IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2571-d870247.html
   My bibliography  Save this article

A New Software-Based Optimization Technique for Embedded Latency Improvement of a Constrained MIMO MPC

Author

Listed:
  • David Sotelo

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Antonio Favela-Contreras

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Alfonso Avila

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Arturo Pinto

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

  • Francisco Beltran-Carbajal

    (Departamento de Energía, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Av. San Pablo No. 180, Col. Reynosa Tamaulipas, Mexico City 02200, Mexico)

  • Carlos Sotelo

    (Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico)

Abstract

Embedded controllers for multivariable processes have become a powerful tool in industrial implementations. Here, the Model Predictive Control offers higher performances than standard control methods. However, they face low computational resources, which reduces their processing capabilities. Based on pipelining concept, this paper presents a new embedded software-based implementation for a constrained Multi-Input-Multi-Output predictive control algorithm. The main goal of this work focuses on improving the timing performance and the resource usage of the control algorithm. Therefore, a profiling study of the baseline algorithm is developed, and the performance bottlenecks are identified. The functionality and effectiveness of the proposed implementation are validated in the NI myRIO 1900 platform using the simulation of a jet transport aircraft during cruise flight and a tape transport system. Numerical results for the study cases show that the latency and the processor usage are substantially reduced compared with the baseline algorithm, 4.6 × and 3.17 × respectively. Thus, efficient program execution is obtained which makes the proposed software-based implementation mainly suitable for embedded control systems.

Suggested Citation

  • David Sotelo & Antonio Favela-Contreras & Alfonso Avila & Arturo Pinto & Francisco Beltran-Carbajal & Carlos Sotelo, 2022. "A New Software-Based Optimization Technique for Embedded Latency Improvement of a Constrained MIMO MPC," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2571-:d:870247
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2571/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/15/2571/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eduardo Zafra & Sergio Vazquez & Hipolito Guzman Miranda & Juan A. Sanchez & Abraham Marquez & Jose I. Leon & Leopoldo G. Franquelo, 2020. "Efficient FPSoC Prototyping of FCS-MPC for Three-Phase Voltage Source Inverters," Energies, MDPI, vol. 13(5), pages 1-16, March.
    2. David Sotelo & Antonio Favela-Contreras & Viacheslav V. Kalashnikov & Carlos Sotelo, 2020. "Model Predictive Control with a Relaxed Cost Function for Constrained Linear Systems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, March.
    3. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.
    4. Fang Liu & Haotian Li & Ling Liu & Runmin Zou & Kangzhi Liu, 2021. "A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control," Mathematics, MDPI, vol. 9(7), pages 1-16, April.
    5. Alessia Musa & Michele Pipicelli & Matteo Spano & Francesco Tufano & Francesco De Nola & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul & Gianluca Toscano, 2021. "A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yan Liang & Xianzhi Hu & Gang Hu & Wanting Dou, 2022. "An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-33, November.

    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. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.
    2. Paweł Sokólski & Tomasz A. Rutkowski & Bartosz Ceran & Daria Złotecka & Dariusz Horla, 2023. "Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set," Energies, MDPI, vol. 16(13), pages 1-23, June.
    3. Deepa Sankar & Lakshmi Syamala & Babu Chembathu Ayyappan & Mathew Kallarackal, 2021. "FPGA-Based Cost-Effective and Resource Optimized Solution of Predictive Direct Current Control for Power Converters," Energies, MDPI, vol. 14(22), pages 1-26, November.
    4. Pier Giuseppe Anselma, 2022. "Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health," Energies, MDPI, vol. 15(5), pages 1-25, February.
    5. Yan Liang & Xianzhi Hu & Gang Hu & Wanting Dou, 2022. "An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-33, November.
    6. Roberto O. Ramírez & Carlos R. Baier & Felipe Villarroel & Eduardo Espinosa & Mauricio Arevalo & Jose R. Espinoza, 2023. "Reduction of DC Capacitor Size in Three-Phase Input/Single-Phase Output Power Cells of Multi-Cell Converters through Resonant and Predictive Control: A Characterization of Its Impact on the Operating ," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    7. Pedro Bautista-Camino & Alejandro I. Barranco-Gutiérrez & Ilse Cervantes & Martin Rodríguez-Licea & Juan Prado-Olivarez & Francisco J. Pérez-Pinal, 2022. "Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion," Energies, MDPI, vol. 15(5), pages 1-23, February.
    8. Paweł Sokólski & Tomasz A. Rutkowski & Bartosz Ceran & Daria Złotecka & Dariusz Horla, 2022. "The Influence of Cooperation on the Operation of an MPC Controller Pair in a Nuclear Power Plant Turbine Generator Set," Energies, MDPI, vol. 15(18), pages 1-19, September.
    9. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.
    10. Patryk Chaber & Andrzej Wojtulewicz, 2022. "Flexible Matrix of Controllers for Real Time Parallel Control," Energies, MDPI, vol. 15(5), pages 1-23, March.
    11. Yesid Bello & Juan Sebastian Roncancio & Toufik Azib & Diego Patino & Cherif Larouci & Moussa Boukhnifer & Nassim Rizoug & Fredy Ruiz, 2023. "Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption," Energies, MDPI, vol. 16(4), pages 1-26, February.
    12. Denis Sidorov, 2023. "Preface to “Model Predictive Control and Optimization for Cyber-Physical Systems”," Mathematics, MDPI, vol. 11(4), pages 1-3, February.

    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:10:y:2022:i:15:p:2571-:d:870247. 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.