Authors:
M. A. Hossain
;
A. A. Madkour
and
K. P. Dahal
Affiliation:
University of Bradford, United Kingdom
Keyword(s):
Performance issues, system identification, active vibration control, genetic algorithm, recursive least square and ANFIS.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Real-Time Systems Control
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This research presents an investigation into the comparative performance in implementing intelligent system identification and control algorithms. Several approaches for on-line system identification and control are explored and evaluated to demonstrate the merits in implementing the algorithms for similar level of error convergence. Active vibration control (AVC) of a flexible beam system is considered as a platform for the investigation. The AVC system is designed using three different on-line identification approaches, which include (a) genetic algorithms (GAs) (b) adaptive neuro-fuzzy inference system (ANFIS) and (c) recursive least square (RLS) estimation. These algorithms are used to estimate a linear discrete model of the system. Based on these algorithms, different approaches of the AVC system are implemented, tested and validated to evaluate the relative merits of the algorithms. Finally, a comparative performance of the error convergence performance in implementing the iden
tification and control algorithms is presented and discussed through a set of experiments.
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