With the increasing use of electric propulsion ships, the emergence of the shaftless rim-driven thruster (RDT) as a revolutionary integrated motor thruster is gradually becoming an important development direction for green ships. The shaftless structure of RDTs leads to their dependence on position sensorless control techniques. In this study, a novel control algorithm using a composite sliding mode observer (SMO) with a modified feed-forward phase-locked loop (PLL) is presented for achieving high accuracy position and speed control of shaftless RDT motors. The deviation between the observed and actual currents is exploited to develop a current SMO to extract back electromotive force (back-EMF) errors. On this basis, a back-EMF observer is established to achieve accurate estimation of the back-EMF. The basic structure of the PLL was modified and incorporates a speed feedforward mechanism, which enhances the performance of rotor position estimation and facilitates bidirectional rotation. The stability of the algorithm has been verified in Matlab/Simulink for a range of steady-state, dynamic, and ship propeller loading conditions. Remarkably, the control algorithm boasts an impressive adjustment time of approximately 0.006 s and its position estimation error may be as low as 0.03 rad. Simulation results highlight the performance of the algorithm to achieve bidirectional rotation, while exhibiting fast convergence, minimal vibration, exceptional control accuracy, and robustness.

In order to improve the energy efficiency and economic effect of conventional diesel-powered rubber-tired gantry (RTG) cranes in container terminals, various hybrid RTG cranes were studied. However, these current hybrid RTG cranes have several disadvantages, such as high initial investment cost and poor versatility of energy management methods. In this paper, a hybrid RTG crane consisting of a small-sized diesel generator (DG), a ternary material lithium battery, and a supercapacitor (SC) is studied, and a hybrid RTG crane energy management method based on game theory is proposed. The DG, lithium battery, and SC are modeled as three independent agents to participate in the game, and a multi-agent system (MAS) is established. During the RTG crane work process, agents achieve a coordinated and stable working state through the game, i.e., the Nash equilibrium. Three typical crane operation scenarios, the rated load, continuous work, and intermittent work, are simulated and studied. According to the results, combinations of the three devices can meet the power demand and system performance. The power of the DG in the hybrid system is small (only 20 kW), reducing fuel consumption and overall emissions during RTG crane operation.

This paper considers the trajectory tracking problem for a class of automatic straddle carriers (ASCs) with external interferences and the overturning constraints. First, the steering and the dynamics of the ASC are analyzed and the mathematical model of the ASC is established. Then, considering the impact of external interferences on the trajectory tracking accuracy, an improved dynamic Kalman filter is designed in order to obtain the state estimation of the ASC. Based on the obtained state estimation, a model-predictive control method is proposed which takes the anti-overturning constraints into account. In addition, the improved Kalman-filter-based model-predictive control (iKFMPC) algorithm is summarized for the considered ASC to travel smoothly along the given trajectory while meeting the overturning resistance. Finally, simulation analyses demonstrate the effectiveness and superiority of the proposed method.