基于鲁棒模型预测的智能汽车轨迹跟踪控制研究

针对传统的基于跟踪误差模型轨迹跟踪控制器在复杂行驶环境下控制精度不高,鲁棒性差等问题,设计一种鲁棒模型预测轨迹跟踪控制策略。采用车辆凸多胞体动力学模型显式描述车辆动态特性,结合轨迹跟踪多目标约束设计鲁棒性目标函数,通过线性矩阵不等式优化方式求解状态反馈控制律;引入前馈控制消除稳态误差,提高跟踪精度。结果表明：在不同行驶工况下,该控制器在保证车辆跟踪精度基础上可有效提高行驶稳定性,具有较强的鲁棒性。

Research on Intelligent Vehicle Trajectory Tracking Control Based on Robust Model Prediction

Aiming at the low control accuracy and poor robustness of traditional trajectory tracking controller based on the tracking error model in complex driving environment, a robust model predictive trajectory tracking control strategy is designed. The vehicle convex multicellular dynamic model is used to explicitly describe the vehicle dynamic characteristics, and the robust performance objective function is designed in combination with the trajectory tracking multi-objective constraint, and the state feedback control law is solved through the linear matrix inequality optimization. Feedforward control is introduced to eliminate the steady-state errors and improve the tracking accuracy. The simulation result shows that under the different driving conditions on the basis of ensuring the vehicle tracking accuracy, the controller can effectively improve the transmission stability and the robustness is stronger.