To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters K_P, K_I, and K_D. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of K_P, K_I, and K_D. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

It is necessary to switch the control strategies for propulsion system frequently according to the changes of sea states in order to ensure the stability and safety of the navigation. Therefore, identifying the current sea state timely and effectively is of great significance to ensure ship safety. To this end, a reasoning model that is based on maximum likelihood evidential reasoning (MAKER) rule is developed to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. Firstly, a data-driven MAKER model is constructed, which fully considers the interdependence between the input features. Secondly, the genetic algorithm (GA) is used to optimize the parameters of the MAKER model in order to improve the evaluation accuracy. Finally, a simulation is built to obtain experimental data to train the MAKER model, and the validity of the model is verified. The results show that the intelligent sea state identification model that is based on the MAKER rule can identify the propeller ventilation type more accurately, and finally realize intelligent identification of sea states.