1. Hierarchical, distributed and multi-agent control for ATM
B. De Schutter
Control Lab, Fac. ITS, Delft Univ. of Technology
Centralized vs decentralized control
Hierarchical control
Free flight
Hierarchical multi-agent control

2. Centralized vs decentralized control
on-line real-time control
modular, scalable
locally optimal performance
instability, conflicts
global performance
required information bandwidth
computational complexity
lack of scalability

3. Hierarchical control compromise between centralized & distributed
local controllers
higher-level coordination
safety & global performance
tractable & scalable

4. Current ATM limitations
predefined corridors  inefficient airspace utilization
indirect routing, non-optimal altitude & speed
trajectories do not exploit favorable winds
results: increased flight time + fuel consumption
ATM complexity
current ATM architecture is mainly centralized (USA)
results: heavy workload for air traffic controllers, unnecessary ground and air holding, not fault tolerant
growing demand for air travel

5. Future concepts [Tomlin&Sastry]
free flight
pilots determine own routes, speed, altitude
restrictions imposed in congested air space: close to airport  control tower
aircraft coordinate to predict and resolve conflicts: decentralized ATM
benefits: reduced flight times & fuel burned, increased fault tolerance & capacity
hierarchical multi-agent control

6. Hierarchical multi-agent control

7. Control design techniques
decision support systems: heuristics, AI, fuzzy logic, scheduling, operations research & optimization
multi-agent controllers: game theory, automated learning, adaptive control, model predictive control
verification: automata, hybrid systems

8. References (free flight & multi-agent control)
C. Tomlin, G. Pappas, J. Lygeros, D. Godbole, S. Sastry, G. Meyer, “Hybrid control in air traffic management systems,” Proc. 13th IFAC World Congress (IFAC'96), San Francisco, pp , June-July 1996.
G. Pappas, C. Tomlin, J. Lygeros, D. Godbole, S. Sastry, “A next generation architecture for air traffic management systems,” Proc. 36th IEEE Conference on Decision and Control, San Diego, pp , Dec