Abstract – This paper proposes a cognitive neural controller to guide a nonholonomic mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network topology that controls the mobile robot actuators in order to track a desired path based on back-stepping technique and posture identifier. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimization (PSO) technique. The stability and convergence of control system are proved by using the Lyapunov criterion. Simulation results and experimental work show the effectiveness of the proposed cognitive neural control algorithm; this is demonstrated by minimizing tracking error and obtaining the smooth torque control signal, especially when the robot navigates through static obstacles with collision-free and the external disturbances applied.