Ship Course Control Study Based On Robust Neural Network

According to the ship response type course control nonlinear system, using Lyapunov stability theory, Backstepping method and RBF neural network approximation method, firstly put forward a ship course robust intelligent damping control algorithm and design a ship course controller; then introduce the concept of prescribed performance, and specially design an uncertain ship course robust intelligent controller with prescribed performance bounds.At first, a ship course robust intelligent controller considering nonlinear damping is put forward for the ship nonlinear uncertain system. In order to solve the issue of the ship course control under the condition of model uncertainty, put forward a method of robust intelligent controller design based on Lyapunov stability theory and Backstepping. Such controller can make the state signal exponential asymptotic stable, it also means that the ship can track the setting course with arbitrary small tracking error.Then, the concept of prescribed performance is introduced in this thesis, based on Lyapunov stability theory and Backstepping, a ship course robust intelligent controller with nonlinear damping, uncertainty and prescribed performance is put forward for the ship mathematical model of nonlinear response type. So that it can guarantee that the tracking error converges to an arbitrary small region and the convergence speed and the overshoot of the system is lower than a prespecified constant.In this thesis, both two designed controllers can guarantee the stability of the closed-loop system, and can make the tracking error arbitrarily small. And to demonstrate the effectiveness of the mentioned controller above, MATLAB simulation results are used.