logo

SCIENCE CHINA Information Sciences, Volume 63 , Issue 9 : 190204(2020) https://doi.org/10.1007/s11432-019-2945-0

Data-driven optimal cooperative adaptive cruise control of heterogeneous vehicle platoons with unknown dynamics

More info
  • ReceivedNov 18, 2019
  • AcceptedMay 21, 2020
  • PublishedAug 12, 2020

Abstract

This paper considers the cooperative adaptive cruise control (CACC) problem of heterogeneous vehicle platoons and proposes a data-driven optimal CACC approach for the heterogeneous platoon with unknown dynamics. To cope with the unknown dynamics of the vehicle CACC platoon system, the adaptive dynamic programming is used to design an online iteration policy for optimal CACC of the platoon. Using the predecessor-following topology, the CACC controllers are computed by employing the desired spacing errors, relative velocities, and accelerations of the vehicles. The stability of the closed-loop CACC system and the iteration algorithm are presented. Furthermore, the string stability of the platoon with the CACC system is established in terms of the acceleration transfer function between adjacent vehicles in frequent domain. Finally, the effectiveness of the proposed method is verified in two complex scenarios of varying speed cruise.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61803336) and Zhejiang Provincial Natural Science Foundation (Grant No. LR17F030004).


References

[1] Guo H, Liu F, Xu F. Nonlinear Model Predictive Lateral Stability Control of Active Chassis for Intelligent Vehicles and Its FPGA Implementation. IEEE Trans Syst Man Cybern Syst, 2019, 49: 2-13 CrossRef Google Scholar

[2] Guo H, Shen C, Zhang H. Simultaneous Trajectory Planning and Tracking Using an MPC Method for Cyber-Physical Systems: A Case Study of Obstacle Avoidance for an Intelligent Vehicle. IEEE Trans Ind Inf, 2018, 14: 4273-4283 CrossRef Google Scholar

[3] Mudigonda S, Fukuyama J, Ozbay K. Evaluation of a Methodology for Scalable Dynamic Vehicular Ad Hoc Networks in a Well-Calibrated Test Bed for Vehicular Mobility. Transpation Res Record, 2013, 2381: 54-64 CrossRef Google Scholar

[4] Alcaraz J, Vales-Alonso J, Garcia-Haro J. Control-based scheduling with QoS support for vehicle to infrastructure communications. IEEE Wireless Commun, 2009, 16: 32-39 CrossRef Google Scholar

[5] Zhao D, Hu Z, Xia Z. Full-range adaptive cruise control based on supervised adaptive dynamic programming. Neurocomputing, 2014, 125: 57-67 CrossRef Google Scholar

[6] Zhao D, Xia Z, Zhang Q. Model-Free Optimal Control Based Intelligent Cruise Control with Hardware-in-the-Loop Demonstration [Research Frontier]. IEEE Comput Intell Mag, 2017, 12: 56-69 CrossRef Google Scholar

[7] Xiao L, Gao F. A comprehensive review of the development of adaptive cruise control systems. Vehicle Syst Dyn, 2010, 48: 1167-1192 CrossRef ADS Google Scholar

[8] He D, Shi Y, Li H. Multiobjective predictive cruise control for connected vehicle systems on urban conditions with InPA?§QP. Optim Control Appl Meth, 2019, 40: 479-498 CrossRef Google Scholar

[9] He D, Qiu T, Luo R. Fuel efficiency-oriented platooning control of connected nonlinear vehicles: A distributed economic MPC approach. Asian J Control, 2019, 80 CrossRef Google Scholar

[10] Schakel W J, Arem B V, Netten B D. Effects of cooperative adaptive cruise control on traffic flow stability. In: Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, 2010. 759--764. Google Scholar

[11] van Arem B, van Driel C J G, Visser R. The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics. IEEE Trans Intell Transp Syst, 2006, 7: 429-436 CrossRef Google Scholar

[12] Seiler P, Pant A, Hedrick K. Disturbance Propagation in Vehicle Strings. IEEE Trans Automat Contr, 2004, 49: 1835-1841 CrossRef Google Scholar

[13] Hedrick Professor J K, Swaroop Doctoral C D. Dynamic Coupling in Vehicles Under Automatic Control. Vehicle Syst Dyn, 1994, 23: 209-220 CrossRef Google Scholar

[14] Liang C Y, Peng H. Optimal Adaptive Cruise Control with Guaranteed String Stability. Vehicle Syst Dyn, 1999, 32: 313-330 CrossRef Google Scholar

[15] Naus G J L, Vugts R P A, Ploeg J. String-Stable CACC Design and Experimental Validation: A Frequency-Domain Approach. IEEE Trans Veh Technol, 2010, 59: 4268-4279 CrossRef Google Scholar

[16] Guo G, Yue W. Sampled-Data Cooperative Adaptive Cruise Control of Vehicles With Sensor Failures. IEEE Trans Intell Transp Syst, 2014, 15: 2404-2418 CrossRef Google Scholar

[17] Sheikholeslam S, Desoer C A. Longitudinal control of a platoon of vehicles with no communication of lead vehicle information: a system level study. IEEE Trans Veh Technol, 1993, 42: 546-554 CrossRef Google Scholar

[18] Song X, Lou X, Meng L. Time-delay feedback cooperative adaptive cruise control of connected vehicles by heterogeneous channel transmission. Measurement Control, 2019, 52: 369-378 CrossRef Google Scholar

[19] Oncu S, Ploeg J, van de Wouw N. Cooperative Adaptive Cruise Control: Network-Aware Analysis of String Stability. IEEE Trans Intell Transp Syst, 2014, 15: 1527-1537 CrossRef Google Scholar

[20] Xing H, Ploeg J, Nijmeijer H. Padé Approximation of Delays in Cooperative ACC Based on String Stability Requirements. IEEE Trans Intell Veh, 2016, 1: 277-286 CrossRef Google Scholar

[21] Huang Y, Na J, Wu X. Robust adaptive control for vehicle active suspension systems with uncertain dynamics. Trans Institute Measurement Control, 2018, 40: 1237-1249 CrossRef Google Scholar

[22] Tang T, Qi R, Jiang B. Adaptive nonlinear generalized predictive control for hypersonic vehicle with unknown parameters and control constraints. Proc Institution Mech Engineers Part G-J Aerospace Eng, 2019, 233: 510-532 CrossRef Google Scholar

[23] Bertsekas D P, Tsitsiklis J N, Siklis J T. Neuro-Dynamic Programming. Belmont: Athena Scientific, 1996. Google Scholar

[24] Powell W B. Approximate Dynamic Programming: Solving the Curses of Dimensionality. Hoboken: Wiley, 2007. Google Scholar

[25] Gao W, Jiang Z P. Adaptive Dynamic Programming and Adaptive Optimal Output Regulation of Linear Systems. IEEE Trans Automat Contr, 2016, 61: 4164-4169 CrossRef Google Scholar

[26] Zhou Y, Li D, Xi Y. Synthesis of model predictive control based on data-driven learning. Sci China Inf Sci, 2020, 63: 189204 CrossRef Google Scholar

[27] Jian Wang , Xin Xu , Daxue Liu . Self-Learning Cruise Control Using Kernel-Based Least Squares Policy Iteration. IEEE Trans Contr Syst Technol, 2014, 22: 1078-1087 CrossRef Google Scholar

[28] Gao W, Jiang Z P, Ozbay K. Data-Driven Adaptive Optimal Control of Connected Vehicles. IEEE Trans Intell Transp Syst, 2017, 18: 1122-1133 CrossRef Google Scholar

[29] Zhu Y, Zhao D, Zhong Z. Adaptive Optimal Control of Heterogeneous CACC System With Uncertain Dynamics. IEEE Trans Contr Syst Technol, 2019, 27: 1772-1779 CrossRef Google Scholar

[30] Hou Z S, Wang Z. From model-based control to data-driven control: Survey, classification and perspective. Inf Sci, 2013, 235: 3-35 CrossRef Google Scholar

[31] Wang Z, Wu G, Barth M J. Developing a Distributed Consensus-Based Cooperative Adaptive Cruise Control System for Heterogeneous Vehicles with Predecessor Following Topology. J Adv Transpation, 2017, 2017(3): 1-16 CrossRef Google Scholar

[32] Darbha S, Konduri S, Pagilla P R. Benefits of V2V Communication for Autonomous and Connected Vehicles. IEEE Trans Intell Transp Syst, 2019, 20: 1954-1963 CrossRef Google Scholar

[33] Guo H, Liu H, Yin Z. Modular scheme for four-wheel-drive electric vehicle tire-road force and velocity estimation. IET Intelligent Transp Syst, 2019, 66: 551-562 CrossRef Google Scholar

[34] Guo H, Chen H, Xu F. Implementation of EKF for Vehicle Velocities Estimation on FPGA. IEEE Trans Ind Electron, 2013, 60: 3823-3835 CrossRef Google Scholar

[35] Guo G, Yue W. Autonomous Platoon Control Allowing Range-Limited Sensors. IEEE Trans Veh Technol, 2012, 61: 2901-2912 CrossRef Google Scholar

[36] Vrabie D, Pastravanu O, Abu-Khalaf M. Adaptive optimal control for continuous-time linear systems based on policy iteration. Automatica, 2009, 45: 477-484 CrossRef Google Scholar

[37] Kleinman D. On an iterative technique for Riccati equation computations. IEEE Trans Automat Contr, 1968, 13: 114-115 CrossRef Google Scholar

[38] Vamvoudakis K G, Lewis F L. Multi-player non-zero-sum games: Online adaptive learning solution of coupled Hamilton-Jacobi equations. Automatica, 2011, 47: 1556-1569 CrossRef Google Scholar

[39] Jiang Y, Jiang Z P. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. Automatica, 2012, 48: 2699-2704 CrossRef Google Scholar

[40] Lin Y, McPhee J, Azad N L. Longitudinal dynamic versus kinematic models for car-following control using deep rein-forcement learning. In: Proceedings of IEEE Intelligent Transportation Systems Conference, Auckland, 2019. 1504--1510. Google Scholar

[41] Lin Y, McPhee J, Azad N L. Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. 2019,. arXiv Google Scholar

Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有

京ICP备14028887号-23       京公网安备11010102003388号