H∞ model reduction of linear systems with distributed delay

This paper is concerned with the problem of H_∞ model reduction for linear distributed-delay systems. For a given stable system containing both discrete and distributed delays in the state, our attention is focused on the construction of reduced-order model of the same structure, which guarantees the corresponding error system to be asymptotically stable and has a prescribed H_∞ norm error performance. This problem is solved through a delay-dependent approach. By defining a new Lyapunov functional and making use of the Newton-Leibniz formula, a delay-dependent bounded real lemma is first established in terms of linear matrix inequalities (LMIs). Based on this performance criterion and by employing the projection lemma, sufficient conditions are formulated for the existence of admissible reduced-order models. Owing to the presence of matrix inverse constraints, these conditions are intrinsically nonconvex. An algorithm based on the sequential linear programming matrix method is proposed for verifying these conditions, which can be readily implemented by using standard numerical software. Desired reduced-order models can be constructed if these conditions are satisfied. The effectiveness of the proposed model reduction method is illustrated via a numerical example.