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Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton's second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses. [ABSTRACT FROM AUTHOR]

An overhead crane system is a complex time-varying system if it includes simultaneous hoisting and moving operations. To address the problem that time-varying systems usually have no exact analytical solution, an anti-swing method based on linear approximation of the phase plane trajectory is proposed. Specifically, the phase plane trajectory of the swing angle is first analyzed and it is found that the double-step acceleration method with unequal acceleration amplitude and acceleration time has a combination of limit loops in the phase plane trajectory of the swing angle when the rope length varies, so the average rope length of each section is used to approximate the natural oscillation frequency. Next, the input shaping technique is used to shape the input signal into the desired output signal so that the load swing angle can follow the planned trajectory. The desired acceleration amplitude and switching time are then derived based on the geometric relationship between the swing angle in the phase plane and the specific physical constraints. Finally, the feasibility of the proposed method is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Predicting the output power of the wind turbine accurately is an important means to ensure the stable operation of the wind power system. More and more deep learning methods are currently applied to the prediction of the output power, but few of them pay attention to the non-stationarity of the output power, which leads to a decrease in the prediction accuracy. In this study, a hybrid forecasting model that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and temporal convolutional neural network (TCN) is developed to predict the output power. First, the CEEMDAN method is used to decompose the original output power sequence into several sub-sequences of different frequencies. Then, the TCN model is used to predict each sub-sequence. Finally, the sum of the prediction results is used as the predicted value of the output power. Take a wind turbine in Shanghai as an example, the experimental results show that the CEEMDAN-TCN model has the highest prediction accuracy when compared with the current mainstream deep learning models. Compared with the single TCN, the mean square error (MSE) predicted by the CEEMDAN-TCN model is reduced by 17%, and the root mean square error (RMSE) is reduced by 11%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]