Smooth-Switching Control-Based Cooperative Adaptive Cruise Control by Considering Dynamic Information Flow Topology

Smooth-Switching Control-Based Cooperative Adaptive Cruise Control by Considering Dynamic Information Flow Topology
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考虑动态信息流拓扑的基于平滑切换控制的协同自适应巡航控制

DOI:
10.1177/0361198120910734
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发表时间:
2020-03
影响因子:
1.7
通讯作者:
Peeta Srinivas
Peeta Srinivas
中科院分区:
工程技术4区
文献类型:
--
作者:
Zhou Anye;Gong Siyuan;Wang Chaojie;Peeta Srinivas

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由于干扰和信息拥塞,车辆间通信可能不可靠,从而导致联网自动驾驶车辆队列中出现动态信息流拓扑 (IFT)。现有的一些研究自适应地切换协作自适应巡航控制(CACC)的控制器,以在 IFT 变化时优化串稳定性。然而,控制器之间瞬态响应的差异可能会在切换时引起不舒服的抖动,显着影响乘坐舒适性并危及车辆动力系统。为了在保持琴弦稳定性的同时提高骑行舒适度,作者通过实施双层优化模型和卡尔曼预测器,引入了一种基于平滑切换控制的 IFT 优化 CACC 方案 (CACC-SOIFT)。第一个优化层以最佳方式平衡通信失败的概率和控制性能,生成稳健的 IFT 以减少控制器切换。第二优化层调整控制器参数以最小化跟踪误差和不需要的加加速度。此外,如果发生通信故障,则应用卡尔曼预测器来预测车辆加速度。它还用于估计前方车辆的状态,以抑制测量噪声和加速度干扰。通过基于 NGSIM 现场数据的数值实验验证了所提出的 CACC-SOIFT 的有效性。结果表明,CACC-SOIFT框架可以保证动态IFT环境下的弦稳定性和乘坐舒适性。
Vehicle-to-vehicle communications can be unreliable because of interference and information congestion, which leads to the dynamic information flow topology (IFT) in a platoon of connected and autonomous vehicles. Some existing studies adaptively switch the controller of cooperative adaptive cruise control (CACC) to optimize string stability when IFT varies. However, the difference of transient response between controllers can induce uncomfortable jerks at switching instances, significantly affecting riding comfort and jeopardizing vehicle powertrain. To improve riding comfort while maintaining string stability, the authors introduce a smooth-switching control-based CACC scheme with IFT optimization (CACC-SOIFT) by implementing a bi-layer optimization model and a Kalman predictor. The first optimization layer balances the probability of communication failure and control performance optimally, generating a robust IFT to reduce controller switching. The second optimization layer adjusts the controller parameters to minimize tracking error and the undesired jerk. Further, a Kalman predictor is applied to predict vehicle acceleration if communication failures occur. It is also used to estimate the states of preceding vehicles to suppress the measurement noise and the acceleration disturbance. The effectiveness of the proposed CACC-SOIFT is validated through numerical experiments based on NGSIM field data. Results indicate that the CACC-SOIFT framework can guarantee string stability and riding comfort in the environment of dynamic IFT.
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