64 research outputs found
Guaranteed performance analysis and controller synthesis for interconnected linear systems from noisy input-state data
The increase in available data and complexity of dynamical systems has
sparked the research on data-based system performance analysis and controller
design. Recent approaches can guarantee performance and robust controller
synthesis based on noisy input-state data of a single dynamical system. In this
paper, we extend a recent data-based approach for guaranteed performance
analysis to distributed analysis of interconnected linear systems. We present a
new set of sufficient LMI conditions based on noisy input-state data that
guarantees performance and have a structure that lends
itself well to distributed controller synthesis from data. Sufficient LMI
conditions based on noisy data are provided for the existence of a dynamic
distributed controller that achieves performance. The
presented approach enables scalable analysis and control of large-scale
interconnected systems from noisy input-state data sets
Data-driven dissipative verification of LTI systems:Multiple shots of data, QDF supply-rate and application to a planar manipulator
We present a data-driven dissipative verification method for LTI systems based on using multiple input-output data. We assume that the supply-rate functions have a quadratic difference form corresponding to the general dissipativity notion known in the behavioural framework. We validate our approach in a practical example using a two-degree-of-freedom planar manipulator from Quanser, with which we demonstrate the applicability of multiple datasets over one-shot of data recently proposed in the literature
Data-driven input-to-state stabilization with respect to measurement errors
We consider noisy input/state data collected from an experiment on a
polynomial input-affine nonlinear system. Motivated by event-triggered control,
we provide data-based conditions for input-to-state stability with respect to
measurement errors. Such conditions, which take into account all dynamics
consistent with data, lead to the design of a feedback controller, an ISS
Lyapunov function, and comparison functions ensuring ISS with respect to
measurement errors. When solved alternately for two subsets of the decision
variables, these conditions become a convex sum-of-squares program. Feasibility
of the program is illustrated with a numerical example.Comment: Submitted for peer review on 31 March 2023. To appear in the
Proceedings of the 62nd IEEE Conference on Decision and Control, 13-15
December 2023, Singapor
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