摘要:

The paper introduces a Riccati equation approach to synthesise of the full state observers and state feedback controllers for uncertain largescale systems. In this approach, if two given algebraic Riccati equations are solved, their solutions can be applied to synthesise the stabilising state feedback and observer gain matrices. The uncertainties considered in each subsystem may be time-varying and appear in the system matrices (matrixAi), input connection matrices (matrixBi), or/and output matrices (matrixCi). However the values of those uncertainties are constrained to lie within some known admissable bounds. Furthermore, the so-called matching conditions are not needed in the paper.

DOI:10.1049/ip-d.1992.0011

出版商:IEE    

年代:1992

数据来源: IET

摘要:

The problem of controller placements in large-scale linear systems is considered. Specifically, given a large-scale linear system it is desired to find the optimal number of linear controllers, the optimal locations in the system to place these controllers and the optimal feedback gain of each controller so that an objective function is optimised, and also so that other desirable properties of the controlled system, such as eigenstructure assignment, can occur. A formulation of the controller placement problem is presented. A general solution algorithm based on simulated annealing is developed for the controller placement problem. The solution algorithm can arrive at the globally optimal solution. The solution algorithm has been applied to an eleven-bay structure comprising a 132-dimension linear system with very promising results.

DOI:10.1049/ip-d.1992.0012

出版商:IEE    

年代:1992

数据来源: IET

摘要:

It is shown that the Laguerre network can be used to connect directly frequency response estimates to a continuous-time parametric model. This approach combines the smoothing effect of frequency windows with a systematic method for converting the nonparametric model into a parametric one.

DOI:10.1049/ip-d.1992.0013

出版商:IEE    

年代:1992

数据来源: IET

摘要:

A convenient means of extending generalised predictive control to the multivariable case is through the use of singular value decomposition. However, owing to the bicausal nature of this decomposition, special care has to be taken to ensure that the overall control law is causal; this can be achieved by giving up some of the available degrees of freedom, but the results obtained in this way are suboptimal. It is the purpose of this paper to remove this difficulty. Owing to the complexity of the problem, consideration is given only to the scalar case for which the singular value decomposition reduces to a gain/phase decomposition; the proposed approach can be extended to the multivariable case but this is not undertaken here. The efficacy of the algorithm developed in this paper is demonstrated by means of a numerical example.

DOI:10.1049/ip-d.1992.0014

出版商:IEE    

年代:1992

数据来源: IET