In this study, a side-slip angle estimator was presented, capable of compensating the model bias between test data and model prediction under the uncertainties of vehicle speed and friction coefficient, to obtain the actual side-slip angle. Specifically, the model validation method was adopted to optimise the irrelative coefficient of the principal component analysis model for calibrating the side-slip angle model bias. To calibrate all the model bias under other conditions without test data, the response surface methodology was developed to approximate all the side-slip angle model bias under any conditions. Based on the estimator, an integrated method based on the fuzzy model predictive control (MPC) algorithm was proposed with respect to the yaw moment and side-slip angle, taking brake pressure and steering angle as the controlled objects. Such a method integrates the function of active front steering and direct yaw-moment control into the MPC model and coordinates their proportion in accordance with the relevant parameters using the fuzzy control method. Lastly, the results of simulations with the hardware-in-loop platform verified that the proposed algorithm could enhance the vehicle stability and the computational accuracy of the side-slip angle.

中文翻译：

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考虑侧滑角不确定性的车辆动力学稳定性综合策略

在这项研究中，提出了一种侧滑角估计器，它能够在车速和摩擦系数不确定的情况下补偿测试数据和模型预测之间的模型偏差，从而获得实际的侧滑角。具体地，采用模型验证方法对主分量分析模型的相关系数进行优化，以校正侧滑角模型偏差。为了在没有测试数据的情况下校准其他条件下的所有模型偏差，开发了响应面方法来近似任何条件下的所有侧滑角模型偏差。基于估计量，针对制动力矩和转向角作为控制对象，针对偏航力矩和侧滑角提出了一种基于模糊模型预测控制（MPC）算法的集成方法。这种方法将主动式前转向功能和直接偏航力矩控制功能集成到MPC模型中，并使用模糊控制方法根据相关参数协调它们的比例。最后，在硬件在环平台上的仿真结果验证了所提算法能够提高车辆的稳定性和侧滑角的计算精度。