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Real-time weighted multi-objective model predictive controller for adaptive cruise control systems

  • R. C. Zhao
  • P. K. Wong
  • Z. C. Xie
  • J. Zhao
Article

Abstract

In this paper, a novel spacing control law is developed for vehicles with adaptive cruise control (ACC) systems to perform spacing control mode. Rather than establishing a steady-state following distance behind a newly encountered vehicle to avoid collision, the proposed spacing control law based on model predictive control (MPC) further considers fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized in which a lower controller compensates for nonlinear longitudinal vehicle dynamics and enables to track the desired acceleration. The upper controller based on the proposed spacing control law is designed to compute the desired acceleration to maintain the control objectives. Moreover, the control objectives are then formulated into the model predictive control problem using acceleration and jerk limits as constrains. Furthermore, due to the complex driving conditions during in the transitional state, the traditional model predictive control algorithm with constant weight matrix cannot meet the requirement of improvement in the fuel economy and ride comfort. Therefore, a real-time weight tuning strategy is proposed to solve time-varying multi-objective control problems, where the weight of each objective can be adjusted with respect to different operating conditions. In addition, simulation results demonstrate that the ACC system with the proposed real-time weighted MPC (RW-MPC) can provide better performance than that using constant weight MPC (CW-MPC) in terms of fuel economy and ride comfort.

Key Words

Adaptive cruise control Model predictive control Real-time weight tuning Fuel economy Ride comfort 

Nomenclature

ah

acceleration of host vehicle, m/s2

amin

minimum acceleration limit of host vehicle, m/s2

amax

maximum acceleration limit of host vehicle, m/s2

ap

acceleration of preceding vehicle, m/s2

b

constrains of system for input and output

Cu

parameter matrix of constrains

d

relative distance between preceding and host vehicle, m

ddes

Desired relative distance, m

ds

Safety distance, m

F

fuel consumption, grams

G

parameter matrix of cost function

H

parameter matrix of cost function

m

control horizon

p

prediction horizon

P

disturbance vector

R

set-point vector

Sx, I, Sd, Su

matrix parameter of predicted output performance vector

Th

constant-time headway, s

u

control input

vh

velocity of host vehicle, m/s

wu

corresponding weight of input increment

wd

corresponding weight of spacing error

wv

corresponding weight of relative velocity error wa

kak

corresponding weight of acceleration

Yp

prediction performance vector

Γu

weight scale of input increment

Γy

weight matrix of output

Δd

errors of relative distance, m

ΔU

change of control vector

Δu

control input increment

Δv

errors of relative velocity, m/s

Δumin

minimum incremental limit of control input, m/s2

Δumax

maximum incremental limit of control input, m/s2

Δx

change of system state variable

Δρ

change of system disturbance

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References

  1. Ali, Z., Popov, A. A. and Charles, G. (2013). Model predictive control with constraints for a nonlinear adaptive cruise control vehicle model in transition manoeuvres. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 51, 6, 943–963.CrossRefGoogle Scholar
  2. Baghwar, V., Garrard, W. L. and Rajamani, R. (2004). Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Trans. Vehicular Technology 53, 5, 1573–1585.CrossRefGoogle Scholar
  3. Connolly, T. R. and Hedrick, J. K. (1999). Longitudinal transition maneuvers in an automated highway system. J. Dynamic Systems, Measurement and Control 121, 3, 471–478.CrossRefGoogle Scholar
  4. Drehmer, L. R. C., Casas, W. J. P. and Gomes, H. M. (2015). Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 53, 4, 449–474.CrossRefGoogle Scholar
  5. Fancher, P. and Bareket, Z. (1994). Evaluating headway control using range versus range-rate relationships. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 23, 1, 575–596.CrossRefGoogle Scholar
  6. Goggia, T., Sorniotti, A., De Novellis, L., Ferrara, A., Gruber, P., Theunissen, J., Steenbeke, D., Knauder, B. and Zehetner, J. (2015). Integral sliding mode for the torque-vectoring control of fully electric vehicles: Theoretical design and experimental assessment. IEEE Trans. Vehicular Technology 64, 5, 1701–1715.CrossRefGoogle Scholar
  7. Ioannou, P. and Xu, Z. (1994). Throttle and brake control system for automatic vehicle following. J. Intelligent Transportation Systems 1, 4, 345–377.Google Scholar
  8. Kamal, M., Murata, J. and Kawabe, T. (2009). Development of ecological driving system using model predictive control. Proc. ICROS-SICE Int. Joint Conf., 3549–3554.Google Scholar
  9. Kim, S., Tomizuka, M. and Cheng, K. H. (2010). Smooth motion control of the adaptive cruise control system with linear quadratic control with variable weights. Proc. ASME 2010 Dynamic Systems and Control Conf., 879–886.Google Scholar
  10. Kim, S. G., Tomizuka, M. and Cheng, K. H. (2012). Smooth motion control of the adaptive cruise control system by a virtual lead vehicle. Int. J. Automotive Technology 13, 1, 77–85.CrossRefGoogle Scholar
  11. Kato, S., Tsugawa, S., Tokuda, K., Matsui, T. and Fujii, H. (2002). Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications. IEEE Trans. Intelligent Transportation Systems 3, 3, 155–161.CrossRefGoogle Scholar
  12. Li, S. B., Li, K. Q., Rajamani, R. and Wang, J. Q. (2011). Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans. Control Systems Technology 19, 3, 556–566.CrossRefGoogle Scholar
  13. Luo, L., Liu, H., Li, P. and Wang, H. (2010). Model predictive control for adaptive cruise control with multiobjectives: Comfort, fuel-economy, safety and carfollowing. J. Zhejiang University-Science A 11, 3, 191–201.CrossRefzbMATHGoogle Scholar
  14. Li, S. E., Li, K. Q. and Wang, J. Q. (2013). Economyoriented vehicle adaptive cruise control with coordinating multiple objectives function. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 51, 1, 1–17.CrossRefGoogle Scholar
  15. Liang, C. Y. and Peng, H. (1999). Optimal adaptive cruise control with guaranteed string stability. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 32, 4–5, 313–330.CrossRefGoogle Scholar
  16. Lee, M. H., Park, H. G., Lee, S. H., Yoon, K. S. and Lee, K. S. (2013). An adaptive cruise control system for autonomous vehicles. Int. J. Precision Engineering and Manufacturing 14, 3, 373–380.CrossRefGoogle Scholar
  17. Martinez, J. J. and Canudas-De-Wit, C. (2007). A safe longitudinal control for adaptive cruise control and stopand-go scenarios. IEEE Trans. Control Systems Technology 15, 2, 246–258.CrossRefGoogle Scholar
  18. McDonough, K., Kolmanovsky, I., Filev, D., Yanakiev, D., Szwabowski, S. and Michelini, J. (2013). Stochastic dynamic programming control policies for fuel efficient vehicle following. Proc. American Control Conf., 1350–1355.Google Scholar
  19. McGehee, J. and Yoon, H. S. (2015). Optimal torque control of an integrated starter-generator using genetic algorithms. Proc. Institution of Mechanical Engineers Part D: J. Automobile Engineering 229, 7, 875–884.Google Scholar
  20. Naus, G. J., Ploeg, J., Van de Molengraft, M. J. G., Heemels, W. P. M. H. and Steinbuch, M. (2010). Design and implementation of parameterized adaptive cruise control: An explicit model predictive control approach. Control Engineering Practice 18, 8, 882–892.CrossRefzbMATHGoogle Scholar
  21. Rajamani, R. (2012). Vehicle Dynamics and Control. 2nd edn. Springer. New York, USA.CrossRefzbMATHGoogle Scholar
  22. Rajamani, R., Choi, S. B., Law, B. K., Hedrick, J. K., Prohaska, R. and Kretz, P. (2000). Design and experimental implementation of longitudinal control for a platoon of automated vehicles. J. Dynamic Systems, Measurement, and Control 122, 3, 470–476.CrossRefGoogle Scholar
  23. Sheikholeslam, S. and Desoer, C. A. (1993). Longitudinal control of a platoon of vehicles with no communication of lead vehicle information: A system level study. IEEE Trans. Vehicular Technology 42, 4, 546–554.CrossRefGoogle Scholar
  24. Swaroop, D. and Hedrick, J. K. (1996). String stability of interconnected systems. IEEE Trans. Automatic Control 41, 3, 349–357.MathSciNetCrossRefzbMATHGoogle Scholar
  25. Sun, M., Lewis, F. L. and Ge, S. S. (2004). Platoon-stable adaptive controller design. Proc. IEEE Conf. Decision and Control, 5481–5486.Google Scholar
  26. Santhanakrishnan, K. and Rajamani, R. (2003). On spacing policies for highway vehicle automation. IEEE Trans. Intelligent Transportation Systems 4, 4, 198–204.CrossRefGoogle Scholar
  27. Shakouri, P. and Ordys, A. (2014). Nonlinear model predictive control approach in design of adaptive cruise control with automated switching to cruise control. Control Engineering Practice, 26, 160–177.CrossRefGoogle Scholar
  28. Shakouri, P., Ordys, A. and Askari, M. R. (2012). Adaptive cruise control with stop&go function using the statedependent nonlinear model predictive control approach. ISA Trans. 51, 5, 622–631.CrossRefGoogle Scholar
  29. Xiao, L. Y. and Gao, F. (2010). A comprehensive review of the development of adaptive cruise control systems. Vehicle System Dynamics: Int. J. Vehicle Mechanics and Mobility 48, 10, 1167–1192.CrossRefGoogle Scholar
  30. Yi, K. and Kwon, Y. D. (2001). Vehicle-to-vehicle distance and speed control using an electronic-vacuum booster. JSAE Review 22, 4, 403–412.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Electromechanical EngineeringUniversity of MacauMacauChina
  2. 2.School of Mechanical and Automotive EngineeringSouth China University of TechnologyBeijingChina

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