Analysis of adaptation law of the robust evolving cloud-based controller

Andonovski, Goran and Blazic, Saso and Angelov, Plamen Parvanov and Skrjanc, Igor (2015) Analysis of adaptation law of the robust evolving cloud-based controller. In: Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, pp. 1-7. ISBN 9781467366984

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Abstract

In this paper we propose a performance analysis of the robust evolving cloud-based controller (RECCo) according to the different initial scenarios. RECCo is a controller based on fuzzy rule-based (FRB) systems with non-parametric antecedent part and PID type consequent part. Moreover, the controller structure (the fuzzy rules and the membership function) is created in online manner from the data stream. The advantage of the RECCo controller is that do not require any a priory knowledge of the controlled system. The algorithm starts with zero fuzzy rules (zero data clouds) and evolves/learns during the process control. Also the PID parameters of the controller are initialed with zeros and are adapted in online manner. According to the zero initialization of the parameters the new adaptation law is proposed in this article to solve the problems in the starting phase of the process control. Several initial scenarios were theoretically propagated and experimentally tested on the model of a heat-exchanger plant. These experiments prove that the proposed adaptation law improve the performance of the RECCo control algorithm in the starting phase.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 77916
Deposited By: ep_importer_pure
Deposited On: 26 Jan 2016 13:56
Refereed?: Yes
Published?: Published
Last Modified: 27 Feb 2020 05:42
URI: https://eprints.lancs.ac.uk/id/eprint/77916

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