Abstract

The use of persistently exciting data has recently been popularized in the context of data-driven analysis and control. Such data have been used to assess system-theoretic properties and to construct control laws, without using a system model. Persistency of excitation is a strong condition that also allows unique identification of the underlying dynamical system from the data.

In the first part of the talk, I will introduce the data informativity framework in order to work with data that are not necessarily persistently exciting. Within this framework, I will present necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems. When such informativity conditions hold, it is possible to construct controllers from the given data that do not necessarily determine the underlying system uniquely.

The second part of the talk will briefly address the important issue of online experiment design within the data informativity framework. Finally, the last but more extensive segment of the talk will present an online experiment design for system identification in the minimum possible steps.

Both seminars are hosted by Malcolm Smith

We strongly encourage you to attend the seminars in person. You can also connect via Zoom:

https://newnham.zoom.us/j/92544958528?pwd=YS9PcGRnbXBOcStBdStNb3E0SHN1UT09 Meeting ID: 925 4495 8528 Passcode: 203369