Authors:
M. Ehtiawesh
and
M. Mahfouf
Affiliation:
University of Sheffield, United Kingdom
Keyword(s):
Fuzzy Logic, Self-Organising, Uncertainty, Optimisation, Non-Linear, Robustness.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Control
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
The Self-Organising Fuzzy Logic Control (SOFLC) which is an extended version of the Fuzzy logic controller was designed to make Fuzzy controllers work with less dependency on previous knowledge. Since the introduction of the SOFLC, only a few attempts have been made to create a performance index table that is responsible for the corrections of the low-level control ‘adaptable’ according to the dynamics of the process under control. In this paper a new dynamic supervisory layer is proposed which enables the controller to adapt its structure on-line to any given certain performance criteria. In this mechanism, the controller starts from an empty rule-base and uses an on-line Particle Swarm Optimisation (PSO) algorithm to adapt the cells of the performance index (PI) table while issuing control actions to the low-level fuzzy rule-base. The Simulation results achieved when the proposed scheme was tested on a non-linear muscle relation process showed that it is superior to the standard SO
FLC scheme in terms of accurate tracking and efficient fuzzy rule-base elicitation (a conservative number of fuzzy rules)
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