacles. Theproposed cognitive system consists of two parts: the first part is to plan the desired path for themobile robot with the static obstacle environment in order to determine the target point and toavoid the obstacles based on the proposed square road map algorithm. The second part is toguide and track the wheeled mobile robot on the desired path equation based on the proposednonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle SwarmOptimization (PSO) is used to on-line tune the variable control parameters of the proposedcontroller to get the optimal torques actions for the mobile robot platform. Based on using theMATLAB package (2017), the numerical simulation results show that the proposed cognitivesystem has high accuracy for planning the desired path equation in terms of avoiding the staticobstacles with smooth and short distance and generating a perfect torque action of (0.7 N.m)without a saturation state of (3.07 N.m), which leads to minimize the tracking pose error forthe mobile robot to the zero value approximation. These results were confirmed by acomparative study with different nonlinear PID controller types in terms of number ofiterations and the performance index.