Adaptive Terminal Sliding Mode Control Method Based on RBF Neural Networkfor Operational AUV and Its Experimental Research

The trajectory tracking control problem of operational AUV (autonomous underwater vehicle) is addressed. In general, the stretching and operation processes of underwater manipulator will lead to changes of AUV dynamic performance, which will affect the AUV trajectory tracking control, and so does the water current. Aiming at the trajectory tracking control problem of AUV, an adaptive terminal sliding mode control method based on RBF (radial basis function) neural network is proposed. Under the framework of Lyapunov stability theory, the RBF neural network is used to approximate the changes of AUV dynamic performance caused by the stretching of the manipulator and the disturbance of the water current online. Then combined with the adaptive terminal sliding mode controller, the weights of neural network and control parameters of AUV are adaptively adjusted online. According to the Lyapunov stability theory, it is proved that the system trajectory tracking error of AUV is uniformly stable and bounded. Aiming at the chattering problem caused by the sliding mode control items, a chattering reduction method for the saturated continuous function with variable sliding mode gain is proposed to reduce the chattering of sliding mode control variables. Experiments on heading and vertical trajectory tracking are conducted to verify the effectiveness of the AUV system control method and the sliding mode chattering reduction method.