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About

Xinmin Song is a rising leader in the field of resilient control for nonlinear cyber-physical systems, specializing in adaptive control strategies that ensure safety and stability under malicious cyber threats. His core research focuses on developing observer-based output feedback frameworks capable of withstanding denial-of-service (DoS) attacks and deception attacks—two of the most challenging threats to modern networked control systems. In his most cited work (2024, 19 citations), Song introduced a novel composite observer approach for dynamic event-triggered adaptive fuzzy control, enabling prescribed-time tracking even when DoS attacks intermittently block output signals. Another highly influential paper (12 citations) tackles the problem of unknown nonlinear deception attacks that corrupt sensor data, proposing an adaptive resilient output feedback controller that maintains performance despite injected counterfeit information. His work also addresses practical challenges such as input/output quantization under DoS attacks, with application to a single-link robot (4 citations). Song’s contributions are critical for the safe operation of autonomous vehicles, industrial automation, and other safety-critical cyber-physical systems. By combining rigorous theoretical guarantees with practical implementation considerations, he is shaping the next generation of attack-resilient control architectures.