Robust controller design for unstable systems using statistical confidence bounds

Robust control aims to account for model uncertainty in design. Traditional methods for robust control typically assume knowledge of hard bounds on the system frequency response. However, this does not match system identification procedures which typically yield statistical confidence bounds on the estimated model. Recent work in this area has proposed a procedure for obtaining a better match between robust control and system identification by using statistical confidence bounds for robust control design. The results presented here are a generalization of these results to the case of open loop unstable plants.