Engineering and Technology Journal

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.

This paper presents a trajectory tracking control algorithm for a non-holonomic wheeled mobile robot using optimization technique based nonlinear PID neural controller in order to follow a pre-defined a continuous path. As simple and fast tuning algorithms, particle swarm optimization algorithm is used to tune the nonlinear PID neural controller's parameters to find best velocity control actions for the mobile robot. Simulation results show the effectiveness of the proposed nonlinear PID control algorithm; this is demonstrated by the minimized tracking error and the smoothness of the velocity control signal obtained, especially with regards to the external disturbance attenuation problem.