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Application of Interior-Point Methods to Model Predictive Control

Author

Listed:
  • C. V. Rao

    (University of Wisconsin)

  • S. J. Wright

    (Argonne National Laboratory)

  • J. B. Rawlings

    (University of Wisconsin)

Abstract

We present a structured interior-point method for the efficient solution of the optimal control problem in model predictive control. The cost of this approach is linear in the horizon length, compared with cubic growth for a naive approach. We use a discrete-time Riccati recursion to solve the linear equations efficiently at each iteration of the interior-point method, and show that this recursion is numerically stable. We demonstrate the effectiveness of the approach by applying it to three process control problems.

Suggested Citation

  • C. V. Rao & S. J. Wright & J. B. Rawlings, 1998. "Application of Interior-Point Methods to Model Predictive Control," Journal of Optimization Theory and Applications, Springer, vol. 99(3), pages 723-757, December.
    • Handle: RePEc:spr:joptap:v:99:y:1998:i:3:d:10.1023_a:1021711402723
    DOI: 10.1023/A:1021711402723
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    Cited by:

    1. Mu, Yunfei & Xu, Yanze & Zhang, Jiarui & Wu, Zeqing & Jia, Hongjie & Jin, Xiaolong & Qi, Yan, 2023. "A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems," Applied Energy, Elsevier, vol. 346(C).
    2. R. Milman & E. J. Davison, 2008. "A Fast MPC Algorithm Using Nonfeasible Active Set Methods," Journal of Optimization Theory and Applications, Springer, vol. 139(3), pages 591-616, December.
    3. Daniel Word & Jia Kang & Johan Akesson & Carl Laird, 2014. "Efficient parallel solution of large-scale nonlinear dynamic optimization problems," Computational Optimization and Applications, Springer, vol. 59(3), pages 667-688, December.
    4. Mohsen Davoodi & Hamed Jafari Kaleybar & Morris Brenna & Dario Zaninelli, 2023. "Energy Management Systems for Smart Electric Railway Networks: A Methodological Review," Sustainability, MDPI, vol. 15(16), pages 1-35, August.
    5. Daniel P. Robinson, 2015. "Primal-Dual Active-Set Methods for Large-Scale Optimization," Journal of Optimization Theory and Applications, Springer, vol. 166(1), pages 137-171, July.
    6. Nai-Yuan Chiang & Victor M. Zavala, 2016. "An inertia-free filter line-search algorithm for large-scale nonlinear programming," Computational Optimization and Applications, Springer, vol. 64(2), pages 327-354, June.

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