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  • 标题:Parsimonious Cooperative Distributed MPC for Tracking Piece-Wise Constant Setpoints
  • 本地全文:下载
  • 作者:Matteo Razzanelli ; Gabriele Pannocchia
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2016
  • 卷号:49
  • 期号:7
  • 页码:520-525
  • DOI:10.1016/j.ifacol.2016.07.395
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Distributed Model Predictive Control refers to a class of predictive control architectures in which a number of local controllers manipulate a subset of inputs to regulate a subset of outputs composing the overall system. These controllers may cooperate to find an optimal control sequence that minimizes a global cost function, as in the case of Cooperative Distributed Model Predictive Control (CD-MPC). In this paper two linear CD-MPC algorithms for tracking are proposed. The aim of these controllers is to drive the outputs of the overall system to any admissible piece-wise constant set-point, satisfying input and state constraints. However, in the available literature this result is achieved by using a set of centralized variables that keep track of the global state of the system. In contrast, we develop novel CD-MPC approaches for tracking that rely on “as local as possible” information instead of the plant-wide information flow. These new control strategies reduce the required communication overhead, local computational demands, and are more scalable than CD-MPC algorithms available in the literature. We illustrate the main characteristics and benefits of the proposed approaches by means of a multiple evaporator process example.
  • 关键词:MPCreference trackingcooperative distributed controllarge-scale systems
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