Neural Networks Based Control of Mobile Robots: Development and Experimental Validation

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Abstract
The paper proposes a neural networks approach to the solution of the tracking problem for mobile robots. Neural networks based controllers are investigated in order to exploit the nonlinear approximation capabilities of the nets for modeling the kinematic behavior of the vehicle and for reducing unmodeled tracking errors contributions. 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 classical kinematic control schemes. Experimental results are satisfactory in terms of tracking errors and computational efforts.