Large variations load-to-curb weight ratios are linked to significant changes in parameters critical to control design for vehicle stability control system. Unique and highly customised vehicles, such as the lightweight solar car in this paper, are more susceptible to the impact of such variations when developing control methods. The purpose of this study is to study the influence of variation in loading conditions, the effect of ignoring changes in inertial parameters, and develop and compare a number of alternative vehicle stability control methods that can be applied to rear-wheel driven vehicles via in-wheel motors. In this paper a Sliding Mode Control (SMC) both nominal and when including uncertainty, Dynamic Curvature Control (DCC) and a Proportional–Integral Control (PI) strategies are compared to the baseline open-loop control case. Each controller is implemented through co-simulation via MATLAB® Simulink® and Siemens Amesim™ using a 15-DOF non-linear vehicle model. The results show that SMC achieves the best performance, whilst DCC tends to overshoot target conditions prior to settling, indicating that SMC is the preferred control strategy. It is also demonstrated that by ignoring the change in the inertial parameters in simulation environments can produce an incorrect translation of the control performance.

负载与路边重量比的大变化与对车辆稳定性控制系统的控制设计至关重要的参数的显着变化有关。独特且高度定制的车辆，例如本文中的轻型太阳能汽车，在开发控制方法时更容易受到这种变化的影响。本研究的目的是研究负载条件变化的影响，忽略惯性参数变化的影响，并开发和比较一些可通过 in- 应用于后轮驱动车辆的替代车辆稳定性控制方法。车轮马达。在本文中，滑模控制 (SMC) 标称和包含不确定性时，动态曲率控制 (DCC) 和比例积分控制 (PI) 策略与基线开环控制情况进行了比较。每个控制器都是通过 MATLAB® Simulink® 和 Siemens Amesim™ 使用 15 自由度非线性车辆模型通过联合仿真实现的。结果表明，SMC 实现了最佳性能，而 DCC 在稳定之前往往会超出目标条件，这表明 SMC 是首选的控制策略。它还表明，通过忽略仿真环境中惯性参数的变化，可能会产生不正确的控制性能转换。