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Related Experiment Videos

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

J H van der Lee1, W Y Svrcek, B R Young

  • 1Virtual Materials Group Inc., 657 Hawkside Mews NW, Calgary, Alberta T3G 3S1, Canada.

ISA Transactions
|September 18, 2007
PubMed
Summary
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A new automated tuning algorithm for Model Predictive Control (MPC) systems combines genetic algorithms and fuzzy decision-making. This approach efficiently optimizes MPC performance across diverse applications, overcoming limitations of traditional methods.

Area of Science:

  • Process Control Engineering
  • Automation and Control Systems
  • Computational Intelligence

Background:

  • Model Predictive Control (MPC) is widely used but application-specific tuning is complex.
  • Existing MPC tuning methods are often limited to specific cases, necessitating time-consuming trial-and-error.
  • Non-optimum tuning can result from manual approaches, impacting system performance.

Purpose of the Study:

  • To develop a generalized automated tuning algorithm for Model Predictive Control (MPC).
  • To address the limitations of manual and case-specific tuning methods for MPC.
  • To provide a feasible and efficient solution for optimizing MPC performance.

Main Methods:

  • A numerically-based approach combining a genetic algorithm with multi-objective fuzzy decision-making.

Related Experiment Videos

  • Utilizing genetic algorithms for their problem-agnostic nature, adaptable to MPC tuning parameters.
  • Employing multi-objective fuzzy decision-making to handle qualitative control objectives and multiple inputs, especially for MIMO systems.

    • Main Results:

    • Demonstrated the feasibility of a generalized automated tuning algorithm for MPC.
    • Showcased how different definitions of 'optimum' control are handled by the algorithm.
    • Presented a case study illustrating the algorithm's application and the resulting parameter variations for diverse objectives.

    Conclusions:

    • The developed algorithm offers a generalized and automated solution for MPC tuning.
    • Multi-objective fuzzy decision-making effectively incorporates qualitative control goals.
    • The approach is feasible for various MPC applications, including complex MIMO systems, leading to optimized performance.