Adaptation and Learning in Neural Networks Multiple Models Based Control of Mobile Robots

Abstract

The paper proposes a multiple models based control scheme for the solution of the tracking problem for mobile robots. The proposed method utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFN) are considered for the multiple models. The training of the nets and the control performances analysis have been done in a real experimental setup. The proposed solutions are implemented on a PC-based control architecture for the real-time control of the LabMate mobile base. The experimental results are satisfactory in terms of tracking errors and computational efforts and show the improvement in the tracking performance when the proposed methodology is used for tracking tasks in dynamical uncertain environments.