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

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


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 



acceleration of host vehicle, m/s2


minimum acceleration limit of host vehicle, m/s2


maximum acceleration limit of host vehicle, m/s2


acceleration of preceding vehicle, m/s2


constrains of system for input and output


parameter matrix of constrains


relative distance between preceding and host vehicle, m


Desired relative distance, m


Safety distance, m


fuel consumption, grams


parameter matrix of cost function


parameter matrix of cost function


control horizon


prediction horizon


disturbance vector


set-point vector

Sx, I, Sd, Su

matrix parameter of predicted output performance vector


constant-time headway, s


control input


velocity of host vehicle, m/s


corresponding weight of input increment


corresponding weight of spacing error


corresponding weight of relative velocity error wa


corresponding weight of acceleration


prediction performance vector


weight scale of input increment


weight matrix of output


errors of relative distance, m


change of control vector


control input increment


errors of relative velocity, m/s


minimum incremental limit of control input, m/s2


maximum incremental limit of control input, m/s2


change of system state variable


change of system disturbance


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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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