Differential steering is an emerging steering concept based on traction force differences between left and right sides of vehicles equipped with independently actuated wheels. Actually, this concept is only used to support classical vehicle designs utilising rack and pinion steering components. However, it may fully substitute such devices to reduce complexity and cut down costs. Since we do not have well-established design rules for such kind of vehicles, this paper will overcome the missing experience and explore the general applicability of such a new steering concept by using multiobjective optimisation. Three design objectives and three constraints are defined with respect to the dynamic, steady-state and low-speed steering performance of the vehicle. Since mechanical and control parts are strongly coupled, their parameters are optimised simultaneously by a multiobjective genetic algorithm aided by iteratively updated neural network-based response surface metamodels. Optimisation results in a variety of technically feasible Pareto-optimal designs. Investigation of a particular optimal design shows that a vehicle with differential steering is able to provide convincing steering performance.

差速转向是一种新兴的转向概念，它基于配备有独立驱动轮的车辆左右两侧之间的牵引力差异。实际上，该概念仅用于支持利用齿条和小齿轮转向组件的经典车辆设计。但是，它可以完全替代此类设备，以降低复杂性并降低成本。由于我们没有针对此类车辆的既定设计规则，因此本文将克服所缺少的经验，并通过多目标优化来探索这种新转向概念的普遍适用性。相对于车辆的动态，稳态和低速转向性能，定义了三个设计目标和三个约束。由于机械零件和控制零件紧密耦合，通过多目标遗传算法同时优化基于迭代神经网络的响应面元模型，从而优化了它们的参数。优化导致各种技术上可行的帕累托最优设计。对特定最佳设计的研究表明，具有差速转向的车辆能够提供令人信服的转向性能。