Two-Layer Neuro-Adaptive Compensation Control Applied to a 4-Wheeled Omnidirectional Mobile Robot
Keywords:
Neuro-adaptive control, Tracking control, Online weight update, Omnidirectional mobile robot, Mecanum wheelsAbstract
Thanks to recent advances in artificial intelligence, interest in autonomous mobile systems has increased, and consequently, the development and validation of advanced control schemes for them has also seen a rise. This work introduces a two-layer neuro-adaptive compensation control scheme designed to address the trajectory tracking problem for an omnidirectional wheeled mobile robot equipped with four independent Mecanum wheels. The two-layer artificial neural network is used to compensate for the unknown dynamics of the mobile robot; the filtered error technique is used to obtain the weights of the artificial neural network. This approach does not require offline training. A key contribution of this approach is the integration of a novel auxiliary signal to provide robustness, particularly in non-ideal scenarios. This robust term effectively bounds the disturbance commonly encountered in such control approaches. A significant advantage of this approach is its independence from precise knowledge of plant parameters or the overall plant dynamics. Experimental results demonstrate the effectiveness of the proposed controller in achieving desired performance for the 4-wheeled omnidirectional mobile robot.
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