Control performance assessment for multivariable systems based on a modified relative variance technique

Yuan, Qiaolin; Lennox, Barry

doi:10.1016/j.jprocont.2008.05.005

Cited by 19 publications

(7 citation statements)

References 13 publications

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“…Some references also use a weighting matrix in the definition of the MV index to specify the significance of each output in the controller function. 13,47 The MV index with weighting matrix R is defined as…”

Section: Performance Assessment Of Mimo Systemsmentioning

confidence: 99%

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Khosroshahi

Poshtan

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Control system performance assessment is significant, especially in practical applications. One of the most important indices for the performance assessment of control systems is minimum variance. Calculating the minimum variance index in multivariate systems requires prior knowledge of system parameters and models, and is therefore an obstacle in practical applications. In this article, an index is proposed for the performance assessment of multivariate control loops, evaluating the system performance with the minimum variance criterion and using only the system’s routine operation data. This index can quantify the performance using neither any prior knowledge of system parameters nor the system’s optimal operation data. The proposed index is based on the Hurst exponent, a parameter for measuring correlations in time series data. In this article, detrended fluctuation analysis and rescaled range analysis are used to estimate the Hurst exponents of system outputs. Using a combination of these Hurst exponents, an index is defined for the performance assessment of multivariate systems. The results of simulation examples illustrate that the proposed index can assess the performance efficiently.

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Section: Performance Assessment Of Mimo Systemsmentioning

confidence: 99%

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Khosroshahi

Poshtan

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The estimation of J decen directly from the closed loop data, however, is difficult. When G is known (or has been identified using open or closed loop identification experiments), H can be estimated by prewhitening the pseudosignal ( y – Gu ) . Then, SOS programming can be used to identify a lower bound on J decen and η decen based on the identified disturbance model.…”

Section: Simultaneous Analysismentioning

confidence: 99%

“…When G is known (or has been identified using open or closed loop identification experiments), H can be estimated by prewhitening the pseudosignal (y − Gu). 39 Then, SOS programming can be used to identify a lower bound on J decen and η decen based on the identified disturbance model. We point out that the knowledge of G is also required by other available approaches for performance assessment of decentralized controllers.…”

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confidence: 99%

Minimum Variance Benchmark for Performance Assessment of Decentralized Controllers

Sendjaja

Kariwala

2012

Ind. Eng. Chem. Res.

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The available minimum variance (MV) benchmarks do not take the controller structure into account and thus can lead to incorrect conclusions regarding the performance of decentralized controllers. In this paper, we present a method for computing a lower bound on the least output variance achievable using decentralized controllers, where the nonconvexity of the optimization problem is handled using sums of squares programming. Though tight, the computation of this lower bound requires that the process model be known. As a more practical approach, we derive an MV benchmark for performance assessment of decentralized controllers on a loop by loop basis. The MV benchmark for loop by loop analysis can be estimated from the closed loop operating data with the knowledge of time delays between all the inputs and outputs. The usefulness of the proposed benchmarks is demonstrated using simulation examples.

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“…Although FCOR algorithm seems to be effective for on-line estimate of MV benchmark [10,13], it should be noted that this technique involves applying time series analysis to estimate the source white noise under an assumption of stationarity for output data [10,12,13]. However, such assumption is usually problematic in industrial process due to the facts: either the periodic disturbances, such as sine wave and rectangular signal, are common in industrial environment [15]-[19]; or deterministic disturbances such as sudden step changes may occur randomly in time [21]- [23]; or changes of disturbances dynamics are often encountered in chemical processes [14], [19].…”

Section: Introductionmentioning

confidence: 99%

Online Performance Monitoring of Processes Subject to Non-stationary Stochastic Disturbance

Huang

2022

J. Phys.: Conf. Ser.

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Since non-stationary disturbances are common in practical situation due to the varying stochastic disturbance or some deterministic disturbance injected, the traditional algorithm based on FCOR is not valid anymore. This paper develop a new approach, which combines EMD method with FCOR algorithm, to handle this problem such that the minimum variance (MV) performance of the closed-loop system can be monitored online. In the presence of varying stochastic disturbance and step/sine disturbance, the numerical example demonstrates the effectiveness of the proposed algorithm.

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Control performance assessment for multivariable systems based on a modified relative variance technique

Cited by 19 publications

References 13 publications

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Minimum Variance Benchmark for Performance Assessment of Decentralized Controllers

Online Performance Monitoring of Processes Subject to Non-stationary Stochastic Disturbance

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