Parameter and Time Delay Identification Method of Sensor Network System

Abstract

The invention discloses a parameter and time delay identification method of a sensor network system, which first uses a parameterization method to transform a parameter vector in an identification model into a high-dimensional sparse parameter vector, and then uses Householder transformation to simplify the greedy criterion and calculation method, A sparse identification method that avoids matrix inversion and identifies the position and value of non-zero elements in a sparse vector. The invention can be used to jointly estimate the time delay and parameters of the system under limited sampling data, so as to reduce the identification cost and have high precision.

Description

technical field [0001] The invention relates to the field of system identification, in particular to a parameter and time delay identification method of a sensor network system. Background technique [0002] Many practical systems in the industrial production process (such as thermal systems, chemical processes, sensor networks, wireless communication systems, etc.) contain unknown time delays, and the existence of unknown time delays will make the output of the system unable to respond in time. The stability becomes worse, which affects the control of the system. If the system model is known, the parameter identification of the system is the premise of controlling the system. System identification, control theory, and state estimation are three closely related research fields in modern control theory. Control theory and state estimation are inseparable from the mathematics of the system. Model, therefore, identification is the basis of control theory and state estimation, ...

AI Technical Summary

Problems solved by technology

CS theory is an important theory for the study of sparse systems. It can reconstruct unknown high-dimensional sparse signals based on low-dimensional observation data and measurement matrices. In many fields, sparse systems have been studied in depth based on this theory. However, in the control There is not much theoretical research

The CS theory shows that: under certain conditions, the sparse system can realize the estimation of the system parameters when the amount of sampled data is lower than the dimension of the system parameters. Commonly used compressive sensing reconstruction methods include convex optimization methods and greedy algorithms. Its superior performance and easy implementation have made great progress in recent years. The Orthogonal Matching Pursuit (OMP) algorithm is a typical greedy algorithm. It can obtain accurate reconstruction of the signal when there is no noise interference. On the basis of the OMP algorithm, a compression algorithm is also proposed. A series of improved algorithms such as sampling matching pursuit (CoSaMP) and regularized orthogonal matching pursuit (ROMP). The problem with this type of algorithm is that the algorithm needs to calculate the inverse matrix to find the least squares solution

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