Regulation and Optimal Error Reduction in Linear Multivariable Systems
Abstract In this paper, we study a general problem of optimal error reduction in linear multivariable systems. We show how a symptotic regulation can be achieved along with minimization of the H∞ norm of the error transfer function matrix. These resulte are of significance in the development of a new approach to the design of multivariable control systems.
Simulating a radial-inflow turboexpander from its nozzle inlet to the impeller output as the single nozzle, and through theoretic analysis, this thesis concludes two general characteristic models for design and off-design steady-state of the turboexpanders. Based on experiment data, and generalized least squares method is used in imitation of the data, the coefficient solutions which are needed to describe the models have been obtained. Using the model to predict performance of the other two turboexpanders, the results show that the two general characteristic models in this thesis are more accurate and universal. Therefore, in design of turboexpanders, as two important models, they can give a base for optimizing design and off-design characteristic.