An Efficient Data-Driven Switched Predictive Control Strategy With Online Data for Vehicle Lateral Stabilization in Ice and Snow-Rutted Conditions
针对冰雪车辙路面车辆稳定性控制中模型精度与控制器易实现性的矛盾，提出一种基于在线数据的高效数据驱动切换预测控制策略，通过引入Givens旋转和遗忘因子更新子空间预测方程，并采用衰减激励信号和后验预测误差的滞回比较来减少稳态波动，数值仿真验证了其有效性。
In ice and snow-rutted conditions, it is challenging to design a vehicle stability controller to simultaneously resolve the conflict between the accuracy of the system model and the easy implementation of the controller. To this end, the application of a data-driven control method for vehicle stability control represents a novel, feasible opportunity. This article introduces Givens rotation and forgetting factors to efficiently update the subspace prediction equation with online data. An online data-driven predictive control (ODPC) method is proposed on this basis. To address the problem that persistently excited (PE) condition will cause fluctuations in the steady-state response of ODPC, a data-driven switched predictive control strategy (DSPCS) employing attenuated excitation (AE) signals and hysteresis comparisons based on posterior prediction errors is proposed. In addition, an implementation method involving the Laguerre function (LF) parameterization of the control input is proposed to improve the computational efficiency further. Numerical simulation results show that both the ODPC method and the DSPCS can effectively track given yaw rate and sideslip angle reference under the influence of ruts. Furthermore, the DSPCS can effectively reduce steady-state response fluctuations. In addition, the LF parameterization is superior regarding computational time.