A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control

The paper proposes a multiple models based control methodology 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 (RBFNs) are considered for the multiple models in order to exploit the non-linear approximation capabilities of the nets for modeling the kinematic behaviour of the vehicle and for reducing unmodelled tracking errors contributions. The training of the nets and the control performance analysis have been done in a real experimental setup. 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.