This paper presents an optimal approach to the multi-objective synthesis of path following and rudder roll reduction for a container ship in heavy waves. An improved line of sight principle with course-keeping in track-belt is proposed to guide the ship in accordance with marine practice. Concise robust controllers for the course and roll motion based on Backstepping and closed-loop gain shaping are developed. The control parameters have obvious physical significance. The determination method is given and much effort is made to guarantee the uniform asymptotic stability of the closed-loop systems by Lyapunov synthesis. Furthermore, the multi-objective optimization method a fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the restrictions caused by the model perturbation, external disturbance and performance trade-off. Contrasting with the existing literature, the research strategy and control performance are more in line with marine engineering practice. Simulation results illustrate the performances and effectiveness of the proposed system.

This paper presents an adaptive approach to the federated filter for multi-sensor nonlinear systems with cross-correlations between process noise and local measurement noise. The adaptive Gaussian filter is used as the local filter of the federated filter for the first time, which overcomes the performance degradation caused by the cross-correlated noises. Two kinds of adaptive federated filters are proposed, one uses a de-correlation framework as local filter, and the subfilter of the other one is defined as a Gaussian filter with correlated noises at the same-epoch, and much effort is made to verify the theoretical equivalence of the two algorithms in the nonlinear fusion system. Simulation results show that the proposed algorithms are superior to the traditional federated filter and Gaussian filter with same-paced correlated noises, and the equivalence between the proposed algorithms and high degree cubature federated filter is also demonstrated.