Robustness to Incorrect System Models in Stochastic Control

Robustness to Incorrect System Models and Application to Data-Driven Learning in Stochastic Control

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Robustness to incorrect priors in partially observed stochastic control

We study the continuity properties of optimal solutions to stochastic control problems with respect to initial probability measures and applications of these to the robustness of optimal control policies applied to systems with incomplete or incorrect priors. It is shown that for single and multi-stage optimal cost problems, continuity and robustness cannot be established under weak convergence...

System design of stochastic models using robustness of temporal properties

Stochastic Robustness Synthesis Applied to a Benchmark Control Problem

Stochastic robustness synthesis is used to find compensators that solve a benchmark problem. The synthesis minimizes a robustness cost function that is the weighted quadratic sum of stochastic robustness metrics. These metrics probability of instability, probability of actuator saturation, and probability of settling time violation are estimated using Monte Carlo analysis. A simple search metho...

A robustness result for stochastic control

The solution of a stochastic control problem depends on the underlying model. The actual real world model may not be known precisely and so one solves the problem for a hypothetical model, that is in general different but close to the real one; the optimal (or nearly optimal) control of the hypothetical model is then used as solution for the real problem. In this paper we assume that, what is n...