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Neural Computing and Applications
Published in: Neural Computing and Applications 20/2021

26-05-2021 | Original Article

Real-time analysis of adaptive fuzzy predictive controller for chaotification under varying payload and noise conditions

Author: Bedri Bahtiyar

Published in: Neural Computing and Applications | Issue 20/2021

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Abstract

In this paper, an adaptive fuzzy generalized predictive controller is proposed for the anti-chaos control where the system dynamics are assumed to be unknown or uncertain in large. In the proposed mechanism, the control signal is generated by an input constraint generalized predictive controller to track the Duffing oscillator which is used as a chaotic reference system. The adaptive fuzzy system, as a real plant model, is employed to obtain prediction through the receding horizon. In order to show the capability of the proposed mechanism, three different payloads and noise cases are studied in the real-time control experiments of flexible-joint manipulator and tracking results are compared with three more non-model-based controllers which are conventional adaptive fuzzy control, indirect adaptive fuzzy sliding mode controller and proportional–integrative–derivative control. Results show that the proposed mechanism has better control performance than the others.
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Metadata
Title
Real-time analysis of adaptive fuzzy predictive controller for chaotification under varying payload and noise conditions
Author
Bedri Bahtiyar
Publication date
26-05-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 20/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-05969-w

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