Distributed MPC

Description
Distributed model predictive control (MPC) is a powerful control method for systems subject to state and input constraints. This course covers the theory of model predictive control when applied to large-scale/networked systems, focusing on how computations can be distributed across multiple agents. 

Tentative content:

• Introduction to distributed systems and control 
• Non-cooperative and cooperative DMPC algorithms
• Distributed optimization and applicatons to MPC
• Dual-decomposition-based methods and design of terminal ingredients for MPC
• Localized and Distributed System Level Synthesis 

Requirements
Basic courses in control, advanced course in optimal control, basic MPC course strongly recommended.
Background in linear algebra is recommended.

Literature
Lecture notes will be provided.