Multi-stimulation identification method for multivariable linear continuous system

A continuous system and identification method technology, applied in the field of multiple excitation identification, can solve problems such as low efficiency and loss of identification accuracy
CN106773702AInactive Publication Date: 2017-05-31SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI UNIVERSITY OF ELECTRIC POWER
Publication Date
2017-05-31
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention relates to a multi-stimulation identification method for a multivariable linear continuous system. The multi-stimulation identification method comprises the following steps: carrying out model identification on a multivariable linear continuous system with m-dimensional inputs; supposing that m groups of identification stimulation tests are carried out and the dimension of each group of the tests is m, forming an m*m-dimensional stimulation signal matrix by virtue of m groups of stimulation signals, wherein when the rank of the matrix is m (namely identifying that the identified stimulation signal matrix is full rank and an inverse matrix exists), a system model can be effectively identified under the action of the identified stimulation signal matrix, otherwise, the identified system model is invalid. According to the multi-stimulation identification method, guiding rules are provided for the stimulation signal design of multivariable system identification tests and the multivariable system identification.
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Description

technical field

[0001] The invention relates to an experimental signal processing technology, in particular to an excitation signal design requirement for identification of a multivariable linear continuous system and a multiple excitation identification method for a multivariable linear continuous system. Background technique

[0002] With the development of science and technology and social economy, modern industrial equipment is becoming larger and more complex. Coupling and correlation between links are common in the production process composed of many links. This kind of coupling and association shows that a certain input variable of the system will affect multiple output variables at the same time, or that a certain output variable will be affected by multiple input variables. This coupling and correlation pair has become a key difficult factor affecting multivariable system modeling and multivariable system control. The research on multivariable system identificatio...

Claims

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