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This paper is concerned with the simultaneous stability of the multi-mode large-scale systems composed of the interaction subsystems. A novel distributed control network consisting of multiple network-based controllers with the partial information exchange is adopted to simultaneously stabilize the large-scale systems in multiple operation modes. In the distributed control network (DCN), a partial state information exchange approach is developed to save the real-time communication and computation resources. To compensate for the effects of dynamic couplings between interaction subsystems, the designed controllers use both the local states and the neighbors’ partial information with packet dropouts for local feedback design. Then, a series of Lyapunov functions are constructed to derive a matrix-inequality-based sufficient condition for the existence of the desired controllers. Based on an orthogonal complement technique, the gains of the controllers in DCN are parameterized. The iterative algorithm for the solution of simultaneous stabilization problem is also developed. Finally, a numerical example is performed to show the relevant feature of the proposed method.

This paper is concerned with the H ∞ control problem for a class of linear parameter-varying (LPV) systems with randomly multi-step sensor delays. A mathematical model which describes the randomly multi-step sensor delayed measurements for LPV systems is established. An improved Lyapunov functional is proposed to determine the asymptotically mean-square stability of the closed-loop system which depends on the varying parameters. The obtained full-order parameter-dependent dynamic feedback controller guarantees the considered system to be asymptotically mean-square stable and to satisfy the modified H ∞ performance for all possible delayed measurements. An extended cone complementarity linearization method (CCLM) is used to solve the constrained linear matrix inequality (CLMI). Simulation results illustrate the effectiveness of the proposed method.