Tingliang Hu

Papers

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About

Tingliang Hu is a control theorist whose work focuses on robust adaptive control for complex nonlinear systems. His most-cited paper, "Robust adaptive neural control of a class of nonlinear systems" (2007), tackles the challenge of stabilizing uncertain continuous-time MIMO nonlinear systems. In this influential work, Hu employs multiple multi-layer neural networks to approximate unknown nonlinear functions, integrating robustifying control terms to ensure system stability despite model uncertainties. This approach bridges neural network approximation with rigorous control theory, offering a practical framework for systems where traditional models fall short. With 5 citations, this paper has laid groundwork for subsequent advances in intelligent control, particularly in applications requiring adaptability and robustness. Hu's research contributes to the broader field of nonlinear control, demonstrating how machine learning techniques can enhance classical control methodologies. His work is especially relevant for students and researchers exploring the intersection of neural networks and adaptive control, providing a clear example of how theoretical guarantees can be maintained while leveraging the flexibility of neural approximations.