ℋ2 optimal sensing architecture with model uncertainty

In this paper we present an integrated approach to control and sensing design. The framework assumes sensor noise as a design variable along with the controller and determines l
₁
regularized optimal sensing precision that satisfies a given closed loop performance in the presence of model uncertainty. We pursue two approaches here. In the first approach, we represent the uncertainty as polytopic and, in the second formulation, we model it using integral quadratic constraints (IQC). We apply these two approaches to an active suspension control and sensing design problem and demonstrate that the IQC based approach provides better results and is able to incorporate larger system uncertainty.