A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

Andonovski, Goran and Angelov, Plamen Parvanov and Blazic, Saso and Skrjanc, Igor (2016) A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant. Applied Soft Computing, 48. pp. 29-38. ISSN 1568-4946

[thumbnail of PHE_RECCo_Goran]
PHE_RECCo_Goran.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (2MB)


The RECCo control algorithm, presented in this article, is based on the fuzzy rule-based (FRB) system named ANYA which has non-parametric antecedent part. It starts with zero fuzzy rules (clouds) in the rule base and evolves its structure while performing the control of the plant. For the consequent part of RECCo PID-type controller is used and the parameters are adapted in an online manner. The RECCo does not require any off-line training or any type of model of the controlled process (e.g. differential equations). Moreover, in this article we propose a normalization of the cloud (data) space and an improved adaptation law of the controller. Due to the normalization some of the evolving parameters can be fixed while the new adaptation law improves the performance of the controller in the starting phase of the process control. To assess the performance of the RECCo algorithm, firstly a comparison study with classical PID controller was performed on a model of a plate heat-exchanger (PHE). Tuning the PID parameters was done using three different techniques (Ziegler–Nichols, Cohen–Coon and pole placement). Furthermore, a practical implementation of the RECCo controller for a real PHE plant is presented. The PHE system has nonlinear static characteristic and a time delay. Additionally, the real sensor's and actuator's limitations represent a serious problem from the control point of view. Besides this, the RECCo control algorithm autonomously learns and evolves the structure and adapts its parameters in an online unsupervised manner.

Item Type:
Journal Article
Journal or Publication Title:
Applied Soft Computing
Additional Information:
This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 48, 2016 DOI: 10.1016/j.asoc.2016.05.036
Uncontrolled Keywords:
?? evolving systemrobust adaptive controlfuzzy cloud-based systemsoftware ??
ID Code:
Deposited By:
Deposited On:
22 Jul 2016 14:04
Last Modified:
21 Apr 2024 23:56