Papers
22
Total Citations
575
H-Index
11
About
Alexander S. Poznyak is a prominent control systems researcher whose work spans robust nonlinear control, neural network-based identification, sliding mode theory, and state estimation for uncertain dynamical systems. Over more than two decades, he has made foundational contributions to the intersection of intelligent control and classical robust control frameworks, consistently addressing the challenge of controlling systems with incomplete knowledge and real-world uncertainties. His 2001 book on differential neural networks (155 citations) remains a landmark contribution, establishing rigorous theoretical foundations for using continuous-time neural networks in identification, neuro-observer design, and trajectory tracking. This work has significantly shaped how researchers approach nonlinear system identification under uncertainty. More recently, Poznyak has advanced sliding mode control with barrier Lyapunov functions to enforce full-state constraints in nonlinear and manipulator systems, earning over 200 citations across two influential papers published in 2021 and 2022. His research on observers — including high-gain, neuro-based, time-delay, and attractive ellipsoid formulations — demonstrates a sustained commitment to solving the state estimation problem under stochastic and deterministic disturbances. His contributions to robotic manipulator control, integrating neural adaptation with sliding mode compensation, further illustrate his ability to bridge theoretical rigor with engineering application. Poznyak's body of work represents an essential reference for researchers in advanced control theory.
Research Focus
Key Achievements
Top Papers
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- 6Robust asymptotic neuro-observer with time delay term18 citations · 2000
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- 10Robust high‐gain observer for nonlinear closed‐loop stochastic systems13 citations · 1999