In this paper, a new hybrid stabilization method based on \({\text{H}}_{\infty }\) and fuzzy logic is introduced for time-delayed linear systems. One of the challenges of the H∞ method is determining the optimal values of the uncertain upper limit and the parameters of the Riccati equation. In most of the relevant articles, these parameters are usually considered fixed (already known), but in this article, a fuzzy system estimates these parameters online. With this strategy, both the accuracy and response speed of the control system are improved. Moreover, the proposed approach enhances robustness against parametric variations, ensuring stability even under significant model uncertainties. A key challenge in time-delayed systems is guaranteeing stability and acceptable performance in the presence of uncertain dynamics, delay variations, and external disturbances, where conventional robust controllers may fail to provide the desired results. Motivated by this challenge, the proposed fuzzy H∞ approach aims to improve disturbance rejection while maintaining performance under uncertainty. The main contribution of this study is integration of a fuzzy system to tune the Riccati parameters favorably in H∞ control; to handle actuator defects by designing a reliable controller structure; and rigorous mathematical evidence and comprehensive simulation analysis perform better performance under various challenging scenarios. In the simulation, both structural uncertainty and disturbance have been applied to the system and the performance of the proposed control system has been compared with conventional \({\text{H}}_{\infty }\) . This methodology can be effectively applied to various engineering systems, including power grids, robotic networks, and communication systems, where time delay and uncertainties are critical challenges. The proposed method shows clear advantages over conventional H∞ controllers in terms of disturbance rejection, stability margin, and tracking accuracy under uncertain conditions. The results also demonstrate better performance, particularly when the signal trajectory changes suddenly. Specifically, numerical simulation results show that the proposed controller can drive all system states to convergence in less than 3s under nominal conditions. Moreover, it maintains satisfactory performance under sudden parameter variations and attenuates external disturbances within about 4 s, confirming the effectiveness and robustness of the proposed approach.
