Computational Sampled-Data Nonlinear Control

点击翻译按钮获取中文摘要

While researchers have made major breakthroughs in controlling nonlinear continuous time systems overthe last two decades, more recently the research has started to focus on methods for controlling continuoustime systems based on discrete time models and over digital networks, because of the need to control increas-ingly complex systems and because of the increasing computer power typically available. The proposed workaims to use previously developed nonlinear continuous time control methods as a springboard to developnew, high performance control algorithms for general nonlinear systems based on discrete-time models. Thefocus will be on developing algorithms that are robust, adaptive, and don't require full measurement ofthe system state. Applications of these results will include the development of optimal treatment schedulesfor HIV patients, leading to a non-progressive state where treatment is no longer needed. Other applica-tions will include control and calibration of automotive engines where state constraints, which are easy toincorporate into certain optimization-based control strategies but which often lead to non-robust controlalgorithms, play an important role. The proposed work also aims to give new, improved stability conditionsfor networked nonlinear control systems, especially those using protocols that don't necessarily guaranteetransmission to every node of the communication network within a fixed amount of time. It is expected thatthese new stability conditions for networked control systems will give insight into the development of novelcommunication protocols which will, in turn, make networked control systems more successful and prevalent.Broader impact:The work in this proposal has the potential for broad impact via the role that control plays as an enablingtechnology. As suggested above, the ideas that we will investigate may help lead the way to efficient drugtreatment schedules for HIV patients leading to a non-progressive state, thereby eliminating the need forfurther drug therapy. In general, it should make it more feasible to rely on optimization-based automationalgorithms for a wide range of engineering tasks that impact all of society. The proposal will supportgraduate students who will present their findings at national and regional conferences. The results will bemade available, promptly, on the web and will be immediately incorporated into graduate courses at theproposing institution. The work will help to further shape UCSB's Center for Control Engineering andComputation, which is one of the leading programs in control education in the nation. It will strengtheninteractions with international partners, e.g., in Australia, France and Italy, will foster novel collaborativeefforts with theoretical biologists at the Fred Hutchinson Cancer Research Center in Seattle, and will furthercollaborative efforts with researchers at Ford Motor Company.