State estimation method of nonlinear system

A nonlinear system and state estimation technology, applied in the field of state estimation of nonlinear systems, can solve the problems of limited accuracy of unscented Kalman filtering and reduced estimation performance, and achieve the effect of self-adaptive adjustment and improvement of estimation accuracy.
CN107045490AInactive Publication Date: 2017-08-15QUZHOU UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
QUZHOU UNIV
Publication Date
2017-08-15
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a high-order volume Kalman filtering and neural network-based state estimation method. The method is realized through the following steps of: firstly establishing a state space model for a nonlinear system by utilizing a neural network; combining a weight of the neural network and a state variable of the system to serve as a new state variable; and updating the new state in real time by adoption of high-order volume Kalman filtering, so as to achieve the real approach, to a nonlinear system model, of the neural network and the accurate estimation of a state value. Through experimental simulation, the effectiveness of the method is proved.
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Description

technical field

[0001] The application belongs to the technical fields of aerospace, intelligent transportation, pattern recognition and medical engineering, and in particular relates to a state estimation method of a nonlinear system. Background technique

[0002] When the nonlinear system state model is unknown, the existing algorithms based on nonlinear filtering and neural network are limited in the accuracy of nonlinear system state estimation.

[0003] When the system model is unknown, it is a simple and effective method to use neural network to approximate the model nonlinearly. The neural network determines the nonlinear function approximated by the network according to the actual data set. However, when the state variables of the actual system are not completely measurable, the neural network alone will not be able to establish a model of the process. The state space model method describes the change relationship between the internal state of the system and the ext...

Claims

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