Control and Systems Eng. Dept. at University of Technology


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 during
the continuous path-tracking with collision-free navigation through static obstacles. The
proposed cognitive system consists of two parts: the first part is to plan the desired path for the
mobile robot with the static obstacle environment in order to determine the target point and to
avoid the obstacles based on the proposed square road map algorithm. The second part is to
guide and track the wheeled mobile robot on the desired path equation based on the proposed
nonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle Swarm
Optimization (PSO) is used to on-line tune the variable control parameters of the proposed
controller to get the optimal torques actions for the mobile robot platform. Based on using the
MATLAB package (2017), the numerical simulation results show that the proposed cognitive
system has high accuracy for planning the desired path equation in terms of avoiding the static
obstacles 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 for
the mobile robot to the zero value approximation. These results were confirmed by a
comparative study with different nonlinear PID controller types in terms of number of
iterations and the performance index.