A Cognitive Nonlinear Trajectory Tracking Controller Design for Wheeled Mobile Robot based on Hybrid Bees-PSO Algorithm

Abstract

The aim of the work for this paper is a comparative study of different types of on-line cognitive algorithms for the proposed nonlinear controller of the trajectory tracking for dynamic wheeled mobile robot that has a capability to track a continuous desired path. Three optimization algorithms are used (Bees, PSO and proposed hybrid Bees-PSO) in order to find and tune the values of the control gains of the neural controller as simple on-line with fast tuning techniques. The best torques control actions of the right wheel and left wheel for the cart mobile robot are generated on-line from the proposed controller. Simulation results (Matlab Package) show that the proposed nonlinear neural controller with hybrid Bees-PSO cognitive algorithm is more accurate in terms of fast on-line finding and tuning parameters of the controller; obtaining smoothness control action as well as minimizing tracking error of the wheeled mobile robot than PSO or Bees optimization algorithms.