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Controller performance analysis with LQG benchmark obtained under closed loop conditions.

Ramesh Kadali1, Biao Huang

  • 1Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada.

ISA Transactions
|October 26, 2002
PubMed
Summary
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This study introduces a data-driven subspace method to calculate the Linear Quadratic Gaussian (LQG) benchmark from closed-loop data, enabling controller performance assessment without explicit models. This approach directly identifies optimal input and output variances for improved control system analysis.

Area of Science:

  • Control Engineering
  • System Identification
  • Process Optimization

Background:

  • The Linear Quadratic Gaussian (LQG) benchmark is crucial for evaluating controller performance by analyzing input and output variances.
  • Calculating the LQG benchmark traditionally requires an explicit parametric model of the system.
  • Assessing controller performance using LQG benchmarks is limited by the need for system models, especially in closed-loop scenarios.

Purpose of the Study:

  • To propose a novel data-driven subspace approach for computing the LQG benchmark using only closed-loop data.
  • To enable controller performance assessment without relying on explicit system models.
  • To extend the applicability of LQG benchmark calculations to both univariate and multivariate systems.

Main Methods:

Related Experiment Videos

  • A data-driven subspace identification method is employed to estimate system matrices from closed-loop operational data.
  • The method utilizes setpoint excitation to distinguish between deterministic and stochastic inputs within the closed-loop system.
  • Optimal LQG benchmark variances are derived directly from identified subspace matrices, representing performance limits.

    • Main Results:

    • The proposed method successfully calculates LQG benchmark variances directly from closed-loop data, eliminating the need for explicit system models.
    • The approach is validated for both univariate and multivariate systems, demonstrating its versatility.
    • Profit analysis indicates potential benefits of integrating feedforward control within the LQG framework.

    Conclusions:

    • A practical, model-free method for LQG benchmark calculation from closed-loop data has been developed.
    • The data-driven subspace approach offers a robust tool for controller performance evaluation in various industrial systems.
    • The findings support the integration of advanced control strategies for enhanced process efficiency and profitability.