On simultaneously optimal decentralized performance

This work focuses on the simultaneously optimal decentralized performance problem. Specifically, a method that quantifies the best (in an l¹-l^∞ sense) dynamic performance achievable by one decentralized dynamic feedback compensator, for a finite family of process models, is presented. To achieve this goal, necessary and sufficient conditions for decentralized simultaneous stabilization are introduced. These conditions are realized through a set of quadratic equality constraints. Consequently, the decentralized simultaneous performance problem is formulated as a quadratically constrained minimax problem that is nondifferentiable and infinite dimensional. This problem is solved through iterative solution of appropriately constructed finite dimensional nonlinear programming problems. For small horizons, ε-globally optimal solutions of these NLP´s can be achieved. These concepts are employed in the solution of an illustrative example