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This study focuses on the asynchronous  ∞ control problem for two-dimensional discrete-time hidden Markovian jump systems where the mode observation conditional probability matrix is partly known. Considering the original system modes are invisible, the observed modes emitted from an observer serve as an alternative for stability analysis and controller design where a mode observation conditional probability matrix is constructed to characterize the emission between system modes and observed modes. Specially, only partly known information of the mode observation conditional probability matrix is accessible. With the introduction of the free-connection weighting matrices, the asymptotic mean square stability criterion is firstly derived based on Lyapunov method. This introduction provides a further degree of relaxation and less conservatism is therefore achieved. Secondly, we present synthesis conditions for asynchronous  ∞ state feedback controller design given in terms of a set of interconnected linear matrix inequalities. Moreover, cluster concept based on the partitions of observed modes is adopted which helps to decrease the number of controllers and simplify the design complexity. A numerical example, regarding the cases with and without clustering of the observed modes, is presented to illustrate the effectiveness of the proposed method.

Through the test of regenerative heat exchanger, the switch time and the flow velocity of liquid passed through the heat exchanger are found to be the two important factors influencing the efficiency of heat exchanger, and the qualitative relation between the two factors is found. The test result gives a reference to the research, design and operation of the regenerative heat exchanger.

In the present work, a commercial CFD software package, FLUENT, was used to develop a three-dimensional model of pusher-type billet reheating furnace for the second high speed wire rod plant of XiangTan Iron and steel Co. Ltd. The purpose of the study was to gain a better understanding of the gas flow and velocity and pressure distribution in the furnace. The results show that the numerical results are in agreement with the practice and the characteristics of the furnace configuration. The CFD model can be used to improve the performance and structure by analyzing and studying the behavior of the reheating furnace.