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Zero-speed fin stabilizer is applied to reduce the roll motion of ships at zero speed. This paper aims to explore the control strategy of zero-speed fin stabilizer through a composite method of theoretical analysis, simulations and tank tests. The hydrodynamic force model is established using analytical approach and a simplified model is obtained by fitting the CFD simulation data. The control strategy of zero-speed fin stabilizer is obtained based on disturbance and compensation by analyzing the phase matching relationship between the wave disturbance, the roll motion of the ship, the movement of the fin and the fin-induced hydrodynamic force. Simulations and water tank tests are performed to verify the effectiveness and feasibility of the obtained control strategies. The results of simulations and tank tests show that the obtained control strategies of zero-speed fin stabilizer based on disturbance and compensation are effective and practical. The proposed method provides theoretical and experimental support for engineering application, and can also be a reference for the controller design of zero-speed fin stabilizers. [ABSTRACT FROM AUTHOR]

This paper focuses on the effects of vehicle gap changes on fuel and emission performance of the simulated traffic flow in the adaptive cruise control (ACC) strategy. Firstly, the close correlation of vehicle gap changes and the host car's behaviors was explored with the measured car-following data. Secondly, the correlation between the host car's velocity and vehicle gap changes with different memory steps was also explored to develop the nth car’s optimal velocity function. Thirdly, a microscopic traffic simulation program was created for analyzing the traffic flow evolution process and approximately estimating the fuel consumptions and exhaust emissions. As a result, it was seen that vehicle gap changes with memory significantly affect fuel economy and emission performance of the simulated traffic flow in the ACC strategy, which can result in low fuel consumptions and exhaust emissions. This study is an incremental step forward for designing the control strategy of the ACC system. [ABSTRACT FROM AUTHOR]

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. [ABSTRACT FROM AUTHOR]