https://doi.org/10.1140/epjs/s11734-024-01293-1
Regular Article
Quantum genetic algorithm-based memory state feedback control for T–S fuzzy system
1
Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), 624 302, Dindigul, Tamil Nadu, India
2
Flow and Material Simulation, Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
Received:
9
April
2024
Accepted:
3
August
2024
Published online:
16
August
2024
In this paper, the authors utilize a linear matrix inequality (LMI) technique for designing a quantum genetic algorithm (QGA)-based memory state feedback control of a nonlinear system. The performance of the proposed model is enhanced using the QGA-based algorithm for finding the control gain matrices as a searching tool. To evaluate the fitness function of QGA, the LMI problem is formulated as a constrained optimization. The more general Lyapunov–Krasovskii (LKFs) functional is selected to analyze the closed-loop system stability and the criterion for its asymptotic stability. Numerical examples are provided to verify the effectiveness of the QGA-based proposed control scheme.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
