Mr. Yufeng Zhang Profile

Yufeng Zhang

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Proceedings Article | 1 April 2024 Paper

Clamping force control strategy of electromechanical brake system

Tianyu Li, Jingyang Li, Yufeng Zhang, Zhenyang Yu

Proceedings Volume 13082, 1308230 (2024) https://doi.org/10.1117/12.3026152

KEYWORDS: Control systems, Mathematical modeling, Modeling, Angular velocity, Systems modeling, Resistance, Magnetism, Error analysis, Design, Vehicle control

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The electronic mechanical braking system has become the development direction of passenger car braking systems because of their advantages of simple structure and rapid response. To achieve precise control of braking force, this paper makes a mathematical analysis of the motor parts and mechanical parts of EMB. In this paper, a new sliding mode control strategy is proposed to overcome the limitations of the existing control, that is, the installation and control accuracy of the sensor. Then, by comparing the simulation results of sliding mode control and PID control, the effectiveness of the control algorithm is verified. Finally, the performance of the EMB braking system is verified by simulating the braking condition under dry concrete pavement.

Proceedings Article | 9 January 2024 Paper

An improved dung beetle optimizer

Jinxiang Feng, Jingyang Li, Yufeng Zhang, Hexi Baoyin

Proceedings Volume 12969, 129692V (2024) https://doi.org/10.1117/12.3014472

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Dung Beetle Optimizer(DBO) is an effective metaheuristic algorithm proposed in 2022. But at the same time, DBO also suffers from a local-global imbalance in the exploration process, tends to fall into local optimization and exploitability needs to be further improved, etc. Therefore, we propose an improved DBO algorithm to address these shortcomings and named it CDBO. Firstly, Tent chaotic mapping can be used for the purpose of initializing the population, improving the quality of initial solutions, promoting the enhancement of population variety, and augmenting the global search capability of the algorithm. Secondly, introducing dynamic weighting factors enables the algorithm to fully search for local areas while also taking into account global exploration. To assess the effectiveness of CDBO, a total of 12 benchmark test functions were utilized to evaluate the performance of this algorithm, wherein CDBO was compared with other widely recognized metaheuristic algorithms. The results showed that CDBO had improved search accuracy and convergence speed. Finally, CDBO was applied to airfoil optimization problem, verifying the feasibility of applying CDBO to practical engineering problems.

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