Performance enhancement of unfalsified adaptive control strategy using fuzzy logic

Unfalsified Adaptive Switching Supervisory Control (UASSC) is a performance-based data-driven methodology to control uncertain systems with the least possible plant assumptions. There are a set of pre-designed controllers in the controller bank, and the goal is to select the best controller at each...

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Bibliographic Details
Published in:International journal of systems science 2019-11, Vol.50 (15), p.2752-2763
Main Authors: Habibi, S. I., Khaki-Sedigh, A., Manzar, M. N.
Format: Article
Language:English
Subjects:
Adaptive control
Computer simulation
Control stability
Controllers
Cost function
data-driven control
Fuzzy control
Fuzzy logic
Fuzzy systems
Performance enhancement
Stability analysis
Supervisory control
Uncertain plants
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Summary:Unfalsified Adaptive Switching Supervisory Control (UASSC) is a performance-based data-driven methodology to control uncertain systems with the least possible plant assumptions. There are a set of pre-designed controllers in the controller bank, and the goal is to select the best controller at each time instance. The Multi-Model UASSC (MMUASSC) uses the UASSC concept, but it also benefits from a set of pre-specified models in the model bank. This paper introduces a method to improve the performance of the UASSC and MMUASSC by cost function manipulations and fuzzy logic concepts. To achieve this, fuzzy UASSC and fuzzy MMUASSC methods are introduced. In these methods, a time-varying coefficient, which is the output of a fuzzy system, is used along with the conventional cost functions. The input of this fuzzy system is chosen to properly reflect the performance of the corresponding controller in the controller bank. Using this method, the performance of the outside loop controllers is accurately evaluated, and closed-loop stability proof is provided. Also, as the existence of non-minimum phase controllers is problematic, a solution is provided to handle such cases. Finally, simulation results are used to show the effectiveness of the introduced methods.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2019.1675797