designing an optimal pid for heading control of a linearized high speed container ship using adaptive particle swarm optimization algorithm

The reliable control of marine vessels remains a critical challenge due to the nonlinear dynamics and strong environmental disturbances inherent in ocean operations. this paper proposes an optimal heading control strategy for a linearized model of a high-speed container ship based on a proportional–integral–derivative (pid) controller whose parameters are tuned using the adaptive particle swarm optimization (apso) algorithm. while classical pid controllers are widely adopted for their structural simplicity and robustness, they often require labor-intensive parameter tuning and exhibit performance degradation under time-varying sea states. to overcome these limitations, the proposed apso framework adaptively balances global exploration and local exploitation to identify optimal pid gains. the optimization objective function integrates both trajectory-tracking accuracy and control effort, thereby ensuring a trade-off between precision and efficiency. the linear dynamic model of the container ship is formulated and implemented in matlab/simulink, serving as the test platform. simulation results reveal that the apso-tuned pid controller achieves substantial improvements in transient and steady-state responses, including overshoot suppression, reduced settling time, and acceptable gain margin, compared with conventional pid tuning. these findings highlight the potential of apso-based pid design as a robust and interpretable control solution for advanced marine navigation and dynamic positioning applications.