The technological frontier and performance barrier for control and management of many present-day engineering systems lie in their complexity. Consequently, complexity management has become an essential part of control systems design. An emerging approach to controlling such systems consists of a decomposition of the control actions into a sequence of modes, each of which is defined for a particular task, operating point, or data source. The central question is how to schedule the various modes in order to optimize the system's performance. Related questions concern the development of real-time algorithms for performance improvement and the tradeoff between the size of the mode set and the system's performance.
This project will answer the above questions by casting them in the setting of optimal control, by combining techniques from hybrid systems, motion description languages, and numerical optimization. This new approach to controlling complex systems will advance the state of the art of supervisory controller design by providing effective algorithms for off-line computation of optimal controls, and for real-time implementation of suboptimal controls.
On the technological side, the framework for optimal timing control of multi-modal systems has far-reaching implications in a variety of application domains. For instance, optimal scheduling of robotic tasks can be crucial for the success of autonomous planetary explorations, mine sweeping, search-and-rescue applications, and other military missions. On the educational side, a new controls curriculum will be developed at the Georgia Institute of Technology that combines computer-science techniques with the traditional approach to signals and systems.
