In the realm of network control systems, the presence of uncertain factors, notably network delays, frequently precipitates a deterioration in control performance and system stability. To address this formidable challenge, we introduce a pioneering network control algorithm. This novel approach establishes an auto-regressive model to predict network time delays. The predictive model uses the parameter self-tuning Least Mean Square (LMS) algorithm for real-time prediction. Subsequently, we employ an enhanced generalized predictive control method rooted in the state-space model to rectify the adverse effects of time delays adeptly. Through comprehensive simulation studies, we substantiate the efficacy of our proposed method, showcasing its capacity for proficient time delay compensation and real-time performance enhancement. Its low computational requirements make it suitable for real-time applications. Furthermore, our technique showcases robustness in the face of interference and data loss, fortifying its effectiveness and applicability.
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