A radial basis function networks approach for the tracking problem of mobile robots

Abstract

Proposes a radial basis function network (RBFN) approach to the solution of the tracking problem for mobile robots. RBFN-based controllers are investigated in order to introduce some degree of robustness in the control system and to avoid the main disadvantage of multilayer neural networks (MNN) to be highly nonlinear in the parameters. 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 and are compared with MNN-based control schemes. The experimental results are satisfactory in terms of tracking errors and computational efforts.