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                    通信延時環境下基于觀測器的智能網聯車輛隊列分層協同縱向控制

                    朱永薪 李永福 朱浩 于樹友

                    朱永薪, 李永福, 朱浩, 于樹友. 通信延時環境下基于觀測器的智能網聯車輛隊列分層協同縱向控制. 自動化學報, 2021, 47(x): 1?15 doi: 10.16383/j.aas.c210311
                    引用本文: 朱永薪, 李永福, 朱浩, 于樹友. 通信延時環境下基于觀測器的智能網聯車輛隊列分層協同縱向控制. 自動化學報, 2021, 47(x): 1?15 doi: 10.16383/j.aas.c210311
                    Zhu Yong-Xin, Li Yong-Fu, Zhu Hao, Yu Shu-You. Observer-based longitudinal control for connected and automated vehicles platoon subject to communication delay. Acta Automatica Sinica, 2021, 47(x): 1?15 doi: 10.16383/j.aas.c210311
                    Citation: Zhu Yong-Xin, Li Yong-Fu, Zhu Hao, Yu Shu-You. Observer-based longitudinal control for connected and automated vehicles platoon subject to communication delay. Acta Automatica Sinica, 2021, 47(x): 1?15 doi: 10.16383/j.aas.c210311

                    通信延時環境下基于觀測器的智能網聯車輛隊列分層協同縱向控制

                    doi: 10.16383/j.aas.c210311
                    基金項目: 國家自然科學基金(U1964202, 61773082), 國家重點研發計劃(2018YFB1600500)資助
                    詳細信息
                      作者簡介:

                      朱永薪:重慶郵電大學自動化學院碩士研究生, 主要研究方向為車輛隊列控制. E-mail: zhuyongxin994@163.com

                      李永福:博士, 重慶郵電大學自動化學院教授, 智能空地協同控制重慶市高校重點實驗室主任. 主要研究方向為智能網聯汽車和空地協同控制. 本文通信作者. E-mail: liyongfu@cqupt.edu.cn

                      朱浩:博士, 重慶郵電大學自動化學院教授. 主要研究方向為智能車環境感知與信息融合. E-mail: zhuhao@cqupt.edu.cn

                      于樹友:博士, 吉林大學控制科學與工程系教授. 主要研究方向為模型預測控制. E-mail: shuyou@jlu.edu.cn

                    Observer-based Longitudinal Control for Connected and Automated Vehicles Platoon Subject to Communication Delay

                    Funds: Supported by National Natural Science Foundation of China (U1964202, 61773082), and National Key R&D Program of China (2018YFB1600500)
                    More Information
                      Author Bio:

                      ZHU Yong-Xin Master student at the College of Automation, Chongqing university of Posts and Telecommunications. His research interest covers platoon control of vehicles

                      LI Yong-Fu Ph. D., professor at the College of Automation, Chongqing University of Posts and Telecommunica-tions. His research interest covers connected and automated vehicles and air-ground cooperative control. Corresponding author of this paper

                      ZHU Hao Ph. D., professor at the College of Automation, Chongqing University of Posts and Telecommunications. His research interest covers environmental perception of intelligent vehicles and information fusion

                      YU Shu-You Ph. D., professor at the Department of Control Science and Engineering, Jilin University. His research interest covers model predictive control

                    • 摘要: 考慮通信延時影響的車輛隊列控制問題, 本文提出了一種基于觀測器的分布式車輛隊列縱向控制器. 首先, 基于分層控制策略分別設計上下層控制器, 通過上層控制器優化期望加速度, 下層控制器克服車輛模型非線性實現期望加速度和實際加速度的一致, 上層控制器設計過程中, 基于三階線性化車輛模型, 考慮觀測器、車輛動態耦合特性和通信延時, 提出一種通信延時環境下基于觀測器的車輛隊列控制器, 利用觀測器估計領導車輛加速度信息從而減輕通信負擔. 然后利用Lyapunov-Krasovskii方法分析了車輛隊列的穩定性, 并得出了通信延時上界, 同時利用傳遞函數方法分析了串穩定性. 最后通過數值仿真驗證上層控制器的有效性和穩定性, 在此基礎上, 利用PreScan軟件中高保真車輛動態模型, 驗證了所提分層控制策略的有效性.
                    • 圖  1  車輛隊列與通信拓撲結構

                      Fig.  1  Vehicle platoon and communication topology

                      圖  2  發動機扭矩特性逆模型

                      Fig.  2  Inverse model of engine torque characteristics

                      圖  3  節氣門/剎車控制切換策略

                      Fig.  3  Switching strategy between throttle and brake controls

                      圖  4  基于觀測器的控制器示意圖

                      Fig.  4  Sketch of the proposed observer-based controller

                      圖  5  變速器傳動比

                      Fig.  5  The gear ratio of transmission

                      圖  6  位置圖: (a) 文獻[12]-無延時, (b) 文獻[12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) 控制器 (14) -無延時,(d) 控制器(14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      Fig.  6  Position profile: (a) controller in [12]- no time delay, (b) controller in [12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) controller (14)- no time delay, (d) controller (14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      圖  7  速度圖: (a) 文獻[12]-無延時, (b) 文獻[12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) 控制器 (14) -無延時,(d) 控制器(14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      Fig.  7  Velocity profile: (a) controller in [12]- no time delay, (b) controller in [12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) controller (14)- no time delay, (d) controller (14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      圖  8  加速度圖: (a) 文獻[12]-無延時, (b) 文獻[12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) 控制器 (14) -無延時,(d) 控制器(14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      Fig.  8  Acceleration profile: (a) controller in [12]- no time delay, (b) controller in [12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) controller (14)- no time delay, (d) controller (14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      圖  9  間距誤差圖: (a) 文獻[12]-無延時, (b) 文獻[12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) 控制器 (14) -無延時,(d) 控制器(14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      Fig.  9  Spacing error profile: (a) controller in [12]- no time delay, (b) controller in [12]-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$, (c) controller (14)- no time delay, (d) controller (14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      圖  10  ${z_{2,i}}(t)$${\hat z_{2,i}}(t)$: (a) 控制器 (14) -無延時, (b) 控制器 (14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      Fig.  10  ${z_{2,i}}(t)$ and ${\hat z_{2,i}}(t)$: (a) controller (14)- no time delay, (b) controller (14)-$\tau (t) \in [0.1,\;0.2]{\rm{s}}$.

                      圖  11  基于PreScan/Simulink聯合仿真平臺框架示意圖

                      Fig.  11  Framework of the PreScan/Simulink-based Co-Simulation platform

                      圖  12  PreScan中的實驗場景: (a) 隊列初始狀態, (b) 隊列移動, (c) 隊列停止.

                      Fig.  12  Experiment scenario in PreScan: (a) Initial state of platoon, (b) Platoon move, (c) Platoon stop.

                      圖  13  PreScan中仿真結果: (a) 間距誤差圖, (b) 速度圖, (c) 加速度圖, (d) 節氣門開度圖,(e) 剎車壓力圖, (f). ${z_{2,i}}(t)$${\hat z_{2,i}}(t)$.

                      Fig.  13  Simulation results in PreScan: (a) spacing error profile, (b) velocity profile, (c) acceleration profile, (d) throttle, (e) brake pressure, (f) ${z_{2,i}}(t)$ and ${\hat z_{2,i}}(t)$.

                      表  1  控制器參數

                      Table  1  Controller parameters

                      參數數值單位
                      $\alpha $$0.67$${{\rm{s}}^{ - 1}}$
                      ${g_{o,1}}$$0.12$${{\rm{s}}^{ - 2}}$
                      ${g_{o,2}}$$0.52$${{\rm{s}}^{ - 1}}$
                      ${g_{o,3}}$$0.3$
                      ${V_1}$$6.75$${\rm{m/s}}$
                      ${V_2}$$7.91$${\rm{m/s}}$
                      ${C_1}$$0.13$${{\rm{m}}^{ - 1}}$
                      ${C_2}$$1.59$
                      ${h_1}$$30$
                      ${h_2}$$12$
                      ${k_P}$$10$
                      ${k_I}$$0.3$
                      ${k_D}$$0.1$
                      $\vartheta $$0.1$${\rm{m/}}{{\rm{s}}^2}$
                      下載: 導出CSV

                      表  2  PreScan中車輛模型參數

                      Table  2  The parameters of vehicle model in PreScan

                      參數數值單位
                      $m$$1532$${\rm{kg}}$
                      $g$$9.8$${\rm{m/}}{{\rm{s}}^2}$
                      ${l_c}$$4.63$${\rm{m}}$
                      ${C_A}$$0.31$${\rm{kg/m}}$
                      ${i_0}$$2.7$
                      ${\eta _{\rm{T}}}$$1$
                      $f$$0.01$
                      下載: 導出CSV
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                    • 修回日期:  2021-06-18
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