Modified Kalman filter-based model predictive control for ship heading control with probabilistic constraints

In this paper, the Modified Kalman Filter-Based Model Predictive Control (Modified MPC-KF) algorithm is proposed to solve the ship heading control problem by considering the presence of noise in the system. As such, the MPC problem becomes a stochastic optimization problem with probabilistic constraints. Kalman Filter is employed to replace predictions made by the Model Predictive Control. Probabilistic constraints are transformed into deterministic constraints so that the problem can be solved. In this work, the lower and upper bounds are used as the constraints over state variables. We assume the probability of violating the lower and upper bounds is the same. As a case study, the Modified MPC-KF is used in the ship heading control problem. In the simulation, the rudder angle is employed to control the ship heading angle so that it reaches the desired angle from an initial angle. Simulation results show that the Modified MPC-KF can drive the ship heading angle to the reference angle under some noises.