• DocumentCode
    184357
  • Title

    Distributed Model Predictive Control for MLD systems: Application to freeway ramp metering

  • Author

    Ferrara, A. ; Sacone, Simona ; Siri, Silvia

  • Author_Institution
    Dept. of Electr., Univ. of Pavia, Pavia, Italy
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    5294
  • Lastpage
    5299
  • Abstract
    This paper deals with mixed logical dynamical systems controlled via model predictive schemes. For this class of systems a centralized MPC approach is firstly introduced, in which the finite horizon optimal control problem is a mixed-integer quadratic programming problem aiming at minimizing the deviations of the system variables from their equilibrium points. Since the application in real time of these MPC schemes is sometimes limited because of the high computational load necessary to solve the finite horizon optimal control problem, a distributed MPC scheme is proposed, characterized by two different and alternative algorithms. An important application is then introduced to assess the proposed approaches, i.e. ramp metering freeway traffic control. Referring to this applicative case, the two distributed control algorithms are compared, via simulation, with the centralized MPC scheme and with a completely decentralized one.
  • Keywords
    distributed control; integer programming; optimal control; predictive control; quadratic programming; road traffic control; MLD systems; centralized MPC approach; distributed MPC scheme; distributed model predictive control; equilibrium points; finite horizon optimal control problem; freeway ramp metering; mixed logical dynamical systems; mixed-integer quadratic programming problem; Clustering algorithms; Cost function; Decentralized control; Optimal control; Traffic control; Vectors; Control applications; Large scale systems; Predictive control for nonlinear systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2014
  • Conference_Location
    Portland, OR
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-3272-6
  • Type

    conf

  • DOI
    10.1109/ACC.2014.6859063
  • Filename
    6859063