A new sufficient condition for robust root clustering of linear state-space models with structured uncertainty

A new sufficient condition is proposed for robust root clustering in an arbitrary subregion of the complex plane for both linear continuous time and linear discrete time uncertain systems. The systems are described by state space models with structured parameter uncertainty. Explicit bounds on uncertain parameters are derived to guarantee root clustering inside any specified region which may be demanded by stability and performance robustness. Illustrative examples show that the results of the proposed method can be much less conservative compared with the results of existing methods