Set-membership EIV identification through LMI relaxation techniques

In this paper the Set-membership Error-In-Variables (EIV) identification problem is considered, that is the identification of linear dynamic systems when both the output and the input measurements are corrupted by bounded noise. A new approach for the computation of the Parameters Uncertainty Intervals (PUIs) is discussed. First the problem is formulated in terms of non-convex semi-algebraic optimization. Then, a Linear-Matrix-Inequalities relaxation technique is presented to compute parameters bounds by means of convex optimization. Finally, convergence properties and computational complexity of the given algorithms are discussed. Advantages of the proposed technique with respect to previously published ones are discussed both theoretically and by means of a simulated example.