A New Takagi-Sugeno Fuzzy Control Method for Cohen-Grossberg Neural Networks With State-Dependent Impulses

This paper investigates the asymptotic stability of Cohen-Grossberg neural networks (CGNNs) formulated within the Takagi–Sugeno (T–S) fuzzy framework, with a focus on state-dependent impulsive effects. Impulses occurring at nonfixed instants present significant analytical challenges due to their unpredictable nature, which complicates stability analysis. To address this, the B-equivalence method is applied to transform the system into one with fixed-time impulses by determining the B-equivalent impulsive operator. This transformation simplifies the stability analysis and enables the application of Lyapunov functional techniques, along with comparison principles, to derive sufficient conditions for the asymptotic stability of the T–S fuzzy CGNNs. The proposed impulsive fuzzy control scheme ensures system stability by integrating fuzzy logic to model nonlinearities and applying impulsive control at strategically selected intervals. Two numerical examples are provided to validate the theoretical results, demonstrating the practical applicability of the approach in stabilizing complex neural networks subject to state-dependent impulsive actions and highlighting its potential for real-world systems requiring adaptive, nonlinear control.