Covariance Stabilization for a class of Stochastic Discrete-time Linear Systems using the S-Variable Approach
It provides a numerically efficient LMI formulation for covariance stabilization in SMPC, though more conservative than existing necessary and sufficient conditions.
This paper addresses covariance stabilization for stochastic discrete-time linear systems with i.i.d. additive and parametric uncertainties, proposing LMI-based conditions using the S-variable approach. The method reduces LMI size from O(n^3) to O(n^2), enabling more tractable robust controller design.
This paper deals with the problem of covariance stabilization for a class of linear stochastic discrete-time systems in the Stochastic Model Predictive Control (SMPC) framework. The considered systems are affected by independent and identically distributed (i.i.d.) additive and parametric stochastic uncertainties (potentially unbounded), in addition to polytopic deterministic uncertainties bounding the mean of the state and input parameters. The design conditions presented in this paper are formulated as Linear Matrix Inequalities (LMIs), using the S-variable approach in order to reduce the potential conservatism. These conditions are derived using a deterministic exact characterization of the covariance dynamics, the latter involves bilinear terms in the control gain. A technique to linearize such dynamics is presented, it results in a descriptor representation allowing to derive sufficient conditions for the design of a covariance-stabilizing controller. The derived condition is first compared with a known necessary and sufficient stability condition for systems without deterministic uncertainties and additive stochastic noise. Although more conservative, the proposed condition is more numerically tractable, with an LMI size scaling as O(n^2) instead of O(n^3). Then, the same condition is used to design controllers that are robust to both deterministic and stochastic uncertainties. Several numerical examples are presented for comparison and illustration.