车辆多目标自适应巡航控制算法

发布时间：2018-03-31 15:36

本文选题：自适应巡航控制　切入点：多目标　出处：《西安交通大学学报》2016年11期

【摘要】：为了综合协调车辆跟车时的追踪性能、燃油经济性能、驾乘人员舒适性能和跟车安全性能,研究了多目标自适应巡航控制(ACC)算法,建立了包含车辆模型和车间关系的ACC系统集成式纵向运动学模型,设计了描述追踪误差、燃油消耗量和驾驶员跟车行为误差的目标函数,以及保证动态跟车、期望驾乘感受和跟车安全的约束条件,基于模型预测控制理论将多目标ACC系统控制算法转化为带有多个约束的在线二次规划问题。采用反馈校正机制改善了算法设计时存在的建模失配和外部干扰等低鲁棒性问题,引入向量松弛因子解决了优化求解过程中硬约束导致的控制算法非可行解问题。仿真结果表明,相比线性二次型调节器的ACC算法,所提控制算法在前车循环工况中100km油耗降低9.3%,追踪误差指标降低21.7%,从而实现了良好的车辆追踪,同时满足驾驶员期望的跟车特性要求。
[Abstract]:In order to comprehensively coordinate the tracking performance, fuel economy performance, comfort performance and safety performance of vehicle, the multi-objective adaptive cruise control (ACCS) algorithm is studied.The integrated longitudinal kinematics model of ACC system including vehicle model and workshop relationship is established. The objective function describing tracking error, fuel consumption and driver's behavior error is designed, and the dynamic tracking is ensured.Based on the model predictive control theory, the multi-objective ACC system control algorithm is transformed into an online quadratic programming problem with multiple constraints.The feedback correction mechanism is used to improve the low robustness problems such as modeling mismatch and external disturbance in the design of the algorithm. The vector relaxation factor is introduced to solve the infeasible solution of the control algorithm caused by hard constraints in the optimization process.The simulation results show that compared with the ACC algorithm of the linear quadratic regulator, the proposed control algorithm can reduce the fuel consumption of 100km by 9.3 and the tracking error index by 21.7in the front car cycle, thus achieving good vehicle tracking.At the same time meet the driver's expectations of the characteristics of the car requirements.
【作者单位】：同济大学汽车学院;东京大学生产技术研究所;
【基金】：国家自然科学基金资助项目(51575393) 上海汽车工业科技发展基金会资助项目(1526)
【分类号】：U463.6

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