Adaptive neural networks dynamic surface control algorithm for 3 DOF surface ship

Abstract

The paper presents a adaptive dynamic surface control method for a class of strict-feedback nonlinear system based on neural network. In the previous adaptive neural networks control proposed using backstepping, the number and complexity of intermediate variables increase as the increasing order of the system. This makes it difficult to achieve learning for the high-order strict-feedback systems due to “the explosion of complexity”. To overcome the difficulty, a stable adaptive neural DSC is proposed with auxiliary first-order filters. Due to the use of DSC, the derivative of the filter output variable is used as the NN input instead of the previous intermediate variables. This reduces greatly the dimension of NN inputs, especially for high-order systems. The controller is applied to 3 DOF surface ship model, which proposed by Fossen. The simulation results show the advantages of the proposed control algorithm and the using ability in practice.