Uncertainty inference with applications to control systems

Xinjia Chen

doi:10.1117/12.2557633

23 April 2020 Uncertainty inference with applications to control systems

Xinjia Chen

Proceedings Volume 11425, Unmanned Systems Technology XXII; 114250U (2020) https://doi.org/10.1117/12.2557633
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Control systems are usually designed based on nominal values of relevant physical parameters. To ensure that a control system will work properly when the relevant physical parameters vary within certain range, it is crucial to investigate how the performance measure affected by the variation of system parameters. In this paper, we demonstrate that such issue boils down to the study of the variation of functions of uncertainty. Motivated by this vision, we propose a general theory for inferring function of uncertainties. By virtue of such theory, we investigate concentration phenomenon of bounded random vectors. We derive multidimensional concentration inequalities for bounded random vectors, which are substantially tighter as compared to existing ones. The new concentration inequalities are applied to investigate the performance of control systems with real parametric uncertainty. It is demonstrated much more useful insights of control systems can be obtained. Moreover, the concentration inequalities offer performance analysis in a significantly less conservative way as compared to the classical deterministic worst-case method.

Conference Presentation

Citation Download Citation

Xinjia Chen "Uncertainty inference with applications to control systems", Proc. SPIE 11425, Unmanned Systems Technology XXII, 114250U (23 April 2020); https://doi.org/10.1117/12.2557633

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