Adaptive Path Tracking Mobile Robot Controller Based on Neural Networks and Novel Grass Root Optimization Algorithm

Abstract

This paper proposes a novel metaheuristic optimization algorithm and suggests an adaptive artificial neural network controller that based on the proposed optimization algorithm. The purpose of the neural controller is to track desired proposed velocities and path trajectory with the minimum error, in the presence of mobile robot parameters time variation and system model uncertainties. The proposed controller consists of two sub-neural controllers; the kinematic neural feedback controller, and the dynamic neural feedback controller. The external feedback kinematic neural controller was responsible of generating the velocity tracking signals that track the mobile robot linear and angular velocities depending on the robot posture error, and the desired velocities. On the other hand, the internal dynamic neural controller has been used to enhance the mobile robot against parameters uncertainty, parameters time variation, and disturbance noise. However, the proposed grass root population-based metaheuristic optimization algorithm has been used to optimize the weights of the neural network to have the behavior of an adaptive nonlinear trajectory tracking controller of a differential drive wheeled mobile robot. The proposed controller shows a very good ability to prepare an appropriate dynamic control left and right torque signals to drive various mobile robot platforms using the same offline optimized weights. Grass root optimization algorithms have been used due to their unique characteristics especially, theirs derivative free, ability to optimize discretely and continuous nonlinear functions, and ability to escape of local minimum solutions.