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Intelligent optimization on test bench of electric power steering device in automobile

机译:汽车电动助力转向装置试验台的智能优化

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Automotive steering system is used to change the direction and keep vehicles traveling straight. Electric power steering system (EPS) can improve vehicle handling performance and economy, has become one of hot spots on research and development in modern automobile steering system. Worm gears are important components of the electric power steering system; its transmission efficiency shall affect directly transmission efficiency and reset ability of the electric power steering system. Therefore, it is very important to adopt optimization methods to design the electric power steering systems. The fuzzy optimization model is established to design EPS. The method of second-class comprehensive evaluation was used by the optimal level cut set, thus the fuzzy optimization is transformed into the conventional optimization. This drawback of the genetic algorithm can be overcome by combining it with local gradient-based algorithms, which are known for their faster convergence. This hybrid approach improves the efficiency of the algorithm and also avoids the need to specify a good initial point for the derivative-based methods. A hybrid algorithm combining the genetic algorithm and local algorithm are developed in this paper, and the genetic algorithm can be combined with fuzzy logic to efficiently find a more accurate solution.
机译:汽车转向系统用于改变方向并保持车辆直行。电动助力转向系统(EPS)可以提高车辆的操纵性能和经济性,已成为现代汽车转向系统研究与开发的热点之一。蜗轮齿轮是电动助力转向系统的重要组成部分。其传动效率将直接影响电动助力转向系统的传动效率和复位能力。因此,采用优化方法来设计电动助力转向系统非常重要。建立了模糊优化模型来设计EPS。最优水平割集采用了二等综合评价方法,将模糊优化转化为常规优化。遗传算法的这一缺点可以通过将其与基于局部梯度的算法相结合来克服,该算法以收敛速度更快而闻名。这种混合方法不仅提高了算法的效率,而且还避免了为基于导数的方法指定良好的起始点的需要。本文提出了一种将遗传算法与局部算法相结合的混合算法,并将遗传算法与模糊逻辑相结合可以有效地找到更精确的解。

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