access icon openaccess Robust control of vehicle multi-target adaptive cruise based on model prediction

This is an open access article published by the IET in partnership with Shenzhen University under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 10/07/2020, Accepted 01/10/2020, Published 06/10/2020

On the issue of low utilisation and acceptance of current adaptive cruise control (ACC), a multi-objective adaptive cruise control (MO-ACC) algorithm is developed in this study. Based on model predictive control theory, comprehensively considering the coordination among various conflicting objectives, the decision of desired longitudinal acceleration is transformed into online quadratic programming (QP) problem. In order to compensate for prediction error caused by modelling mismatch, the robustness of control system is improved by introducing an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non-feasible solution owing to hard constraints during the process of optimisation. Further, under different work conditions, the focusing performance index along with constraint space varies, and therefore different ACC modes are established to meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as slack relaxation. The simulations show that under the combined work conditions of the preceding vehicle, the following vehicle can realise seamless switching among various working modes, and also is able to achieve the good expectation during vehicle following, which will help to enhance the adaptability of the ACC system to the complex road traffic environment.

Inspec keywords: robust control; performance index; control system synthesis; predictive control; road traffic; vehicles; road vehicles; optimisation; adaptive control; vehicle dynamics; feedback; quadratic programming

Other keywords: prediction error; vector management method; model prediction; adaptability; model predictive control theory; different ACC modes; vehicle multitarget adaptive cruise; nonfeasible solution; desired longitudinal acceleration; constraint space varies; robust control; multiobjective adaptive cruise control; different work conditions; preceding vehicle; hard constraints; current adaptive cruise control; online quadratic programming problem; combined work conditions; modelling mismatch; error feedback correction mechanism; ACC system; low utilisation; control system; focusing performance index; slightly adjusting performance index

Subjects: Self-adjusting control systems; Control system analysis and synthesis methods; Optimal control; Optimisation techniques; Vehicle mechanics; Stability in control theory; Optimisation techniques; Road-traffic system control

References

    1. 1)
      • 22. Boer, E., Nicholas, W., Michael, M., et al: ‘Driver-model based assessment of behavioral adaptation’. JSAE Spring, Yokohama, Japan, 2005.
    2. 2)
      • 10. Li, S.E.: ‘Economy-oriented vehicle adaptive cruise control with coordinating multiple objectives function’, Veh. Syst. Dyn., 2013, 51, (1), pp. 1–17.
    3. 3)
      • 8. Wu, G., Guo, X., Zhang, L.: ‘Multi-objective robust adaptive cruise control algorithm design of vehicle following model’, J. Harbin Instit. Technol., 2016, 48, (1), pp. 80–86.
    4. 4)
      • 4. 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, (4), pp. 429–436.
    5. 5)
      • 24. Zeilinger, M.N., Morari, M., Jones, C.N.: ‘Soft constrained model predictive control with robust stability guarantees’, IEEE Trans. Autom. Control, 2014, 59, (5), pp. 1190–1202.
    6. 6)
      • 9. Li, S.E., Peng, H., Li, K., et al: ‘Minimum fuel control strategy in automated vehicle-following scenarios’, IEEE Trans. Veh. Technol., 2012, 61, (3), pp. 998–1007.
    7. 7)
      • 16. Li, D., Gao, H.: ‘A hardware platform framework for an intelligent vehicle based on a driving brain’, Engineering, 2018, 4, (2018), pp. 464–470.
    8. 8)
      • 3. Luo, L.: ‘Vehicle adaptive cruise control and the corresponding macroscopic traffic flow model’. Ph.D. dissertation, Zhejiang University, 2011.
    9. 9)
      • 17. Yi, K., Kwon, Y.D.: ‘Vehicle-to-vehicle distance and speed control using an electronic-vacuum booster’, Jsae Rev., 2001, 22, (4), pp. 403–412.
    10. 10)
      • 5. Dang, R., Wang, J., Li, S.E., et al: ‘Coordinated adaptive cruise control system with lane-change assistance’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 2373–2383.
    11. 11)
      • 13. Li, S., Li, K., Rajamani, R., et al: ‘Model predictive multi-objective vehicular adaptive cruise control’, IEEE Trans. Control Syst. Technol., 2011, 19, (3), pp. 556–566.
    12. 12)
      • 23. Zhang, L.: ‘Driver longitudinal behavior based forward collision warning system’. M.S. thesis, Dept. Automot. Eng., Tsinghua Univ., Tsinghua, China, 2006.
    13. 13)
      • 12. Li, S.E., Jia, Z., Li, K., et al: ‘Fast online computation of a model predictive controller and its application to fuel economy coriented adaptive cruise control’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (3), pp. 1199–1209.
    14. 14)
      • 14. Luo, L.: ‘Adaptive cruise control design with consideration of humans’ driving psychology’. IEEE Intelligent Control and Automation, Shenyang, China, 2014, pp. 2973–2978.
    15. 15)
      • 2. Graf, P.M., Bernardini, D., Esen, H., et al: ‘Multi-automated vehicle coordination using decoupled prioritized path planning for multi-lane one-and bi-directional traffic flow control’. IEEE Conf. on Decision and Control, ARIA Resort & Casino, Las Vegas, NV, USA., 2016, pp. 1582–1588.
    16. 16)
    17. 17)
      • 7. Yan, W.: ‘Study on adaptive cruise control algorithms imitating vehicle-following behaviors of drivers’. Ph.D. dissertation, Jilin University, 2016.
    18. 18)
      • 1. Gruyer, D., Pechberti, S., Glaser, S.: ‘Development of full speed range ACC with SiVIC, a virtual platform for ADAS prototyping, test and evaluation’. IEEE Intelligent Vehicles Symp., Gold Coast City, Australia, 2013, pp. 100–105.
    19. 19)
      • 20. Zhang, J., Ioannou, P.A.: ‘Longitudinal control of heavy trucks in mixed traffic: environmental and fuel economy considerations’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (1), pp. 92–104.
    20. 20)
    21. 21)
      • 19. Gao, H., Li, Z., Kang, Y., et al: ‘Adaptive fuzzy region-based control of euler-lagrange systems with kinematically singular configurations’, IEEE Trans. on Fuzzy Systems, 2020, DOI: 10.1109/TFUZZ.2020.2994991.
    22. 22)
    23. 23)
      • 18. Bageshwar, V.L., Garrard, W.L., Rajamani, R.: ‘Model predictive control of transitional maneuvers for adaptive cruise control vehicles’, IEEE Trans. Veh. Technol., 2004, 53, (5), pp. 1573–1585.
    24. 24)
      • 15. De Gelder, E., Care, I., Uittenbogaard, J., et al: ‘Towards personalised automated driving: prediction of preferred ACC behaviour based on manual driving’. IEEE Intelligent Vehicles Symp., Gotenburg, Sweden, 2016, pp. 1211–1216.
    25. 25)
      • 26. Sultan, B.: ‘The study of motorway operation using a microscopic simulation model’. Ph.D. dissertation, University of Southampton, 2000.
    26. 26)
      • 6. Gao, Z., Yan, W., Li, H.: ‘A vehicle adaptive cruise control algorithm based on simulating driver's multi-objective decision making’, Automobile Eng., 2015, 2015, (6), pp. 667–673.
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