My research is concerned with the analysis and design of two types of control networks: (A) large scale networks of semi-autonomous computer controlled physical devices exhibiting continuous and logic behavior; (B) nano-scale networks of bio-molecular circuitry composed of genes and proteins in living cells. In my group, we develop theory, algorithms and experimental validations for both types of control networks.

State estimation and control in multi-agent decision and control systems (Supported by NSF and in part by TOYOTA)
This research is concerned with the modeling, estimation and control of multi-agent systems composed of physical devices that exhibit continuous and discrete behaviors (hybrid systems). Examples include multi-robot systems for combat or surveillance applications and intelligent transportation networks subject to safety and performance constraints. In particular, my research is concerned with the dynamic feedback problem for systems with continuous and discrete behavior. We focus on two main aspects: (1) complexity arising from imperfect state information, large system size, and the interplay of continuous dynamics and logic; (2) formal methods for the design of semi-autonomous systems, that is, systems in which some physical devices are human controlled while others are autonomous. Partial order theory and interval abstraction are employed to mathematically characterize key quantities and tackle complexity.

Dynamic Feedback for Hybrid Automata. Real life control of any system has to be robust to imperfect state information. We study the problem of safety control for hybrid automata with imperfect continuous/discrete state information. Imperfect state information arises from poor or missing sensory information or by the presence of decision agents (humans, for example) that are not controllable. We work on theory and algorithms for hybrid state estimation and dynamic feedback for hybrid automata. We focus on problems of complexity and device techniques that rely on partial order theory and interval abstraction to provide efficient solutions. Our leading application is collision avoidance at traffic intersections, mergings and roundabouts, which we first implement in our multi-agent decision and control lab, in which we re-create collision instances among multiple vehicles (autonomous and human driven) on circular geometries (see the lab wiki for more details and for movies of our experiments). Then, we transfer the algorithms on the software system of the SMART Lab at the TOYOTA Technical Center of Ann Arbor. Finally, we transport the system on full vehicles and test it on the several miles long test track of Ann Arbor.

Bio-molecular circuit design: Retroactivity, Insulation, and Modularity (Supported by AFOSR)
In living organisms, large networks of interactions between genes and proteins play a central role in determining the functioning of the cell. Recent technological developments have set the stage for fabricating synthetic bio-molecular networks in vivo (Synthetic Biology). This enables us to build circuitry with biological hardware to be implemented in cells to control cell behavior. While small working modules have been successfully built, larger networks composed of smaller modules are yet to be successfully fabricated: modular design, which we often take for granted in systems theory, is still not possible in bio-molecular systems. Our research focuses on identifying sources of ``non-modularity'', which we call retroactivity, on designing systems to counteract them, and on developing a bio-molecular systems theory with retroactivity.