A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions. (24th June 2021)

• Record Type: Journal Article
• Authors: Xu, Daxing; Hu, Aiyu; Han, Xuelong; Zhang, Lu
• Other Names: Aguilar-Ibanez Carlos Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-06-24
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/5124841
• Languages: English
• ISSNs: 1076-2787

Abstract

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.