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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
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    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.

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