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Vehicle adaptive cruise control design with optimal switching between throttle and brake

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Abstract

For vehicle adaptive cruise control (ACC) systems, the switching performance between throttle and brake determines the driving comfort, fuel consumption and service lives of vehicle mechanical components. In this paper, an ACC algorithm with the optimal switching control between throttle and brake is designed in model predictive control (MPC) framework. By introducing the binary integer variables, the dynamics of throttle and brake are integrated in one model expression for the controller design. Then the ACC algorithm is designed to satisfy not only safe car following, but also the optimal switching between throttle and brake, which leads to an online mixed integer quadratic programming solved by the nested two-loop method. The simulation results show that the proposed ACC algorithm meets the requirements of safe car following, outperforms the traditional algorithms by performing smoother responses, reducing the switching times between throttle and brake, and therefore improves driving comfort and fuel efficiency significantly.

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References

  1. A. Vahidi, A. Eskandarian. Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Transactions on Intelligent Transportation Systems, 2003, 4(3): 143–153.

    Article  Google Scholar 

  2. L. Xiao, G. Feng. A comprehensive review of development of adaptive cruise control systems. Vehicle System Dynamics, 2010, 48(10): 1167–1192.

    Article  Google Scholar 

  3. R. Rajamani. Vehicle Dynamics and Control. New York: Springer, 2006: 153–181.

    MATH  Google Scholar 

  4. J. C. Gerdes, J. K. Hedrick. Vehicle speed and spacing control via coordinated throttle and brake actuation. Control Engineer Practice, 1997, 5(11): 1607–1614.

    Article  Google Scholar 

  5. Y. Zhang, E. B. Kosmatopoulos, P. A. Ioannou, et al. Autonomous intelligent cruise control using front and back information for tight vehicle following maneuvers. IEEE Transactions on Vehicular Technology, 1999, 48(1): 319–328.

    Article  Google Scholar 

  6. J. Karl Hedrick, X. Y. Lu. Heavy-duty vehicle modeling and longitudinal contro. Vehicle System Dynamics, 2005, 43(9): 653–669.

    Article  Google Scholar 

  7. P. Ioannou, Z. Xu, S. Eckert, et al. Intelligent cruise control: theory and experiment. Proceedings of the 32nd IEEE Conference on Decision and Control. San Antonlo: IEEE, 1993: 1885–1890.

    Google Scholar 

  8. S. N. Huang, W. Ren. Vehicle longitudinal control using throttles and brakes. Robotics and Autonomous Systems, 1999, 26(4): 241–253.

    Article  Google Scholar 

  9. W. Beak, B. Song. Design and validation of a longitudinal via velocity and distance controller via hardware-in-the-loop simulation. International Journal of Automotive Technology, 2009, 10(1): 95–102.

    Article  Google Scholar 

  10. D. Swaroop, K. R. Rajagopal. A review of constant time headway policy for automatic vehicle following. IEEE Intelligent Transportation Systems Conference, 2001: 65–69.

  11. Y. Kyongsu, J. Chung. Nonlinear brake control for vehicle CW/CA systems. IEEE/ASME Transactions on Mechatronics, 2001, 6(1): 17–25.

    Article  Google Scholar 

  12. C. E. Garcia, D. M. Prett, M. Morari. Model predictive control theory and practice—A survey. Automatica, 1989, 25(3): 335–348.

    Article  MATH  Google Scholar 

  13. A. Beccuti, T. Geyer, M. Morari. A hybrid system approach to power systems voltage control. Proceedings of the 44th IEEE Conference Decision Control. Seville: IEEE, 2005: 6774–6779.

    Chapter  Google Scholar 

  14. X. Chen, Z. Li, J. Yang, et al. Nested tabu search (TS) and sequential quadratic programming (SQP) method, combined with adaptive model reformulation for heat exchanger network synthesis. Industrial and Engineering Chemistry Research, 2008, 47(7): 2320–2330.

    Article  MathSciNet  Google Scholar 

  15. M. Clerc, J. Kennedy. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58–73.

    Article  Google Scholar 

  16. Y. Del Valle, G. K Venayagamoorthy, S. Mohagheghi, et al. Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 2008, 12(2): 171–195.

    Article  Google Scholar 

  17. S. Boyd, L. Vandenberghe. Convex Optimization. New York: Cambridge University, 2004.

    MATH  Google Scholar 

  18. M. Barth, G. Scora, T. Younglove. Modal emissions model for heavyduty diesel vehicles. The 83rd Annual Meeting of the Transportation-Research-Board. Washington, DC: TRB, 2004: 10–20.

    Google Scholar 

  19. V. L. Baghwar, W. L Garrard, R. Rajamani. Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Transactions on Vehicular Technology, 2004, 53(5): 1573–1585.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Transport and Communication, Shanghai Maritime University, Shanghai, 200135, China

    Lihua Luo

  2. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou Zhejiang, 310027, China

    Ping Li & Hui Wang

Authors
  1. Lihua Luo
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  2. Ping Li
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  3. Hui Wang
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Corresponding author

Correspondence to Lihua Luo.

Additional information

This work was supported by Science & Technology Program of Shanghai Maritime University (No. 20120077).

Lihua LUO received her B.S. degree in Electrical Engineering and Automation from Xidian University in 2006, and was recommended to Zhejiang University to purse the Ph.D. degree at the same year. She received Ph.D. degree in Control Science and Engineering in 2011. Currently, she is a lecturer in the college of Transport and Communication in Shanghai Maritime University. Her research includes vehicle adaptive cruise control, traffic flow modeling and parameters calibration.

Ping LI received his Ph.D. degree in Industry Automation from Zhejiang University in 1988. From 1988 to 1990, he was a post doctor in Laboratory of Mechanical Fluid Power Driven and Control of Zhejiang University. From 1995 to 1996, he joined in University of British Columbia (UBC) as a senior visiting scholar. He obtained the honor of ‘Chinese Doctor with outstanding contribution’ from State Education Commission and Academic Degree Commission of State Council in 1990. Currently, he is a professor in Zhejiang University. His research includes modeling, control and optimization of complex system, intelligent transportation system, navigation and control of micro-unmanned helicopters.

HuiWANG received her B.S. degree and M.S. degree in Automation from Zhejiang University in 1982 and 1988, respectively. From 1989, She joined in Zhejiang University, and became a professor in 2002. Her research includes intelligent transportation system, modeling and optimization of complicated industry, and application of computer control system.

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Luo, L., Li, P. & Wang, H. Vehicle adaptive cruise control design with optimal switching between throttle and brake. J. Control Theory Appl. 10, 426–434 (2012). https://doi.org/10.1007/s11768-012-0319-0

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  • DOI: https://doi.org/10.1007/s11768-012-0319-0

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