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About

Feiqi Deng is a leading researcher in intelligent control and optimization, with a focus on neural dynamics, Markov jump systems, and cybersecurity. His work bridges theoretical rigor and practical application, particularly in time-varying quadratic programming—a cornerstone of AI and robotics. In his highly cited 2023 paper (19 citations), Deng introduced a discrete error redefinition neural network (D-ERNN) that redefines error monitoring and discretization to solve TV-QP problems with unprecedented efficiency. He also tackles critical security challenges in cyber-physical systems: his 2022 study (13 citations) developed a zero-sum game-based control method using model-free adaptive dynamic programming to defend unknown nonlinear Markov jump systems against false data injection attacks. More recently, Deng has advanced quantized output feedback tracking control for discrete-time periodic Markov jump systems with packet loss compensation (2024, 8 citations), addressing real-world communication constraints. His contributions are vital for resilient autonomous systems, and his work is widely cited for its innovative fusion of game theory, adaptive control, and neural network design. Deng’s research continues to shape how engineers build secure, adaptive controllers for complex, uncertain environments.