**Research Experience**

- Distributed Control and Estimation of Large-Scale Control systems
- Theory of Robust Estimation and Control
- Stochastic Systems and Stochastic Control
- Stability and Control of Quantum Systems

**Memberships**

Senior Member, Institute of Electrical and Electronics Engineers

**Research profile**

The main topic of Prof. Ougrinovski's research has been mathematical robust control systems theory. Control Theory is a branch of Applied Mathematics concerned with mathematical foundations that underpin methodologies for the analysis and design of engineering control systems. Within that broad area, the main direction of Prof. Ougrinovski's work has been concerned with mathematical tools for analysis and synthesis of robust control systems tolerant to modelling errors and disturbances. Robustness is a fundamental property of engineering systems that reflects their ability to maintain adequate performance in the face of imprecise a priori knowledge of system characteristics.

The early work of Prof. Ougrinovski was concerned with developing the method of stochastic H-infinity control which extended the mainstream H-infinity control design methodology into the realm of control systems subject to noises. The stochastic version of this theory developed by Prof. Ougrinovski has allowed this milestone control design methodology to be applied to systems operating under the influence of randomly varying noise. It showed that a non-conservative and tractable analysis is possible in some cases where the conventional main instruments of robust control analysis are not readily applicable.

In the late 1990s - early 2000s, Prof. Ougrinovski and his colleagues including Prof I.R. Petersen from UNSW and Prof M.R. James from the Australian National University developed the Minimax Linear-Quadratic-Gaussian (Minimax LQG) control design methodology. This methodology provided an important bridge between the modern methodology of robust control design via H-infinity methods, on one hand, and the classical method of optimal LQG control design, on the other hand. It allowed controllers with guaranteed robust performance to be designed for stochastic systems, where only partial information on the system is available for measurement. Since then, Prof Ougrinovski has successfully developed this methodology into a comprehensive theory which covers problems of decentralized control, robust control of Markov jump systems, robust filtering, hidden Markov models estimation.

In the last several years, Prof Ougrinovski's work has focused on the development of the theory of distributed control and estimation of large-scale complex systems. His work aims to tackle tough problems arising from system heterogeneity and information constraints. It is concerned with developing a systematic consensus-based theory of distributed robust estimation targeting systems and processes subject to noise and uncertainty in the environment, for which the conventional notions of consensus cannot be applied.