章军辉^{1, 2, 3}，李 庆^{1, 2}，陈大鹏^{1, 2, 3}
AuthorsHTML:章军辉^{1, 2, 3}，李 庆^{1, 2}，陈大鹏^{1, 2, 3}
AuthorsListE:Zhang Junhui ^{1, 2, 3}，Li Qing ^{1, 2}，Chen Dapeng ^{1, 2, 3}
AuthorsHTMLE:Zhang Junhui ^{1, 2, 3}，Li Qing ^{1, 2}，Chen Dapeng ^{1, 2, 3}
Unit:1. 中国科学院微电子研究所，北京 100029；
2. 江苏物联网研究发展中心，无锡 214135；
3. 无锡物联网创新中心有限公司，无锡 214135
Unit_EngLish:1. Institute of Microelectronics of Chinese Academy of Sciences，Beijing 100029，China；
2. Jiangsu R&D Center for Internet of Things，Wuxi 214135，China；
3. Wuxi Internet of Things Innovation Center Co.，Ltd.，Wuxi 214135，China
Abstract_Chinese:为进一步提升车辆跟随控制对复杂工况的适应能力，本文发展了一种多目标自适应巡航控制算法．车辆弯道跟随过程中，充分考虑了车辆动力学的纵横向耦合特性，建立了4-DOF整车动力学模型，基于干扰解耦设计将整车动力学模型解耦成采用一阶惯性环节近似的线性纵向跟随模型以及采用一阶非定常微分方程描述的横向动力学模型，再通过线性变参数方法对连续横向动力学系统方程进行一次性离散化计算，以避免在每个控制周期均需对其进行p次(p为预测时域长度)离散化处理的高计算复杂度问题．进一步，基于MPC框架设计了实时多目标权重控制策略以及考虑跟随工况的多场景融合策略，以实现纵横向性能动态协调的集成式预测控制．仿真结果表明，相对传统的参数离线标定的控制策略(MPC-CW)，考虑多场景融合设计的实时多目标权重控制策略(MPC-RW*)能够较好地实现期望跟车目的．
Abstract_English:In this study，a multi-objective adaptive cruise control algorithm for complex traffic scenarios is developed. 4-DOF vehicle dynamics model taking into consideration of tightly coupled relationships between longitudinal and lateral dynamics is established that is then completely decoupled into a 1st order linearized longitudinal car-following model as well as a 1st order differential equation based lateral dynamics model by utilizing a state-feedback based disturbance decoupling strategy. Moreover，a linear parameter-varying method is employed to discretize the equations of the continuous lateral dynamics subsystem that makes it unnecessary to execute discretization p-times in each control cycle，thereby considerably decreasing the high computational complexity. Furthermore，to achieve dynamic coordination between longitudinal and lateral performance，a weight coefficient self-tuning strategy based on a model predictive control(MPC)framework considering multi-traffic scenarios fusion design(MPC-RW*)is suggested by which the weight coefficient for each sub-objective can be adaptively adjusted along with the change of traffic scenarios. The comparative simulations show that MPC-RW* can achieve better response during car-following.
Keyword_Chinese:自适应巡航控制；直接扭矩控制；多目标；实时权重；模型预测控制
Keywords_English:adaptive cruise control；direct yaw-moment control；multi-objective；real-time weight；model predictive control

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