车辆多目标自适应巡航控制算法

Vehicular Multi-Objective Adaptive Cruise Control Algorithm

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。

To comprehensively coordinate tracking capability, fuel economy, driver comfortable response and car-following safety, a vehicular multi-objective adaptive cruise control （ACC） algorithm is designed, and an integrated longitudinal kinematic model of ACC system including vehicle model and its relationship with preceding vehicle is established. The quadratic objective functions that consider the contradictions between minimal tracking error, low fuel consumption and driver dynamic car-following behavior are developed, and the linear constraints that ensure dynamic car-following, desired comfortable response and driving safety are designed. Following model predictive control theory, the design of multi-objective ACC algorithm can be transformed into an online quadratic programming problem with multi-constraints. Adopting feedback correction mechanism, the modeling mismatch and external disturbances are greatly weakened to improve the control system robustness. Vector relaxation factors are introduced to deal with the non-feasible solution from hard constraints in the optimization process. The simulations show that the proposed algorithm can reduce fuel consumption by 9.3% and decrease tracking error index by 21.70% compared with LQR case in preceding vehicle cycle scenario, thus a good vehicle tracking is realized and the driver desired car following characteristics are satisfied.