A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions.

中文翻译：

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一种数据驱动的预测控制器结合向量自回归与外生输入模型和传播器估计方法用于车辆横向稳定

提出了一种基于子空间模型辨识方法的车辆稳定通用预测控制器。传统的预测控制器总是基于车辆的原理模型开发，不可避免地存在参数不确定性和适应性差的问题。相比之下，所提出的基于子空间的通用预测控制器是通过数据驱动的过程实现的，并且在车辆稳定性控制方面具有良好的适应性。受基于子空间预测器构造的启发，预测控制器的关键如下：（1）根据输入和输出数据根据控制对象的模型结构识别系统模型；(2) 识别模型对系统的输出预​​测；(3) 通过将线性-二次-高斯指数与预测输出相结合而设计的最优控制律。控制器开发中的主要问题在于相关矩阵的递归估计，这受到子空间模型识别理论的限制。外源输入模型向量自回归和子空间识别算法中的传播器方法的实现有效地解决了估计精度和计算效率问题。结合线性-二次-高斯指数函数，详细推导出车辆稳定性控制的预测规律。最后，基于通过标准路试验证的车辆模型，