Risk‐aware self‐triggered linear quadratic control

Abstract

AbstractThis paper presents a self‐triggered control approach with the view of risks for discrete‐time linear stochastic systems. More specifically, under the assumption that the first two moments of the disturbance distribution are known, the paper considers the problem of designing a control law that reduces the use of communication and power resources by aperiodically sampling the states and updating control inputs, while achieving a given performance goal at a risk below a specified level. This problem is approached by using the worst‐case Conditional Value‐at‐Risk as a measure of risk, to quantify the tail behavior of the stochastic systems, thus allowing us to suppress large losses. A numerical example is provided to illustrate the performance of the proposed approach.