Non-Gaussian and persistence measures for control loop quality assessment

Domański, Paweł D.

doi:10.1063/1.4946772

Cited by 24 publications

(18 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• integral time measures, e.g., Mean Square Error (MSE), Integral Absolute Error (IAE) [86], Integral Time Absolute Value (ITAE) [87], Integral of Square Time derivative of the Control input (ISTC) [88], Total Squared Variation (TSV) [89], and Amplitude Index (AMP) [71]; • correlation measures, such as oscillation detection index [90] or relative damping index [91]; • statistical factors utilizing different probabilistic distribution function (standard deviation, variance, skewness, kurtosis, scale, shape, etc.) [92], variance band index [93], or the factors of other probabilistic distributions [94][95][96]; • benchmarking methods [97]; and • alternative indexes using wavelets [98], orthogonal Laguerre [99] and other functions [65], Hurst exponent [100], persistence measures [101,102], entropy [103][104][105], multifractal approaches [106], or fractional-order [107,108].…”

Section: Data-driven Methodsmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

Self Cite

View full text Add to dashboard Cite

Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

show abstract

Section: Data-driven Methodsmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

Self Cite

View full text Add to dashboard Cite

show abstract

“…Po obustronnym zlogarytmizowaniu równania (11) można obliczyć wykładnik H. Niestety jedna prosta w większości przypadków niedokładnie aproksymuje dane na wykresie przeskalowanego zasięgu, stąd też podejmuje się próby aproksymacji danych łamaną z dwoma punktami przegięcia (ang. crossover), co zostało szerzej opisane w [43]. W procesie oceny działania regulatora kluczowy jest począt-Rys.…”

Section: Analiza Fraktalnaunclassified

Comparison of Various Controller Parameters Optimization Strategies for Industrial Processes Using Different Control Performance Assessment Indexes

Bogusz¹,

Rajkowski²,

Domański³

2019

PAR

View full text Add to dashboard Cite

“…Nowadays we observe approaches unequal to the above ones using a nonlinear data analysis approach. They are not based on Gaussian assumptions (Domański, 2016).…”

Section: Control Performance Assessmentmentioning

confidence: 99%

Assessment of the GPC Control Quality Using Non–Gaussian Statistical Measures

Domański

Ławryńczuk

2017

International Journal of Applied Mathematics and Computer Science

Self Cite

View full text Add to dashboard Cite

This paper presents an alternative approach to the task of control performance assessment. Various statistical measures based on Gaussian and non-Gaussian distribution functions are evaluated. The analysis starts with the review of control error histograms followed by their statistical analysis using probability distribution functions. Simulation results obtained for a control system with the generalized predictive controller algorithm are considered. The proposed approach using Cauchy and Lévy α-stable distributions shows robustness against disturbances and enables effective control loop quality evaluation. Tests of the predictive algorithm prove its ability to detect the impact of the main controller parameters, such as the model gain, the dynamics or the prediction horizon.

show abstract

Non-Gaussian and persistence measures for control loop quality assessment

Cited by 24 publications

References 60 publications

Performance Assessment of Predictive Control—A Survey

Performance Assessment of Predictive Control—A Survey

Comparison of Various Controller Parameters Optimization Strategies for Industrial Processes Using Different Control Performance Assessment Indexes

Assessment of the GPC Control Quality Using Non–Gaussian Statistical Measures

Contact Info

Product

Resources

About