The proposed research focuses on understanding the interaction between control and information, and applying the findings to the problems of control over communication networks, dynamic vision, and autonomous vehicles.
These applications have in common the constraint that there is a cost associated with information, which, due to finite resources, limits the available information for control.
These applications are examples of hybrid systems. Understanding how the limited information affects the performance achievable by control systems is a primary goal of this research. If we can learn how to systematically quantize the information without loosing stability or performance of the closed loop system, we can devise new methods of analysis, control, and design of hybrid systems, and directly impact many application fields.
These new problems require a multidisciplinary approach that relies on the rich experience in control of uncertain systems, adopts the new advances in optimization methods, and merges them with information theory concepts.
Preliminary research based on this approach has led to new system theory results for stability and performance guarantee with quantized state information for linear systems. These results are very promising and indicate that vast advances can be made by this study.
The mix of motivating applications and new theoretical problems offers a unique educational opportunity to the students that will be involved in the program. The multidisciplinary nature of the research program will provide material and motivation for two courses that will be developed. The first one, intended for undergraduates, concentrates on system design, particularly on the integration of control, communication, and microprocessors systems. The second one focuses on optimization methods for system design, analysis, and control, and presents optimization theory and computational methods as unifying tools across Electrical Engineering disciplines.
