Fuzzy Command Filter Backstepping Control for Incommensurate Fractional-Order Systems via Composite Learning

This paper investigates the command filter backstepping control of uncertain fractional-order generalized strict-feedback nonlinear systems with input nonlinearities and functional uncertainties based on a composite learning method. The main motivations are that the simplification of backstepping co...

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Bibliographic Details
Published inInternational journal of fuzzy systems Vol. 24; no. 7; pp. 3293 - 3307
Main Authors Alsaadi, Fawaz E., Zhang, Xiulan, Alassafi, Madini O., Alotaibi, Reem M., Ahmad, Adil M., Cao, Jinde
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022
Springer Nature B.V
Subjects
Artificial Intelligence
Calculus
Closed loop systems
Closed loops
Computational Intelligence
Control systems design
Controllers
Convergence
Engineering
Excitation
Feedback control
Fuzzy control
Fuzzy logic
Fuzzy sets
Laplace transforms
Learning
Management Science
Mathematical analysis
Nonlinear systems
Nonlinearity
Numbers
Operations Research
Parameter estimation
Parameters
Systems stability
Tracking control
Tracking errors
Backstepping control
Input nonlinearity
Parameter convergence
Fraction-order command filter
Mismatched uncertainty
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Summary:This paper investigates the command filter backstepping control of uncertain fractional-order generalized strict-feedback nonlinear systems with input nonlinearities and functional uncertainties based on a composite learning method. The main motivations are that the simplification of backstepping control by providing the fractional-order command filter to avoid the calculation of the derivatives of virtual controller functions, and parameters convergence can be achieved without the strict persistency of excitation condition via fractional-order composite learning laws. In the controller design, both tracking errors and prediction errors are used to update adjusted parameters. Moreover, the analytic computation of derivatives of virtual inputs is not required. A set of lemmas is provided to analyze the affect of the fractional-order command filter, and the parameter convergence can be achieved without persistent excitation condition based on the composite learning technique. The control performance can be improved from the asymptotic stability to the M-L stability in closed-loop system. Finally, simulation result verifies the performance of the proposed method.
ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-022-01344-6