A self-tuning trajectory tracking controller for wheeled mobile robots

Purpose: Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach: In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings: Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value: In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.