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

Proceedings of the Institution of Mechanical Engineers, Part I:

2018

This article focuses on investigation of statistical approaches to the task of control performance assessment. Different statistical measures with Gaussian and non-Gaussian probabilistic distributions are taken into consideration. Analysis starts with the observations for simulated proportional-integral-derivative control error histograms followed by its statistical investigation using selected probabilistic distribution functions. Simulation experiments are followed by the analysis of control data originating from real industrial loops. Shadowing effect of long-tail control error histograms is identified, as it may significantly disable proper loop quality assessment. Results show that non-Gaussian approach with Cauchy or a-stable distributions seems to be reasonable assessment alternative in case of disturbances existing in industrial processes.

Section: Conclusion and Open Issuesmentioning

confidence: 99%

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

Proceedings of the Institution of Mechanical Engineers, Part I:

2018

“…Non-Gaussian statistical [157] and fractal [158] methodologies have been investigated for the GPC predictive control algorithm. Linear [159] and nonlinear [160] DMC predictive control have been assessed using integral, statistical, information, and fractal measures.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

2020

Algorithms

Self 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.

“…Used variables may exhibit fat tails, asymmetric properties or varying broadness. The aspects of fat tails [6] and broadness (described by the variance or scaling) [7] have been already addressed in previous research. Observed non-symmetric properties of control variables are relatively frequent, especially in non-linear cases.…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that even simple linear MPC configuration requires alternative CPA approach, such as fractal [48] or non-Gaussian [6]. Nonlinear industrial control generates even more serious challenges for reliable MPC monitoring.…”

Section: Introductionmentioning

confidence: 99%

Control Quality Assessment for Processes With Asymmetric Properties and its Application to pH Reactor

Ławryńczuk

2020

IEEE Access

The majority of processes in chemical industry is nonlinear. However we often take advantage of linear approximation and analysis as the useful simplification. Nonetheless, one has to remember that the reality is often complex, nonlinear and full of unknown unknowns. One of the forgotten aspects in control engineering is connected with the symmetricity. Asymmetric properties appear, when the process or instrumentation introduces nonlinearities. Control systems are then exposed to the asymmetrical behavior and should properly react, while their performance measures have to take them into account. This paper proposes robust control performance indexes in form of the M-estimator using logistic ψ function denoted σ H and α-stable distribution scale factor γ. Additionally, their application procedure in industrial chemical engineering environment is proposed. The approach is illustrated with an example of the pH neutralization process. INDEX TERMS Asymmetry, control performance assessment, fat-tails, MPC, pH neutralization.