Model predictive control for autonomous ground vehicles: a review

Abstract

This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

Keywords

Model predictive control / Autonomous intelligent system / Autonomous ground vehicle

References

[1] FritzW., MartinezR. G., BlanquéJ. R., AdobbatiR., RamaA., SarnoM.. The autonomous intelligent system. Robot. Auton. Syst., 1989, 5(2):109-125

[2] HuangY., DingH., ZhangY., WangH., CaoD., XuN., HuC.. A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Trans. Ind. Electron., 2019, 67(2):1376-1386

[3] RawlingsJ. B., MayneD. Q., DiehlM. M.. Model Predictive Control: Theory, Computation, and Design, 2017 Santa Barbarra, California, USA Nob Hill Publishing

[4] GrüneL., PannekJ.. Nonlinear Model Predictive Control, 2017 Cham Springer International Publishing

[5] MayneD. Q., RawlingsJ. B., RaoC. V., ScokaertP. O. M.. Constrained model predictive control: Stability and optimality. Automatica, 2000, 36(6):789-814

[6] FontesF. A. C. C.. A general framework to design stabilizing nonlinear model predictive controllers. Syst. Control Lett., 2001, 42(2):127-143

[7] ChenH., AllgöwerF.. A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica, 1998, 34(10):1205-1217

[8] GrüneL.. Analysis and design of unconstrained nonlinear MPC dchemes for ginite and infinite dimensional systems. SIAM J. Control. Optim., 2009, 48(2):1206-1228

[9] GrüneL.. NMPC without terminal constraints. IFAC Proc. Vol., 2012, 45(17):1-13

[10] RebleM., AllgöwerF.. Unconstrained model predictive control and suboptimality estimates for nonlinear continuous-time systems. Automatica, 2012, 48(8):1812-1817

[11] YuS., RebleM., ChenH., AllgöwerF.. Inherent robustness properties of quasi-infinite horizon nonlinear model predictive control. Automatica, 2014, 50(9):2269-2280

[12] PannocchiaG., RawlingsJ. B., WrightS. J.. Conditions under which suboptimal nonlinear MPC is inherently robust. Syst. Control Lett., 2011, 60(9):747-755

[13] MayneD. Q., SeronM. M., RakovićS. V.. Robust model predictive control of constrained linear systems with bounded disturbances. Automatica, 2005, 41(2):219-224

[14] ChenH., SchererC. W.. Moving horizon H _∞ control with performance adaptation for constrained linear systems. Automatica, 2006, 42(6):1033-1040

[15] LimonD., AlamoT., RaimondoD. M., de La PeñaD. M., BravoJ. M., FerramoscaA., CamachoE. F.. MagniL., RaimondoD. R., AllgöwerF.. Input-to-state stability: A unifying framework for robust model predictive control. Nonlinear Model Predictive Control, 2009 Berlin Springer 1-26

[16] RakovicS. V., KouvaritakisB., CannonM., PanosC., FindeisenR.. Parameterized tube model predictive control. IEEE Trans. Autom. Control, 2012, 57(11):2746-2761

[17] YuS., MaierC., ChenH., AllgöwerF.. Tube MPC scheme based on robust control invariant set with application to Lipschitz nonlinear systems. Syst. Control Lett., 2013, 62: 194-200

[18] KöhlerJ., SolopertoR., MüllerM. A., AllgöwerF.. A computationally efficient robust model predictive control framework for uncertain nonlinear systems. IEEE Trans. Autom. Control, 2021, 66(2):794-801

[19] LimonD., AlvaradoI., AlamoT., CamachoE. F.. MPC for tracking piecewise constant references for constrained linear systems. Automatica, 2008, 44(9):2382-2387

[20] FerramoscaA., LimonD., AlvaradoI., AlamoT., CamachoE. F.. MPC for tracking with optimal closed-loop performance. Automatica, 2009, 45(8):1975-1978

[21] FaulwasserT., FindeisenR.. MagniL., RaimondoD. R., AllgöwerF.. Nonlinear model predictive path-following control. Nonlinear Model Predictive Control, 2009 Berlin Springer 335-343

[22] YuS., LiX., ChenH., AllgöwerF.. Nonlinear model predictive control for path following problems. Int. J. Robust Nonlinear Control, 2015, 25(8):1168-1182

[23] FaulwasserT., FindeisenR.. Nonlinear model predictive control for constrained output path following. IEEE Trans. Autom. Control, 2016, 61(4):1026-1039

[24] KöhlerJ., MüllerM. A., AllgöwerF.. A nonlinear tracking model predictive control scheme for dynamic target signals. Automatica, 2020, 118: 109030

[25] RawlingsJ. B., AmritR.. MagniL., RaimondoD. R., AllgöwerF.. Optimizing process economic performance using model predictive control. Nonlinear Model Predictive Control, 2009 Berlin Springer 119-138

[26] AngeliD., AmritR., RawlingsJ. B.. On average performance and stability of economic model predictive control. IEEE Trans. Autom. Control, 2012, 57(7):1615-1626

[27] CoulsonJ., LygerosJ., DorflerF.. Data-enabled predictive control: In the shallows of the DeePC. 18th European Control Conference (ECC), 2019 Piscataway, New Jersey IEEE 307-312

[28] BerberichJ., KöehlerJ., MüllerM. A., AllgöwerF.. Data-driven model predictive control with stability and robustness guarantees. IEEE Trans. Autom. Control, 2020, 66(4):1702-1717

[29] ChenM., ZengG., XieX.. Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems. J. Frankl. Inst., 2018, 355(17):8266-8295

[30] KöhlerP. N., MüllerM. A., PannekJ., AllgöwerF.. Distributed economic model predictive control for cooperative supply chain management using customer forecast information. IFAC J. Syst. Control, 2021, 15: 100125

[31] ZhangR., LiuA., YuL., ZhangW.. Distributed model predictive control based on Nash optimality for large scale irrigation systems. IFAC-PapersOnLine, 2015, 48(8):551-555

[32] KögelM., FindeisenR.. Cooperative distributed MPC using the alternating direction multiplier method. IFAC Proc. Vol., 2012, 45(15):445-450

[33] ConteC., SummersT., ZeilingerM. N., MorariM., JonesC. N.. Computational aspects of distributed optimization in model predictive control. IEEE 51st Annual Conference on Decision and Control (CDC), 2012, 2012 Piscataway, NJ IEEE

[34] R. Rostami, G. Costantini, D. Görges, Stabilizing distributed model predictive control using the consensus form of ADMM, (2019).

[35] StewartB. T., VenkatA. N., RawlingsJ. B., WrightS. J., PannocchiaG.. Cooperative distributed model predictive control. Syst. Control Lett., 2010, 59(8):460-469

[36] GrossD., StursbergO.. On the convergence rate of a Jacobi algorithm for Cooperative Distributed MPC. IEEE Conference on Decision and Control, 2013 Firenze IEEE 1508-1513

[37] ConteC., JonesC. N., MorariM., ZeilingerM. N.. Distributed synthesis and stability of cooperative distributed model predictive control for linear systems. Automatica, 2016, 69: 117-125

[38] RichardsA., HowJ. P.. Robust distributed model predictive control. Int. J. Control., 2007, 80(9):1517-1531

[39] GrüneL., WorthmannK.. JohanssonR., RantzerA.. A distributed NMPC scheme without stabilizing terminal constraints. Distributed Decision Making and Control, 2012 London Springer 261-287

[40] MüllerM. A., RebleM., AllgöwerF.. Cooperative control of dynamically decoupled systems via distributed model predictive control. Int. J. Robust Nonlinear Control, 2012, 22(12):1376-1397

[41] KöhlerP. N., MüllerM. A., AllgöwerF.. A distributed economic MPC framework for cooperative control under conflicting objectives. Automatica, 2018, 96: 368-379

[42] RaimondoD. M., MagniL., ScattoliniR.. Decentralized MPC of nonlinear systems: An input-to-state stability approach. Int. J. Robust Nonlinear Control, 2007, 17(17):1651-1667

[43] FrancoE., MagniL., ParisiniT., PolycarpouM. M., RaimondoD. M.. Cooperative constrained control of distributed agents with nonlinear dynamics and delayed information exchange: A stabilizing receding-horizon approach. IEEE Trans. Autom. Control, 2008, 53(1):324-338

[44] FarinaM., ScattoliniR.. Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems. Automatica, 2012, 48(6):1088-1096

[45] DunbarW. B., MurrayR. M.. Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 2006, 42(4):549-558

[46] MaestreJ. M., NegenbornR. R.. Distributed Model Predictive Control Made Easy, 2014 Dordrecht Springer Netherlands

[47] ChristofidesP. D., ScattoliniR., Muñoz de la PeñaD., LiuJ.. Distributed model predictive control: A tutorial review and future research directions. Comput. Chem. Eng., 2013, 51: 21-41

[48] ScattoliniR.. Architectures for distributed and hierarchical model predictive control – A review. J. Process Control, 2009, 19(5):723-731

[49] MüllerM. A., AllgöwerF.. Economic and distributed model predictive control: Recent developments in optimization-based control. SICE J. Control. Meas. Syst. Integr., 2017, 10(2):39-52

[50] Olfati-SaberR., FaxJ. A., MurrayR. M.. Consensus and cooperation in networked multi-agent systems. Proc. IEEE, 2007, 95(1):215-233

[51] Ferrari-TrecateG., GalbuseraL., MarciandiM. P. E., ScattoliniR.. Model predictive control schemes for consensus in multi-agent systems with single- and double-integrator dynamics. IEEE Trans. Autom. Control, 2009, 54(11):2560-2572

[52] ZhanJ., LiX.. Consensus of sampled-data multi-agent networking systems via model predictive control. Automatica, 2013, 49(8):2502-2507

[53] Q. Wang, Z. Duan, Y. Lv, Q. Wang, G. Chen, Distributed model predictive control for linear-quadratic performance and consensus state optimization of multiagent systems. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.3001347.

[54] LiH., YanW.. Receding horizon control based consensus scheme in general linear multi-agent systems. Automatica, 2015, 56: 12-18

[55] ChengZ., ZhangH. -T., FanM. -C., ChenG.. Distributed consensus of multi-agent systems with input constraints: A model predictive control approach. IEEE Trans. Circ. Syst. I: Regular Pap., 2014, 62(3):825-834

[56] T. H. Summers, J. Lygeros, in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). Distributed model predictive consensus via the alternating direction method of multipliers (IEEE, 2012), pp. 79–84.

[57] ZhanJ., JiangZ. -P., WangY., LiX.. Distributed model predictive consensus with self-triggered mechanism in general linear multiagent systems. IEEE Trans. Ind. Inform., 2018, 15(7):3987-3997

[58] ZhanJ., ChenY., AleksandrovA., LiX.. Robust distributed model predictive control based consensus of general linear multi-agent systems. 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 Sapporo IEEE 1-5

[59] Y. Su, Y. Shi, C. Sun, Distributed model predictive control for tracking consensus of linear multiagent systems with additive disturbances and time-varying communication delays. IEEE Trans. Cybern. (2019). https://doi.org/10.1109/TCYB.2019.2939732.

[60] CoppD. A., VamvoudakisK. G., HespanhaJ. P.. Distributed output-feedback model predictive control for multi-agent consensus. Syst. Control Lett., 2019, 127: 52-59

[61] JohanssonB., SperanzonA., JohanssonM., JohanssonK. H.. On decentralized negotiation of optimal consensus. Automatica, 2007, 44(4):1175-1179

[62] KeviczkyT., JohanssonK. H.. A study on distributed model predictive consensus. IFAC Proc. Vol., 2008, 41(2):1516-1521

[63] ShiX., CaoJ., HuangW.. Distributed parametric consensus optimization with an application to model predictive consensus problem. IEEE Trans. Cybern., 2017, 48(7):2024-2035

[64] LeeJ., KimJ. -S., SongH., ShimH.. A constrained consensus problem using MPC. Int. J. Control. Autom. Syst., 2011, 9(5):952

[65] HircheM., KöhlerP. N., MüllerM. A., AllgöwerF.. Distributed model predictive control for consensus of constrained heterogeneous linear systems. 2020 59th IEEE Conference on Decision and Control (CDC), 2020 Jeju IEEE 1248-1253

[66] JiJ., KhajepourA., MelekW. W., HuangY.. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans. Veh. Technol., 2016, 66(2):952-964

[67] RaffoG. V., GomesG. K., Normey-RicoJ. E., KelberC. R., BeckerL. B.. A predictive controller for autonomous vehicle path tracking. IEEE Trans. Intell. Transp. Syst., 2009, 10(1):92-102

[68] VekslerA., JohansenT. A., BorrelliF., RealfsenB.. Dynamic positioning with model predictive control. IEEE Trans. Control Syst. Technol., 2016, 24(4):1340-1353

[69] PčolkaM., žáčekováE., ČelikovskỳS., ŠebekM.. Toward a smart car: Hybrid nonlinear predictive controller with adaptive horizon. IEEE Trans. Control Syst. Technol., 2017, 26(6):1970-1981

[70] LiangY., nong LiY., KhajepourA., NiJ., ZhengL.. Holistic adaptive multi-model predictive control for the path following of 4WID autonomous vehicles. IEEE Trans. Veh. Technol., 2020, 70(1):69-81

[71] ShenC., ShiY., BuckhamB.. Path-following control of an AUV: A multiobjective model predictive control approach. IEEE Trans. Control Syst. Technol., 2018, 27(3):1334-1342

[72] ZhangW., WangZ., DruggeL., NybackaM.. Evaluating model predictive path following and yaw stability controllers for over-actuated autonomous electric vehicles. IEEE Trans. Veh. Technol., 2020, 69(11):12807-12821

[73] GuoH., ShenC., ZhangH., ChenH., JiaR.. 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. Inform., 2018, 14(9):4273-4283

[74] ChenY., HuC., WangJ.. Human-centered trajectory tracking control for autonomous vehicles with driver cut-in behavior prediction. IEEE Trans. Veh. Technol., 2019, 68(9):8461-8471

[75] Y. Chen, J. Wang, in 2019 American Control Conference (ACC). Trajectory tracking control for autonomous vehicles in different cut-in scenarios (IEEE, 2019), pp. 4878–4883.

[76] FranzeG., LuciaW.. A receding horizon control strategy for autonomous vehicles in dynamic environments. IEEE Trans. Control Syst. Technol., 2015, 24(2):695-702

[77] Heshmati-AlamdariS., KarrasG. C., MarantosP., KyriakopoulosK. J.. A robust predictive control approach for underwater robotic vehicles. IEEE Trans. Control Syst. Technol., 2019, 28(6):2352-2363

[78] GuoH., LiuF., XuF., ChenH., CaoD., JiY.. Nonlinear model predictive lateral stability control of active chassis for intelligent vehicles and its FPGA implementation. IEEE Trans. Syst. Man Cybern. Syst., 2017, 49(1):2-13

[79] YuanX., HuangG., ShiK.. Improved adaptive path following control system for autonomous vehicle in different velocities. IEEE Trans. Intell. Transp. Syst., 2019, 21(8):3247-3256

[80] RosoliaU., BorrelliF.. Learning how to autonomously race a car: A predictive control approach. IEEE Trans. Control Syst. Technol., 2019, 28(6):2713-2719

[81] S. Cheng, L. Li, X. Chen, J. Wu, H. da Wang, Model-predictive-control-based path tracking controller of autonomous vehicle considering parametric uncertainties and velocity-varying. IEEE Trans. Ind. Electron. (2020). https://doi.org/10.1109/TIE.2020.3009585.

[82] PengH., WangW., AnQ., XiangC., LiL.. Path tracking and direct yaw moment coordinated control based on robust MPC with the finite time horizon for autonomous independent-drive vehicles. IEEE Trans. Veh. Technol., 2020, 69(6):6053-6066

[83] LuanZ., ZhangJ., ZhaoW., WangC.. Trajectory tracking control of autonomous vehicle with random network delay. IEEE Trans. Veh. Technol., 2020, 69(8):8140-8150

[84] Z. Wang, J. Zha, J. Wang, Autonomous vehicle trajectory following: A flatness model predictive control approach with hardware-in-the-loop verification. IEEE Trans. Intell. Transp. Syst. (2020). https://doi.org/10.1109/TITS.2020.2987987.

[85] WangZ., ZhaJ., WangJ.. Flatness-based model predictive control for autonomous vehicle trajectory tracking. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019 Auckland IEEE 4146-4151

[86] FalconeP., BorrelliF., AsgariJ., TsengH. E., HrovatD.. Predictive active steering control for autonomous vehicle systems. IEEE Trans. Control Syst. Technol., 2007, 15(3):566-580

[87] AlcaláE., PuigV., QuevedoJ.. TS-MPC for autonomous vehicles including a TS-MHE-UIO estimator. IEEE Trans. Veh. Technol., 2019, 68(7):6403-6413

[88] HangP., LvC., XingY., HuangC., HuZ.. Human-like decision making for autonomous driving: A noncooperative game theoretic approach. IEEE Trans. Intell. Transp. Syst., 2020, 22(4):2076-2087

[89] HangP., LvC., HuangC., CaiJ., HuZ., XingY.. An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors. IEEE Trans. Veh. Technol., 2020, 69(12):14458-14469

[90] LiuC., LeeS., VarnhagenS., TsengH. E.. Path planning for autonomous vehicles using model predictive control. 2017 IEEE Intelligent Vehicles Symposium (IV), 2017 Los Angeles IEEE 174-179

[91] GuoH., CaoD., ChenH., SunZ., HuY.. Model predictive path following control for autonomous cars considering a measurable disturbance: implementation, testing, and verification. Mech. Syst. Signal Process., 2019, 118: 41-60

[92] HuangY., WangH., KhajepourA., DingH., YuanK., QinY.. A novel local motion planning framework for autonomous vehicles based on resistance network and model predictive control. IEEE Trans. Veh. Technol., 2019, 69(1):55-66

[93] JalalmaabM., FidanB., JeonS., FalconeP.. Guaranteeing persistent feasibility of model predictive motion planning for autonomous vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV), 2017 Los Angeles IEEE 843-848

[94] BabuM., TheerthalaR. R., SinghA. K., BaladhurgeshB., GopalakrishnanB., KrishnaK. M., MedasaniS.. Model predictive control for autonomous driving considering actuator dynamics. 2019 American Control Conference (ACC), 2019 Philadelphia IEEE 1983-1989

[95] ChengS., LiL., GuoH. -Q., ChenZ. -G., SongP.. Longitudinal collision avoidance and lateral stability adaptive control system based on MPC of autonomous vehicles. IEEE Trans. Intell. Transp. Syst., 2019, 21(6):2376-2385

[96] WeiskircherT., WangQ., AyalewB.. Predictive guidance and control framework for (semi-) autonomous vehicles in public traffic. IEEE Trans. Control Syst. Technol., 2017, 25(6):2034-2046

[97] WanN., ZhangC., VahidiA.. Probabilistic anticipation and control in autonomous car following. IEEE Trans. Control Syst. Technol., 2017, 27(1):30-38

[98] ChaiC., ZengX., WuX., WangX.. Safety evaluation of responsibility-sensitive safety (RSS) on autonomous car-following maneuvers based on surrogate safety measurements. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019 Auckland IEEE 175-180

[99] JeongY., YiK.. Target vehicle motion prediction-based motion planning framework for autonomous driving in uncontrolled intersections. IEEE Trans. Intell. Transp. Syst., 2021, 22(1):168-177

[100] DixitS., MontanaroU., DianatiM., OxtobyD., MizutaniT., MouzakitisA., FallahS.. Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC. IEEE Trans. Intell. Transp. Syst., 2019, 21(6):2310-2323

[101] SakhdariB., AzadN. L.. Adaptive tube-based nonlinear MPC for economic autonomous cruise control of plug-in hybrid electric vehicles. IEEE Trans. Veh. Technol., 2018, 67(12):11390-11401

[102] R. Firoozi, S. Nazari, J. Guanetti, R. O’Gorman, F. Borrelli, Safe adaptive cruise control with road grade preview and V2V communication. arXiv preprint arXiv:1810.09000 (2018).

[103] HundeA., AyalewB., WangQ.. Automated multi-object tracking for autonomous vehicle control in dynamically changing traffic. 2019 American Control Conference (ACC), 2019 Philadelphia IEEE 515-520

[104] F. Seccamonte, J. Kabzan, E. Frazzoli, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC). On maximizing lateral clearance of an autonomous vehicle in urban environments (IEEE, 2019), pp. 1819–1825.

[105] HajilooR., AbroshanM., KhajepourA., KasaiezadehA., ChenS. -K.. Integrated steering and differential braking for emergency collision avoidance in autonomous vehicles. IEEE Trans. Intell. Transp. Syst., 2021, 22(5):3167-3178

[106] TaherianS., MontanaroU., DixitS., FallahS.. Integrated trajectory planning and torque vectoring for autonomous emergency collision avoidance. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019 Auckland IEEE 2714-2721

[107] WangH., HuangY., KhajepourA., ZhangY., RasekhipourY., CaoD.. Crash mitigation in motion planning for autonomous vehicles. IEEE Trans. Intell. Transp. Syst., 2019, 20(9):3313-3323

[108] LiS. E., GuoQ., XuS., DuanJ., LiS., LiC., SuK.. Performance enhanced predictive control for adaptive cruise control system considering road elevation information. IEEE Trans. Intell. Veh., 2017, 2(3):150-160

[109] PinkovichB., RivlinE., RotsteinH.. Predictive driving in an unstructured scenario using the bundle adjustment algorithm. IEEE Trans. Control Syst. Technol., 2020, 29(1):342-352

[110] ShaoY., SunZ.. Optimal vehicle speed and gear position control for connected and autonomous vehicles. 2019 American Control Conference (ACC), 2019 Philadelphia IEEE 545-550

[111] KarlssonJ., MurgovskiN., SjöbergJ.. Computationally efficient autonomous overtaking on highways. IEEE Trans. Intell. Transp. Syst., 2019, 21(8):3169-3183

[112] ZhangQ., FilevD., TsengH. E., SzwabowskiS., LangariR.. A game theoretic four-stage model predictive controller for highway driving. 2019 American Control Conference (ACC), 2019 Philadelphia IEEE 1375-1381

[113] ZhangQ., LangariR., TsengH. E., FilevD., SzwabowskiS., CoskunS.. A game theoretic model predictive controller with aggressiveness estimation for mandatory lane change. IEEE Trans. Intell. Veh., 2019, 5(1):75-89

[114] BaoN., YangD., CarballoA., ÖzgünerÜ., TakedaK.. Personalized safety-focused control by minimizing subjective risk. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019 Auckland IEEE 3853-3858

[115] KimH., ChoJ., KimD., HuhK.. Intervention minimized semi-autonomous control using decoupled model predictive control. 2017 IEEE Intelligent Vehicles Symposium (IV), 2017 Los Angeles IEEE 618-623

[116] LiuJ., GuoH., SongL., DaiQ., ChenH.. Driver-automation shared steering control for highly automated vehicles. Sci. China Inform. Sci., 2020, 63(9):1-16

[117] WangH., HuangY., KhajepourA., CaoD., LvC.. Ethical decision-making platform in autonomous vehicles with lexicographic optimization based model predictive controller. IEEE Trans. Veh. Technol., 2020, 69(8):8164-8175

[118] P. Kavathekar, Y. Chen, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 54808. Vehicle platooning: A brief survey and categorization, (2011), pp. 829–845.

[119] S. E. Li, Y. Zheng, K. Li, J. Wang, in IEEE Intelligent Vehicles Symmposium. An overview of vehicular platoon control under the four-component framework (Seoul, Korea, 2015), pp. 286–291.

[120] PloegJ., Van De WouwN., NijmeijerH.. Lp string stability of cascaded systems: Application to vehicle platooning. IEEE Trans. Control Syst. Technol., 2013, 22(2):786-793

[121] ZhangJ., WangF. -Y., WangK., LinW. -H., XuX., ChenC.. Data-driven intelligent transportation systems: A survey. IEEE Trans. Intell. Transp. Syst., 2011, 12(4):1624-1639

[122] ZhengY.. Dynamic modeling and distributed control of vehicular platoon under the four-component framework. Master Thesis, 2015 China Tsinghua University

[123] TeoR., StipanovicD. M., TomlinC. J.. Decentralized spacing control of a string of multiple vehicles over lossy datalinks, vol. 1. 42nd IEEE International Conference on Decision and Control (IEEE Cat. No. 03CH37475), 2003 Maui IEEE 682-687

[124] ZhouJ., PengH.. Range policy of adaptive cruise control vehicles for improved flow stability and string stability. IEEE Trans. Intell. Transp. Syst., 2005, 6(2):229-237

[125] G. J. Naus, R. P. Vugts, J. Ploeg, DeMolengraft van M. J., M. Steinbuch, String-stable CACC design and experimental validation: A frequency-domain approach. IEEE Trans. Veh. Technol.59(9), 4268–4279 (2010).

[126] ShladoverS. E., DesoerC. A., HedrickJ. K., TomizukaM., WalrandJ., ZhangW. -B., McMahonD. H., PengH., SheikholeslamS., McKeownN.. Automated vehicle control developments in the PATH program. IEEE Trans. Veh. Technol., 1991, 40(1):114-130

[127] F. Browand, J. McArthur, C. Radovich, Fuel saving achieved in the field test of two tandem trucks (UC Berkeley: California Partners for Advanced Transportation Technology, 2004). Retrieved from https://escholarship.org/uc/item/29v570mm.

[128] TsugawaS., KatoS., AokiK.. An automated truck platoon for energy saving. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011 San Francisco IEEE 4109-4114

[129] ChanE.. SARTRE automated platooning vehicles. Towards Innovative Freight and Logistics, 2016, 2: 137-150

[130] C. Bergenhem, Q. Huang, A. Benmimoun, T. Robinson, in 17th World Congress on Intelligent Transport Systems. Challenges of platooning on public motorways, (2010), pp. 1–12.

[131] C. Bergenhem, S. Shladover, E. Coelingh, C. Englund, S. Tsugawa, in Proceedings of the 19th ITS World Congress, Oct 22-26. Overview of platooning systems (Vienna, 2012).

[132] PloegJ., ShladoverS., NijmeijerH., van de WouwN.. Introduction to the special issue on the 2011 grand cooperative driving challenge. IEEE Trans. Intell. Transp. Syst., 2012, 13(3):989-993

[133] S. E. Li, Y. Zheng, K. Li, L. Wang, H. Zhang, Platoon control of connected vehicles from a networked control perspective: Literature review, component modeling, and controller synthesis. IEEE Trans. Veh. Technol. (2017). https://doi.org/10.1109/TVT.2017.2723881.

[134] Ferrari-TrecateG., GalbuseraL., MarciandiM. P. E., ScattoliniR.. Model predictive control schemes for consensus in multi-agent systems with single-and double-integrator dynamics. IEEE Trans. Autom. Control, 2009, 54(11):2560-2572

[135] ZhuY., ZhuF.. Distributed adaptive longitudinal control for uncertain third-order vehicle platoon in a networked environment. IEEE Trans. Veh. Technol., 2018, 67(10):9183-9197

[136] LiS. E., ZhengY., LiK., WuY., HedrickJ. K., GaoF., ZhangH.. Dynamical modeling and distributed control of connected and automated vehicles: Challenges and opportunities. IEEE Intell. Transp. Syst. Mag., 2017, 9(3):46-58

[137] GuoH., LiuJ., DaiQ., ChenH., WangY., ZhaoW.. A distributed adaptive triple-step nonlinear control for a connected automated vehicle platoon with dynamic uncertainty. IEEE Internet Things J., 2020, 7(5):3861-3871

[138] ZhengY., LiS. E., WangJ., LiK., et al.. Influence of information flow topology on closed-loop stability of vehicle platoon with rigid formation. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014 Qingdao IEEE 2094-2100

[139] MahmudM., HossainM., PotaH., OoA.. Robust nonlinear distributed controller design for active and reactive power sharing in islanded microgrids. IEEE Trans. Energy Convers., 2014, 29(4):893-903

[140] TahaeiH., SallehR. B., Ab RazakM. F., KoK., AnuarN. B.. Cost effective network flow measurement for software defined networks: A distributed controller scenario. IEEE Access, 2018, 6: 5182-5198

[141] OktianY. E., LeeS., LeeH., LamJ.. Distributed SDN controller system: A survey on design choice. Comput. Netw., 2017, 121: 100-111

[142] PetersA. A., MiddletonR. H., MasonO.. Leader tracking in homogeneous vehicle platoons with broadcast delays. Automatica, 2014, 50(1):64-74

[143] LinF., FardadM., JovanovićM. R.. Algorithms for leader selection in stochastically forced consensus networks. IEEE Trans. Autom. Control, 2014, 59(7):1789-1802

[144] F. L. Lewis, H. Zhang, K. Hengster-Movric, A. Das, Cooperative Control of Multi-agent Systems: Optimal and Adaptive Design Approaches (Springer, 2013).

[145] ZhengY., LiS. E., LiK., BorrelliF., HedrickJ. K.. Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies. IEEE Trans. Control Syst. Technol., 2016, 25(3):899-910

[146] ZhengY., LiS. E., WangJ., CaoD., LiK.. Stability and scalability of homogeneous vehicular platoon: Study on the influence of information flow topologies. IEEE Trans. Intell. Transp. Syst., 2015, 17(1):14-26

[147] GhasemiA., RouhiS.. A safe stable directional vehicular platoon. Proc. Inst. Mech. Eng. D J. Automob. Eng., 2015, 229(8):1083-1093

[148] KnornS., DonaireA., AgüeroJ. C., MiddletonR. H.. Passivity-based control for multi-vehicle systems subject to string constraints. Automatica, 2014, 50(12):3224-3230

[149] MaximA., IonescuC. M., CaruntuC. F., LazarC., De KeyserR.. Reference tracking using a non-cooperative distributed model predictive control algorithm. IFAC-PapersOnLine, 2016, 49(7):1079-1084

[150] GeM. -F., GuanZ. -H., HuB., HeD. -X., LiaoR. -Q.. Distributed controller–estimator for target tracking of networked robotic systems under sampled interaction. Automatica, 2016, 69: 410-417

[151] GuoX., WangJ., LiaoF., TeoR. S. H.. Distributed adaptive integrated-sliding-mode controller synthesis for string stability of vehicle platoons. IEEE Trans. Intell. Transp. Syst., 2016, 17(9):2419-2429

[152] KianfarR., FalconeP., FredrikssonJ.. A receding horizon approach to string stable cooperative adaptive cruise control. 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011 Washington IEEE 734-739

[153] PloegJ., ShuklaD. P., van de WouwN., NijmeijerH.. Controller synthesis for string stability of vehicle platoons. IEEE Trans. Intell. Transp. Syst., 2013, 15(2):854-865

[154] LiS., LiK., RajamaniR., WangJ.. Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans. Control Syst. Technol., 2010, 19(3):556-566

[155] DunbarW. B., CaveneyD. S.. Distributed receding horizon control of vehicle platoons: Stability and string stability. IEEE Trans. Autom. Control, 2011, 57(3):620-633

[156] ShakouriP., OrdysA.. Nonlinear model predictive control approach in design of adaptive cruise control with automated switching to cruise control. Control. Eng. Pract., 2014, 26: 160-177

[157] FaxJ. A., MurrayR. M.. Information flow and cooperative control of vehicle formations. IEEE Trans. Autom. Control, 2004, 49(9):1465-1476

[158] LiH., ShiY., YanW.. Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed γ-gain stability. Automatica, 2016, 68: 148-154

[159] LuL., SongX., HeD., ChenQ.. Stability and fuel economy of nonlinear vehicle platoons: A distributed economic MPC approach. 2018 37th Chinese Control Conference (CCC), 2018 Wuhan IEEE 7678-7683

[160] HeD., QiuT., LuoR.. Fuel efficiency-oriented platooning control of connected nonlinear vehicles: A distributed economic MPC approach. Asian J. Control., 2020, 22(4):1628-1638

[161] AlvarezL., HorowitzR., ToyC. V.. Multi-destination traffic flow control in automated highway systems. Transp. Res. C Emerg. Technol., 2003, 11(1):1-28

[162] DaoT., ClarkC. M., HuissoonJ. P.. Distributed platoon assignment and lane selection for traffic flow optimization. 2008 IEEE Intelligent Vehicles Symposium, 2008 Eindhoven IEEE 739-744

[163] XavierP., PanY. -J.. A practical PID-based scheme for the collaborative driving of automated vehicles. Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conference, 2009 Shanghai IEEE 966-971

[164] RaiR., SharmaB., VanualailaiJ.. Real and virtual leader-follower strategies in lane changing, merging and overtaking maneuvers. 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2015 Nadi IEEE 1-12

[165] GoliM., EskandarianA.. Evaluation of lateral trajectories with different controllers for multi-vehicle merging in platoon. 2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014 Vienna IEEE 673-678

[166] YangC., KuramiK.. Longitudinal guidance and control for the entry of vehicles onto automated highways. Proceedings of 32nd IEEE Conference on Decision and Control, 1993 San Antonio IEEE 1891-1896

[167] UnoA., SakaguchiT., TsugawaS.. A merging control algorithm based on inter-vehicle communication. Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No. 99TH8383), 1999 Tokyo IEEE 783-787

[168] PueboobpaphanR., LiuF., van AremB.. The impacts of a communication based merging assistant on traffic flows of manual and equipped vehicles at an on-ramp using traffic flow simulation. 13th International IEEE Conference on Intelligent Transportation Systems, 2010 Funchal IEEE 1468-1473

[169] ChenH., GuoL., DingH., LiY., GaoB.. Real-time predictive cruise control for eco-driving taking into account traffic constraints. IEEE Trans. Intell. Transp. Syst., 2018, 20(8):2858-2868

[170] ChuH., GuoL., GaoB., ChenH., BianN., ZhouJ.. Predictive cruise control using high-definition map and real vehicle implementation. IEEE Trans. Veh. Technol., 2018, 67(12):11377-11389

[171] P. Wang, H. Liu, L. Guo, L. Zhang, H. Ding, H. Chen, Design and experimental verification of real-time nonlinear predictive controller for improving the stability of production vehicles. IEEE Trans. Control Syst. Technol. (2020).

Funding

Major Research Plan(61790564); National Natural Science Foundation of China(U1964202); Deutsche Forschungsgemeinschaft (DE)(EXC 2075 - 390740016)