Toward a Unifying Framework Blending Real-Time Optimization and Economic Model Predictive Control

被引:16
|
作者
Faulwasser, Timm [1 ]
Pannocchia, Gabriele [2 ]
机构
[1] KIT, Inst Automat & Appl Informat, D-76131 Karlsruhe, Germany
[2] Univ Pisa, Chem Engn Sect, Dept Civil & Ind Engn, I-56122 Pisa, Italy
关键词
MODIFIER-ADAPTATION METHODOLOGY; SET-POINT OPTIMIZATION; SYSTEM OPTIMIZATION; TURNPIKE; MPC; PERFORMANCE; ALGORITHM; OPERATION; DISSIPATIVITY; FORMULATION;
D O I
10.1021/acs.iecr.9b00782
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Nowadays, real-time optimization (RTO) and nonlinear as well as linear model predictive control (MPC) are standard methods in operation and process control systems. Hence there exists a good understanding of how to combine RTO and set point tracking MPC schemes. However, recently, there has been substantial progress in analyzing the properties of so-called economic MPC schemes. This paper proposes a conceptual framework to blend ideas from (output) modifier adaptation and offset-free economic MPC with recent results on economic MPC without terminal constraints. Specifically, we leverage recent insights into economic MPC based on turnpike and dissipativity properties of the underlying optimal control problem. Interestingly, the proposed scheme alleviates the need for a dedicated computation of steady-state targets by exploiting the turnpike property in the open-loop predictions. Two detailed simulation examples show that the proposed schemes deliver excellent performance, while being conceptually much simpler.
引用
收藏
页码:13583 / 13598
页数:16
相关论文
共 50 条
  • [41] Model-based real-time optimization of automotive gasoline blending operations
    Singh, A
    Forbes, JF
    Vermeer, PJ
    Woo, SS
    JOURNAL OF PROCESS CONTROL, 2000, 10 (01) : 43 - 58
  • [42] Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction
    Liao-McPherson, Dominic
    Nicotra, Marco M.
    Kolmanovsky, Ilya
    AUTOMATICA, 2020, 117
  • [43] A Learning-Based Model Predictive Control Framework for Real-Time SIR Epidemic Mitigation
    She, Baike
    Sundaram, Shreyas
    Pare, Philip E.
    2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 2565 - 2570
  • [44] Toward Real-Time Automotive Model Predictive Control: a Perspective from a Diesel Air Path Control Development
    Huang, Mike
    Liao-McPherson, Dominic
    Kim, Shinhoon
    Butts, Ken
    Kolmanovsky, Ilya
    2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 2425 - 2430
  • [45] Real-time economic control
    Williams, AK
    Rameshni, M
    HYDROCARBON PROCESSING, 1998, 77 (09): : 99 - +
  • [46] Real-time Control of HCCI Engine Using Model Predictive Control
    Ebrahimi, Khashayar
    Koch, C. R. Bob
    2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 1622 - 1628
  • [47] Real-Time Implementation of Model Predictive Control for Flow Control Application
    Rosli, Nurfatihah Syalwiah
    Ibrahim, Rosdiazli
    2014 5TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS 2014), 2014,
  • [48] Real time optimization (RTO) with model predictive control (MPC)
    De Souza, Glauce
    Odloak, Darci
    Zanin, Antonio C.
    COMPUTERS & CHEMICAL ENGINEERING, 2010, 34 (12) : 1999 - 2006
  • [49] Real Time Optimization (RTO) with Model Predictive Control (MPC)
    De Souza, Glauce
    Odloak, Darci
    Zanin, Antonio C.
    10TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, 2009, 27 : 1365 - 1370
  • [50] Advances in sensitivity-based nonlinear model predictive control and dynamic real-time optimization
    Biegler, L. T.
    Yang, X.
    Fischer, G. A. G.
    JOURNAL OF PROCESS CONTROL, 2015, 30 : 104 - 116