Statistical measures for proportional–integral–derivative control quality: Simulations and industrial data

Domański, Paweł D.

doi:10.1177/0959651817754034

Cited by 11 publications

(8 citation statements)

References 21 publications

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“…Automagic evaluation of any index without profound reflection about its properties in a given control environment leads to nowhere [167]. CPA procedure should take into account all available degrees of freedom of the MPC application and should use all available case-specific knowledge [9].…”

Section: Mpc Assessment Proceduresmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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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.

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Section: Mpc Assessment Proceduresmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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“…As is known to all, PID is one of the earliest control strategies. Since its simple structure, good robustness, and high reliability, PID controller plays an important role in the closed industrial system [1], [2]. The PID controller is designed based on the error of the system, which uses proportion, integral, and differential to calculate the control quantity in order to achieve excellent performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

et al. 2021

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“…Industrial practice shows that such an assumption is hardly met. Real process control loop data 19 hardly exhibit non‐Gaussian properties. Fat tails in histograms represent incidents lying far away from normal performance.…”

Section: Introductionmentioning

confidence: 99%

Study on outlier robustness of minimum variance control performance assessment

Kaczmarek

Domański

2021

Adaptive Control & Signal

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Summary Minimum variance (MinVar) method for control performance assessment constitutes one of the most common approaches to the control quality estimation. There are dozens of versions, enriched with numerous reported industrial implementations. MinVar methodology uses the idea of minimum variance, which has been introduced by Kalman. Therefore, it should be remembered that MinVar concept relies on the same assumptions as an idea of the minimum variance control. Among other assumptions, it is essential that the modeled disturbance is an independent random sequence. This article addresses scenarios, when loop noise exhibits non‐Gaussian properties and is characterized by outliers having fat‐tailed distribution. Sensitivity analysis of minimum variance method against such disturbances is evaluated using commonly used PID SISO control benchmarks. It is shown that MinVar method may be significantly biased in such non‐Gaussian situations, which are very frequent in the industrial reality. Reasons of the biased performance are traced and respective solutions are proposed.

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Statistical measures for proportional–integral–derivative control quality: Simulations and industrial data

Cited by 11 publications

References 21 publications

Performance Assessment of Predictive Control—A Survey

Performance Assessment of Predictive Control—A Survey

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

Study on outlier robustness of minimum variance control performance assessment

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