Decentralized Control of Complex Systems - 5AMAC554

Goal(s)

The goal of this course is the optimal constrained control of complex dynamical systems. Elements from control theory and optimization will be merged together in order to provide useful tools which will be further applied to various problems involving multi-agent dynamical systems and interconnected systems in general. Beside classic control challenges related to the centralized vs distributed vs decentralized approaches, the stabilization and the tracking performances of each agent, there are a series of constraints imposed by the interaction with the environment and between themselves (anti-collision, avoidance constraints) as well as solving a collaborative task (e.g., maintain a fixed formation). This is generally the case with vehicles evolving in the same physical space, collaborative robots or drones covering a certain area. Some application benchmarks like control and coordination of multiple drones, energy management in complex energy systems and water distribution networks are discussed.

Content(s)

1. An optimization-based approach for control of complex systems
1.1 Optimization-based control
1.2 Generic prediction models
1.3 Generation of a reference trajectory/profile
1.4 Set-theoretic elements
1.5 Mixed-integer representations in control design
2. Cooperative control of multi-agent dynamical systems
2.1 System description
2.2 Collision avoidance formulation
2.3 Area coverage for multi-agent systems in multi-obstacle environment
2.4 A tight configuration of multi-agent formation
2.5 Centralized MPC
2.6 Distributed MPC
2.7 Decentralized MPC
3. Stability analysis
4. Examples, simulations, benchmarks and applications
4.1 Flight control experiments of Unmanned Aerial Vehicles
4.2 Microgrid energy management
4.3 Decentralized supervision and control of water networks

Algorithms and programming,
Linear and non-linear control,
Optimal and predictive control