Research Areas

Scalable Control with Compositional, Hierarchical, and Learning-based Approaches


Existing control design and verification methods are severely limited in their applicability to large scale systems, such as infrastructure networks and coordinated fleets of autonomous vehicles. The growing sophistication of these systems demand equally sophisticated control methods that are applicable to the complex requirements and large-scale dynamical models describing their operation. To develop scalable control design and verification methods that address large systems with complex requirements, we are pursuing compositional, hierarchical, and learning-based approaches. The aim in compositional analysis is to derive system-level guarantees from properties of the subsystems, thus breaking apart intractably large problems into subproblems of manageable size. The aim in hierarchical control is to decompose the synthesis and verification tasks into layers, from high-level decision making to low-level control synthesis. As an example, the control of an autonomous vehicle may include a routing layer at the top that selects a sequence of road segments, which is then broken into maneuvers at the lower layer, followed by an online motion planner that provides reference signals for a low-level tracking controller. We are also developing data-driven methods for control design and verification, and using statistical learning theory to provide probabilistic performance guarantees and associated sample complexity bounds. The results of the theoretical research are applied to the research areas below.

Traffic Management for Smart Cities


Connected vehicles and infrastructure, combined with increased automation, promise to minimize congestion, enhance safety, and lower emissions. We are pursuing a hierarchical traffic control framework that integrates feedback strategies at the vehicle, road link, and network layers. At the vehicle layer we investigate how vehicle fleets (“platoons”) can dramatically increase the capacity of urban roads and intersections by maintaining a constant spacing between them (see this video for an experimental demonstration). At the road link layer, we bring tools from formal methods to develop reactive signal timing policies for finite horizon objectives, such as dissipating queues and avoiding saturation. At the network layer we study routing games, particularly focusing on transient phenomena rather than assuming equilibrium conditions. A particular focus of this work is to enable better response systems to assist local authorities in managing extreme demand, such as when entire counties have to be evacuated to protect the residents from a wildfire. In a separate project we are studying real-time allocation of curbspace, which is increasingly becoming a dynamic interface between people and vehicles instead of stationary parking space. We are developing pattern recognition tools using camera images to identify curb usage patterns, with the aim of dynamic allocation to different types of activity.

Spatial-Temporal Patterning in Biological Systems


As another application of the compositional and hierarchical approaches described in the first area above, we are investigating how spatial and temporal patterns emerge from local interactions in biological systems, both natural and synthetic. Such biological systems are typically high dimensional and nonlinear, involving interactions among neighboring cells as well as between cells and the environment, as in the emergence of gene expression patterns in developing embryos. Our research reveals theoretical conditions for spontaneous pattern emergence, as well as new perspectives for understanding how initial patterns are refined into later patterns. In recent work, we collaborated with experimental biologists to empirically demonstrate spontaneous contrasting patterning in systems of genetically engineered microorganisms, including colonies of bacteria communicating through diffusible molecules, as well as engineered yeast cells interacting by way of a computer-controlled light input. Our research provides insight into natural biological mechanisms and lays the groundwork for theory-based design of synthetic biological systems that can be used for microbial consortia, biomaterials, and tissue engineering. A further objective of this research area is to help synthetic biology develop into a scalable engineering discipline capable of creating complex, autonomous and controllable multicellular behavior.