Neural networks-based sliding mode tracking control for the four wheel-legged robot under uncertain interaction

When considering the accuracy of tracking control, physical interaction such as structural uncertainties and external dynamics is the main challenge in actual engineering scenarios, especially for the complex robot system. In this article, a neural network-based sliding mode tracking control scheme (SMCR) is presented for the developed four wheel-legged robot (BIT-NAZA) under the uncertain interaction. First, a non-singular fast terminal function based on the kinematic model is proposed for path tracking, which improves the motion quality during the approach movement and the sliding mode movement. At the same time, it can reduce the influence of uncertain disturbances on the premise of ensuring the path tracking control accuracy via neural networks. Finally, some demonstrations using the autonomous platform of the BIT-NAZA robot are employed to evaluate the robustness and effectiveness of the hybrid algorithm.