Abstract
This paper presents a robust evolving cloud-based controller named RECCo. The controller has an evolving fuzzy structure and the rules are represented by data clouds. The evolving part of the algorithm allows adding of new rules (clouds) and moreover, the robust adaptive law using the steepest (gradient) descent method adapts the PID-R parameters of each cloud. There are also some protective mechanisms introduced which improve the robustness of the algorithm. The effectiveness of the controller was tested on the simulated surge tank model and on the real two tank plant. Both plants have quite similar structure but they have different nonlinear dynamics. Using the same initializing procedure the RECCo controller efficiently control both plants.
Zusammenfassung
In dieser Veröffentlichung wird ein robuster Evolutions- Cloud-basierter Regler mit dem Namen RECCo vorgestellt. Der Regler hat eine entwickelnde Fuzzy-Struktur und die Regeln werden durch Datenclouds dargestellt. Der entwickelnde Teil des Algorithmus erlaubt das Hinzufügen neuer Regeln (Wolken) und darüber hinaus passt das robuste adaptive Gesetz, das die Methode des steilsten (Gradienten-) Abstiegs anwendet, die PID-R-Parameter jeder Wolke an. Es werden auch einige Schutzmechanismen eingeführt, die die Robustheit des Algorithmus verbessern. Die Wirksamkeit des Reglers wurde am simulierten Ausgleichsbehältermodell und an einer realen Zweitankanlage getestet. Beide Anlagen haben eine sehr ähnliche Struktur, aber eine unterschiedliche nichtlineare Dynamik. Bei einem gleichen Initialisierungsverfahren steuert der RECCo Regler beide Anlagen effizient.
About the authors
Goran Andonovski received the B.Sc. degree in 2012 from the Faculty of Electrical Engineering, University of Ljubljana. He is currently working on his PhD thesis and he is employed as a researcher at the Laboratory of Modelling, Simulation and Control at the same University. His research interests include adaptive and predictive control of nonlinear processes, evolving fuzzy learning methods and fault detection and diagnosis.
Bruno Costa received the B. Eng., M. Eng. and D. Eng. degrees in Electrical and Computer Engineering from the Federal University of Rio Grande do Norte (UFRN), Brazil, in 2008, 2010 and 2014, respectively. He is currently an Adjunct Professor with the Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Brazil. Dr. Costa’s latest work is based on computational intelligence techniques applied to computer vision, anomaly detection, controls and fault detection and diagnosis problems.
Sašo Blažič received the B.Sc., M. Sc., and Ph. D. degrees in 1996, 1999, and 2002, respectively, from the Faculty of Electrical Engineering, University of Ljubljana. He is currently a Professor with the University of Ljubljana. His research interests include adaptive, fuzzy and predictive control of dynamical systems and modelling of nonlinear systems. Recently, the focus of his research has moved towards the areas of autonomous mobile systems, mobile robotics, and the control of satellite systems.
Igor Škrjanc received B.S., M.S. and Ph.D. degrees in electrical engineering, in 1988, 1991 and 1996, respectively, at the Faculty of Electrical and Computer Engineering, University of Ljubljana, Slovenia. He is currently a Full Professor with the same faculty and Head of Laboratory for Autonomous and Mobile Systems. He is lecturing the basic control theory at graduate and advanced intelligent control at postgraduate study. His main research areas are adaptive, predictive, neuro-fuzzy and fuzzy adaptive control systems. His current research interests include also the field of autonomous mobile systems in sense of localization, direct visual control and trajectory tracking control. He is Humboldt research fellow, research fellow of JSPS and Chair of Excellence at University Carlos III of Madrid. He also serves as an Associated Editor for IEEE Transaction on Neural Networks and Learning System, IEEE Transaction on Fuzzy Systems, the Evolving Systems journal and International journal of artificial intelligence.
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