Risk-Aware Event- and Self-Triggered Controls by Worst-Case Conditional Value-at-Risk

After introducing a risk-aware stability definition, this paper investigates the problems of the event- and self-triggered control for discrete-time linear stochastic systems in the view of risk. In particular, the risk is quantified by the worst-case Conditional Value-at-Risk (CVaR) using the first two moments of the uncertainty probability distributions. This allows us to design triggering mechanisms considering the risk that the estimated worst-case tail behaviors of the system are worse than a given threshold. Numerical examples are included to illustrate the performances of the proposed approaches.