Risk-aware stability of discrete-time systems
MP Chapman, DS Kalogerias - arXiv preprint arXiv:2211.12416, 2022 - arxiv.org
arXiv preprint arXiv:2211.12416, 2022•arxiv.org
We develop a generalized stability framework for stochastic discrete-time systems, where
the generality pertains to the ways in which the distribution of the state energy can be
characterized. We use tools from finance and operations research called risk functionals (ie,
risk measures) to facilitate diverse distributional characterizations. In contrast, classical
stochastic stability notions characterize the state energy on average or in probability, which
can obscure the variability of stochastic system behavior. After drawing connections …
the generality pertains to the ways in which the distribution of the state energy can be
characterized. We use tools from finance and operations research called risk functionals (ie,
risk measures) to facilitate diverse distributional characterizations. In contrast, classical
stochastic stability notions characterize the state energy on average or in probability, which
can obscure the variability of stochastic system behavior. After drawing connections …
We develop a generalized stability framework for stochastic discrete-time systems, where the generality pertains to the ways in which the distribution of the state energy can be characterized. We use tools from finance and operations research called risk functionals (i.e., risk measures) to facilitate diverse distributional characterizations. In contrast, classical stochastic stability notions characterize the state energy on average or in probability, which can obscure the variability of stochastic system behavior. After drawing connections between various risk-aware stability concepts for nonlinear systems, we specialize to linear systems and derive sufficient conditions for the satisfaction of some risk-aware stability properties. These results pertain to real-valued coherent risk functionals and a mean-conditional-variance functional. The results reveal novel noise-to-state stability properties, which assess disturbances in ways that reflect the chosen measure of risk. We illustrate the theory through examples about robustness, parameter choices, and state-feedback controllers.
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