https://doi.org/10.1140/epjs/s11734-025-01830-6
Regular Article
Fuzzy logic-based reinforcement learning control approach to fractal-order nonlinear chaotic systems
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 600127, Chennai, Tamil Nadu, India
Received:
10
April
2025
Accepted:
22
July
2025
Published online:
20
September
2025
This paper presents a novel reinforcement learning-based fractal fuzzy feedback control framework problem for fractal-order nonlinear chaotic systems. Unlike conventional stabilization techniques, the proposed approach effectively merges reinforcement learning with interval type-2 fuzzy logic to tackle the inherent challenges posed by fractal-order dynamics, such as irregular, self-similar, and nonlinear behaviors. While the impact of the non-parallel distributed compensation technique is integrated into the control framework to effectively mitigate the chaotic effects induced by fractal-order dynamics to enhance system stability. The use of interval type-2 fuzzy logic, with its enhanced capability to manage uncertainty and noise through interval-valued membership functions, further ensures robustness under a wide range of operational conditions. Moreover, the developed reinforcement learning-based control algorithm overcomes the limitations posed by insufficient training data and improves control efficacy. By employing Lyapunov stability theory, the convergence of the closed-loop system is ensured through the derived sufficient conditions. The validity and practical significance of the proposed methodology are demonstrated through two illustrative examples, highlighting its reliability and robustness to advance control strategies.
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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.
