IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i2p259-d321288.html
   My bibliography  Save this article

Responsive Economic Model Predictive Control for Next-Generation Manufacturing

Author

Listed:
  • Helen Durand

    (Wayne State University, Detroit, MI 48202, USA
    Current address: 5050 Anthony Wayne Drive, Detroit, MI 48202, USA.)

Abstract

There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types would advance the move toward artificial intelligence properties for this controller beyond those which it has today and would provide new thoughts on interpretability and verification for the controller. This work provides theoretical studies which relate nonlinear systems considerations for EMPC to these higher-level concepts using two ideas for EMPC formulations motivated by specific situations related to self-modification of a control design after human perceptions of the process response are received and to controller handling of anomalies.

Suggested Citation

  • Helen Durand, 2020. "Responsive Economic Model Predictive Control for Next-Generation Manufacturing," Mathematics, MDPI, vol. 8(2), pages 1-38, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:259-:d:321288
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/2/259/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/2/259/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhe Wu & Panagiotis D. Christofides, 2019. "Economic Machine-Learning-Based Predictive Control of Nonlinear Systems," Mathematics, MDPI, vol. 7(6), pages 1-20, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Monica Aureliana Petcu & Liliana Ionescu-Feleaga & Bogdan-Ștefan Ionescu & Dumitru-Florin Moise, 2023. "A Decade for the Mathematics : Bibliometric Analysis of Mathematical Modeling in Economics, Ecology, and Environment," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    2. Guilherme V. Hollweg & Shahid A. Khan & Shivam Chaturvedi & Yaoyu Fan & Mengqi Wang & Wencong Su, 2023. "Grid-Connected Converters: A Brief Survey of Topologies, Output Filters, Current Control, and Weak Grids Operation," Energies, MDPI, vol. 16(9), pages 1-31, April.
    3. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    4. Zhihao Zhang & Zhe Wu & David Rincon & Panagiotis D. Christofides, 2019. "Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning," Mathematics, MDPI, vol. 7(10), pages 1-25, September.
    5. Mohd Shareduwan Mohd Kasihmuddin & Mohd. Asyraf Mansor & Md Faisal Md Basir & Saratha Sathasivam, 2019. "Discrete Mutation Hopfield Neural Network in Propositional Satisfiability," Mathematics, MDPI, vol. 7(11), pages 1-21, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:259-:d:321288. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.