Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

Optimal fuzzy control based on SAC reinforcement learning for nonlinear and chaotic fractional-order systems

Document Type : Original Article

Authors
sepideh nikfahm 1
saeed mizajani 2
Aghileh Heydari 1
1 Department of Mathematics, Payame Noor University, Tehran, Iran
2 Department of Basic sciences, Technical and Vocational University (TVU), Tehran, Iran
10.22034/jfsa.2026.568605.1296
Abstract
In this work, a Guaranteed-Cost Fuzzy Gain-Composite (GC-FGC) control scheme was
introduced to stabilize a specialized class of chaotic fractional-order Takagi–Sugeno (T–S) fuzzy
nonlinear optical systems. The controller design was grounded in fractional-order Lyapunov theory
and formulated using Linear Matrix Inequality (LMI) conditions, enabling the effective handling of
the complex chaotic dynamics inherent in such systems. The proposed method ensures asymptotic
stability and further incorporates a dynamics-free control strategy that remains robust in the presence
of system uncertainties and input saturation constraints. To decouple the control rules from the specific
system dynamics, the design leverages the norm-bounded nature of the chaotic system states.
Furthermore, a deep reinforcement learning framework based on the Soft Actor-Critic (SAC) algorithm
was employed to fine-tune the internal coefficients of the GC-FGC controller. By optimizing a reward
function through the SAC agent's neural network, an optimal policy was derived that guarantees finitetime convergence and satisfied the sliding surface reachability conditions. The effectiveness and
applicability of the proposed control framework were verified through comprehensive simulations and
two illustrative numerical case studies
Keywords
T-S fuzzy model
fractional-order systems
Guaranteed-cost fuzzy gain-composite control
Lyapunov theory
nonlinear optical systems
Subjects

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Fuzzy Systems and its Applications
Volume 9, Issue 1 - Serial Number 18
Open Access Statement
June 2026
Pages 1-36

  • Receive Date 26 December 2025
  • Revise Date 21 February 2026
  • Accept Date 13 April 2026