Welcome to the Hybrid Systems Lab!
The lab is under the direction of Professor Claire Tomlin in the Electrical Engineering & Computer Sciences (EECS) Department at UC Berkeley, and the
Department of Aeronautics and Astronautics at Stanford University.
What Are Hybrid Systems?
Hybrid systems are complex systems which have discrete event dynamics as well as continuous time dynamics. (For example, an A/C unit has discrete modes, either on or off, but changes the temperature in a continuous way over time.) Other examples of
continuous systems controlled by discrete logic include
- Aircraft autopilot modes
- Chemical plants
- Coordinating processes
- Air and ground transportation
- Swarms of vehicles
Other examples of hybrid systems involve continuous systems with phased operation, such as
- Biological cell growth
- Walking robots
- Insect motion
Although we study many problems that can be modeled by hybrid systems, as well as more general robotics research, our lab focuses primarily on three application areas of hybrid systems.
- Autonomous Vehicles and Multi-Agent Control - This work focuses on STARMAC, our autonomous aerial robotic testbed. By developing new safety and perception algorithms, we hope to one day endow vehicles like aircraft and cars with the ability to automatically avoid collisions in a manner that is guaranteed to be safe.
- Stochasticity and Uncertainty in Air Traffic Control - The air traffic system at a busy airport can also be model as a hybrid system: the aircraft travel along continuous trajectories, while the runway configuration (i.e. which runways are in use) can be represented as discrete modes. By incorporating uncertain and random factors such as weather into the model, we hope to create algorithms that can aid air traffic controllers and help reduce costly (and frustratinging!) weather-related delays.
- Biological Modeling Using Hybrid Systems - Various biological systems can also be modeled using the hybrid framework: genes are either active or inactive, but concentrations of proteins or other chemicals are best modeled in a continuous fashion. By building new, more accurate models for specific biological systems (i.e. cancer pathways) we enable increased understanding of these complex dynamical systems.
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Last Updated: 10/15/2009