The heavy-haul train(HHT)has large capacity and high efficiency,which represents the level of freight and makes the amelioration of control performance a trend in various countries.Improving the model reliability and ...
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The heavy-haul train(HHT)has large capacity and high efficiency,which represents the level of freight and makes the amelioration of control performance a trend in various countries.Improving the model reliability and increasing the anti-disturbance ability of the operation controller are two main ways to improve the operation control accuracy of HHTs.Herein,to describe the large nonlinear system more precisely,an interval type-2 fuzzy logic system(IT2FLS)is introduced to obtain a dynamic model.Then,a linear active disturbance rejection controller(LADRC)is designed to achieve precise operational control.In addition,the‘bandwidth method’is combinedwith the sparrowsearch algorithm(SSA)to solve the difficulty of controller parameters adjustment.Afterwards,the stability analysis of the closed-loop control system is given.The simulation experiments are conducted based on data collected from HXD1 locomotives driven by excellent drivers.Results show that the speed tracking error is no more than 0.5 km/h,and demonstrate that the proposed method significantly improves the operational performance of HHTs.
The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of ...
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The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and sparrow search algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.
In this paper, a multi-freedom ski training system is introduced for indoor alpine skiing training and entertainment.The translation platform and the six-degree-of-freedom platform are adopted in the system to realize...
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In this paper, a multi-freedom ski training system is introduced for indoor alpine skiing training and entertainment.The translation platform and the six-degree-of-freedom platform are adopted in the system to realize a wide range of slalom training and terrain simulation. Combining the washout algorithm with the virtual scene and terrain information in Unity3 D, the system provides users with the real physical and visual experience of alpine skiing. In the design of washout algorithm, sparrow search algorithm(SSA) is used to optimize the parameters of washout filter, which can achieve a less sensation error and a higher efficiency of the workspace usage. Simulation and experimental results show that our system can give users a real alpine skiing experience, and facilitate the users to ski training and experience the terrain of the ski trail.
Helicopter gearbox support strut is one of the main research objects in the field of vibration and noise control in helicopter cabin. In this paper, a design method of quasi-periodic struts is proposed based on the ex...
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Helicopter gearbox support strut is one of the main research objects in the field of vibration and noise control in helicopter cabin. In this paper, a design method of quasi-periodic struts is proposed based on the existing researches about periodic struts. The dynamic model of the quasi-periodic strut is established by combining the spectral element method and the transfer matrix method. Simulation studies on the vibrational analysis of one-dimensional finite periodic and quasi-periodic support struts are conducted, and the defective state mechanism introduced by the quasi-periodic is studied in detail through mode cloud images. The simulation results indicate that the quasi-periodic strut can exhibit vibration attenuation characteristics over a wider frequency range, provided that a suitable quasi-periodic sequence is chosen. On this basis, the sparrow search algorithm is adopted to optimize the geometry by taking the maximum attenuation as the performance objective function. The results show that the overall vibration attenuation effect of the optimized strut has been significantly improved.
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