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A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions
- Source :
- Complexity, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi-Wiley, 2021.
-
Abstract
- With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm.
- Subjects :
- Estimation
0209 industrial biotechnology
State variable
Fusion
Multidisciplinary
General Computer Science
Article Subject
Computer science
02 engineering and technology
State (functional analysis)
QA75.5-76.95
Object (computer science)
Nonlinear system
020901 industrial engineering & automation
Development (topology)
Electronic computers. Computer science
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 10990526 and 10762787
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Complexity
- Accession number :
- edsair.doi.dedup.....c1b25592539f2a93e3d2b9cebe5f26b2