Andrei Sperila

Andrei Sperila is an active researcher in control engineering specializing in model predictive control (MPC) for networked and distributed systems. His recent 2025 publications address critical challenges in distributed constraint management and closed-loop decomposition within networked MPC frameworks, demonstrating active collaboration with Alessio Iovine, Sorin Olaru, and Patrick Panciatici.

Research interests include:

• Control Theory: Fundamental principles and advanced methodologies
• Model Predictive Control: Algorithm development and optimization
• Networked Control Systems: Control over communication networks
• Distributed Control: Multi-agent coordination strategies
• Real-time Optimization: Computational efficiency for control applications
• Constraint Handling: System limitation management techniques

This work bridges theoretical control advancements with practical implementations in complex interconnected infrastructures.

His publication trends reveal a focused trajectory in enhancing MPC for distributed networked environments. The 2025 two-part series on Network-Realized MPC demonstrates innovative approaches to subsystem decomposition and constraint management, showing significant potential for scaling control solutions to large-scale systems like power grids and transportation networks where distributed decision-making and communication constraints are critical factors.