Control-Theoretic Analysis of Nonlinear and Uncertain Systems with Data-Driven and Learning Mechanisms
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Wanquan Liu
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Sun Yat-Sen University,
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Peoples Rep of China
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Jianwei Xia
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Liaocheng University
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Peoples Rep of China
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Xuefang Li
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Sun Yat-sen University
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Peoples Rep of China
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Introduction:
| | Nonlinear and uncertain dynamical systems remain a central topic in control theory and applied mathematics. Classical frameworks such as Lyapunov stability theory, adaptive control, and robust control have provided powerful tools for analyzing such systems. However, modern engineering systems increasingly operate in environments where accurate models are unavailable, system structures may vary, and measurements are data-rich yet incomplete. These challenges motivate the integration of data-driven and learning mechanisms into control design, raising new theoretical questions from a control-theoretic perspective.
This invited session focuses on the analysis and synthesis of nonlinear and uncertain control systems, with particular attention to how data-driven or learning components affect fundamental system properties such as stability, convergence, robustness, and performance bounds. Rather than emphasizing algorithmic or application aspects, the session highlights rigorous control-theoretic and mathematical analysis, extending classical nonlinear systems theory to settings involving adaptation, iteration, and data-based updates.
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