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

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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.

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Abbreviations

a h :

acceleration of host vehicle, m/s2

a min :

minimum acceleration limit of host vehicle, m/s2

a max :

maximum acceleration limit of host vehicle, m/s2

a p :

acceleration of preceding vehicle, m/s2

b :

constrains of system for input and output

C u :

parameter matrix of constrains

d :

relative distance between preceding and host vehicle, m

d des :

Desired relative distance, m

d s :

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

S x , I, S d , S u :

matrix parameter of predicted output performance vector

T h :

constant-time headway, s

u :

control input

v h :

velocity of host vehicle, m/s

w u :

corresponding weight of input increment

w d :

corresponding weight of spacing error

w v :

corresponding weight of relative velocity error wa

k ka :

corresponding weight of acceleration

Y p :

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

Δu min :

minimum incremental limit of control input, m/s2

Δu max :

maximum incremental limit of control input, m/s2

Δx :

change of system state variable

Δρ :

change of system disturbance

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Authors and Affiliations

  1. Department of Electromechanical Engineering, University of Macau, Macau, 999078, China

    R. C. Zhao, P. K. Wong & J. Zhao

  2. School of Mechanical and Automotive Engineering, South China University of Technology, Beijing, 510641, China

    Z. C. Xie

Authors
  1. R. C. Zhao
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  2. P. K. Wong
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  3. Z. C. Xie
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  4. J. Zhao
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Correspondence to P. K. Wong.

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Zhao, R.C., Wong, P.K., Xie, Z.C. et al. Real-time weighted multi-objective model predictive controller for adaptive cruise control systems. Int.J Automot. Technol. 18, 279–292 (2017). https://doi.org/10.1007/s12239-017-0028-2

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  • DOI: https://doi.org/10.1007/s12239-017-0028-2

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