Model predictive control for path following and roll stabilization of marine vessels based on neurodynamic optimization

The conventional objective of rudder roll stabilization is to control the ship course and roll motion simultaneously. Nevertheless, the ship course control is not competent for advanced requirement nowadays on controlling the position, which motivates the present study. This paper proposes an efficient model predictive control (MPC) for path following and roll stabilization of marine vessels by rudder in waves. It is a typical underactuated problem with multi-objectives; in addition, the control input is constrained in both amplitude and rate. MPC is one of the most favorable solutions for this problem, given its capability of solving optimization problem with constraints; however, there are two major challenges existed in the implementation of MPC, i.e., the computational intensity and closed-loop stability. In this paper, a neurodynamic system based on the projection neural network (PNN), which is well known for the parallel computational capability, is established to optimize the objective function formulated by MPC method, thereby improving the computational efficiency. The closed-loop stability is guaranteed by including a quadratic terminal cost, derived from the solution of a Lyapunov equation. Various simulations are conducted to illustrate the advantages of control design proposed herein, in which the model uncertainty and wave disturbances are taken into account.