People / Mircea Lazar
Principal investigator
Mircea Lazar
- Institution
- Eindhoven University of Technology
- Work package
- In NEON
- 2020 onwards
- Now
- Full professor, chair Constrained Control of Complex Systems, Control Systems group, Department of Electrical Engineering, Eindhoven University of Technology
WP5 co-applicant (control); supervisor of PhD candidate Victor Reyes Dreke and co-author of all his NEON papers.
Expertise: Model predictive and distributed control
Mircea Lazar is full professor at TU/e, holding the chair in Constrained Control of Complex Systems. He works on model predictive control and data-driven control, with applications in power systems, mechatronics and automotive systems.
PhD researchers supervised
Publications and talks
- LMI Synthesis of Multivariable Current Controllers for AC/AC Modular Multilevel Converters (journal-article, 2026)
- Optimal Ultimate Bound of Synchronized Multi-Agent Systems with Quantized Control Inputs: An Input-to-State Stability Approach (conference-paper, 2025)
- Hierarchical Model Predictive Control for Modular Multilevel Converters: A Linear Parameter Varying Approach (journal-article, 2024)
- Constrained Synchronization of Multi-Agent Systems Using Distributed MPC and Invariant Families of Terminal Sets (conference-paper, 2024)
- Multivariable control of ac/ac modular multilevel converters under square wave operation using Fourier expansion (conference-paper, 2024)
- Multivariable control of modular multilevel converters with convergence and safety guarantees (report, 2024)
- Practical deadbeat MPC design via controller matching with applications in power electronics (journal-article, 2023)
- Consensus-based distributed model predictive control applied to modular multilevel converters (conference-paper, 2023)
- Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters (journal-article, 2022)
- A consensus approach to distributed balancing control of modular multilevel converters (conference-paper, 2022)
- Consensus of hierarchical multi-agent systems with a time-varying set of active agents (report, 2022)