CONVEX OPTIMIZATION FOR FINITE-HORIZON ROBUST COVARIANCE CONTROL OF LINEAR STOCHASTIC SYSTEMS

This work addresses the finite-horizon robust covariance control problem for discrete time, partially observable, linear systems affected by random zero mean noise and deterministic uncertain-but-bounded disturbances restricted to lie in what is called ellitopic uncertainty set (e.g., finite intersection of centered at the origin ellipsoids/elliptic cylinders). Performance specifications are imposed on the random state-control trajectory via averaged convex quadratic inequalities, linear inequalities on the mean, chance constrained linear inequalities as well as convex-monotone constraints on the covariance matrix. For this problem we develop a computationally tractable procedure for designing affine control policies, in the sense that the parameters of the policy that guarantees the aforementioned performance specifications are obtained as solutions to an explicit convex program. Our theoretical findings are illustrated by a numerical example.

Automated summary

This work tackles the finite-horizon robust covariance control problem for discrete time, partially observable, linear systems with constraints on deterministic uncertain-but-bounded disturbances. A computationally tractable procedure for designing control policies is developed, ensuring the required performance specifications. The theoretical findings are demonstrated through a numerical example.