Synthesis of anisotropic suboptimal controllers by convex optimization
It provides a convex optimization approach for designing suboptimal controllers in a stochastic setting, relevant for control engineers dealing with systems under statistical uncertainty.
The paper addresses disturbance attenuation for linear systems under random disturbances with uncertain probability distributions, using anisotropic norm as a performance measure. It synthesizes fixed-order output-feedback controllers via convex optimization to achieve a prescribed anisotropic norm bound.
This paper considers a disturbance attenuation problem for a linear discrete time invariant system under random disturbances with imprecisely known probability distributions. The statistical uncertainty is measured in terms of relative entropy using the mean anisotropy functional. The disturbance attenuation capabilities of the system are quantified by the anisotropic norm which is a stochastic counterpart of the H-infinity norm. The designed anisotropic suboptimal controller generally is a dynamic fixed-order output-feedback compensator which is required to stabilize the closed-loop system and keep its anisotropic norm below a prescribed threshold value.