Decision and Control in Nonlinear Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 1759

Special Issue Editors


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Guest Editor
Institute of Control and Computation Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland
Interests: control performance assessment; industrial advanced process control; outliers, tailed distributions, entropy

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Guest Editor
Faculty of Mechanical Engineering, Bialystok University of Technology, 15-351 Białystok, Poland
Interests: nonlinear control systems; realizability, reducibility of control systems; fractional systems; application of fractional tools to industrial process control; control systems on time scales
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Special Issue Information

Dear Colleagues,

Despite linear simplifications, real industrial systems are generally non-linear and often very complex. The research should remain based in reality and must cope with existing challenges. The analysis of nonlinear systems seeks discover their properties, while the design of an appropriate control strategy enables practitioners to take care over the process. Therefore, efficient nonlinear control should take into account various nonlinear aspects, such as modelling, control system design, stability analysis, and its assessment and sustainability. In fact, any industrial system is additionally subject to unknown uncertainties, but is always impacted by different kinds of disturbances. These unknown environments are often complex and have strange properties. Their analysis gives insight into the process, enabling better understanding and allowing proper control strategy selection. As this subject is broad, the research should be focused.

The nonlinear nature of the industrial real-time processes requires analysis with novel ideas of fractional calculus, entropy, divergence or multi-fractality, and causality analysis, among others. In this Special Issue of Applied Sciences, we want to address these state-of-the-art nonlinear analysis and control issues, with the main focus being on their industrial background and realizations using limited assumptions and model-free approaches. In this Special Issue, we will publish both original and review scientific articles, as well as short communications.

Prof. Dr. Pawel D. Domański
Prof. Dr. Ewa Pawłuszewicz
Guest Editors

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Keywords

  • non-Gaussianity
  • fractional calculus
  • geometric methods in nonlinear control
  • realization of control systems
  • stability and stabilizability
  • fat and heavy tails
  • persistence analysis
  • fractality and multi-fractality
  • causality analysis
  • oscillation detection and propagation
  • transfer entropy

Published Papers (1 paper)

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21 pages, 3563 KiB  
Article
Causality in Control Systems Based on Data-Driven Oscillation Identification
by Michał J. Falkowski, Paweł D. Domański and Ewa Pawłuszewicz
Appl. Sci. 2022, 12(8), 3784; https://0-doi-org.brum.beds.ac.uk/10.3390/app12083784 - 08 Apr 2022
Cited by 1 | Viewed by 1209
Abstract
This paper addresses the subject of causality analysis using simulation data and data collected from a real control system. Simulated data includes Gaussian and Cauchy noise signals. Real-time series include various, mostly unknown distortions, like trends, oscillations, and noises. Presented research focuses on [...] Read more.
This paper addresses the subject of causality analysis using simulation data and data collected from a real control system. Simulated data includes Gaussian and Cauchy noise signals. Real-time series include various, mostly unknown distortions, like trends, oscillations, and noises. Presented research focuses on the oscillatory component in data and its propagation in multi-loop control systems. Oscillation identification is based on a deep decomposition process for control error time series. Identified periodic signals are used for further causality processing. The analysis uses the Transfer Entropy approach. This method belongs to the group of model-free methods. The determination of information pathways is conducted without any model or a priori process knowledge. The research investigates the impact of the oscillation time-series component on the Transfer Entropy causality analysis. The summary shows the observations obtained for given simulated datasets and those collected from real processes. The obtained results show that simulated analysis works properly. On the contrary, the direct application of the oscillation decomposition in real industrial cases may be misleading. Large datasets demand modification in the methodology. Different variants are tested. They show that oscillation propagation is biased in real systems and, therefore, the decomposition should be applied with caution. Furthermore, it is important to remember that the algorithm transition from simulated data to real industrial ones is demanding and should be done with the utmost care. Full article
(This article belongs to the Special Issue Decision and Control in Nonlinear Systems)
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