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Related Experiment Videos

Practical approach to tuning MPC.

Willy Wojsznis1, John Gudaz, Terry Blevins

  • 1Emerson Process Management, Fisher-Rosemount Systems Division, 8627 Mopac Expressway North, Suite 400, Austin, TX 78759, USA. willy.wojsznis@emersonprocess.com

ISA Transactions
|January 28, 2003
PubMed
Summary
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This study introduces a new heuristic approach for tuning model predictive control (MPC) rules, simplifying control system setup. The developed tuning method offers robust performance against process variations, enhancing industrial automation.

Area of Science:

  • Control Engineering
  • Automation Systems

Background:

  • Model Predictive Control (MPC) is a widely used advanced process control strategy.
  • Tuning MPC controllers can be complex and time-consuming, often requiring expert knowledge.
  • Existing tuning methods may lack robustness to process uncertainties like gain and dead-time variations.

Purpose of the Study:

  • To develop a heuristic approach for creating intuitive and effective Model Predictive Control (MPC) tuning rules.
  • To simplify the tuning process for MPC controllers in practical applications.
  • To enhance the robustness of MPC control against process parameter variations.

Main Methods:

  • A heuristic tuning strategy was developed for an easy-to-use MPC implementation.
  • Process modeling utilized normalized input/output ranges, eliminating the need for output tuning (adjusting equal concern error).

Related Experiment Videos

  • Tuning parameters, specifically penalties on moves, were primarily based on process dead time, with adjustments for process gain. A model correction filter was also employed.

    • Main Results:

    • The default tuning provided robust control, tolerating up to a threefold increase in process static gain.
    • On-line adjustments via set point reference trajectory offered a way to manage overly aggressive control.
    • While robust to process gain changes, the tuning was less effective for dead-time variations, which were better handled by the model correction filter.

    Conclusions:

    • A combination of move penalties, reference trajectory adjustments, and a model correction filter resulted in easy-to-understand MPC tuning.
    • The developed heuristic approach offers a practical solution for robust MPC controller tuning.
    • Simulated tests demonstrated the effectiveness and intuitive nature of the proposed tuning method.