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Model Predictive Control with a Relaxed Cost Function for Constrained Linear Systems

Author

Listed:
  • David Sotelo
  • Antonio Favela-Contreras
  • Viacheslav V. Kalashnikov
  • Carlos Sotelo

Abstract

The Model Predictive Control technique is widely used for optimizing the performance of constrained multi-input multi-output processes. However, due to its mathematical complexity and heavy computation effort, it is mainly suitable in processes with slow dynamics. Based on the Exact Penalization Theorem, this paper presents a discrete-time state-space Model Predictive Control strategy with a relaxed performance index, where the constraints are implicitly defined in the weighting matrices, computed at each sampling time. The performance validation for the Model Predictive Control strategy with the proposed relaxed cost function uses the simulation of a tape transport system and a jet transport aircraft during cruise flight. Without affecting the tracking performance, numerical results show that the execution time is notably decreased compared with two well-known discrete-time state-space Model Predictive Control strategies. This makes the proposed Model Predictive Control mainly suitable for constrained multivariable processes with fast dynamics.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:jnlmpe:7485865
    DOI: 10.1155/2020/7485865
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    Cited by:

    1. 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.
    2. 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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