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Recurrent Neural Network-Based Adaptive Sliding Mode Control for Trajectory Tracking of Second-Order Underactuated Systems

In this paper, the trajectory tracking control of second-order underactuated system with unmodeled dynamics and external disturbance is discussed. Adaptive neural network-based hierarchical sliding mode control is investigated to solve the problem. By introducing a novel recurrent neural network (RNN), which is used to compensate the unknown system model and disturbance, the robustness of the control systems is obviously improved. The stability of the control system is rigorously proved by the Lyapunov method and the adaptive laws is obtained. Through the stability analysis, the control scheme proposed in this paper can be applied in different second-order underactuated systems. Finally, the comparative simulation results are given to prove the effectiveness and superiority of the control scheme proposed in this paper.