.png){kind=icon}
Return
Observer Design for Sampled-Data Systems via Deterministic Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2020.3047226.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A unified approach is proposed to design sampled-data observers for a certain type of unknown nonlinear systems undergoing recurrent motions based on deterministic learning in this article. First, a discrete-time implementation of high-gain observer (HGO) is utilized to obtain state trajectory from sampled output measurements. By taking the recurrent estimated trajectory as inputs to a dynamical radial basis function network (RBFN), a partial persistent exciting (PE) condition is satisfied, and a locally accurate approximation of nonlinear dynamics can be realized along the estimated sampled-data trajectory. Second, an RBFN-based observer consisting of the obtained dynamics from the process of deterministic learning is designed. Without resorting to high gains, the RBFN-based observer is shown capable of achieving correct state observation. The novelty of this article lies in that, by incorporating deterministic learning with the discrete-time HGO, the nonlinear dynamics can be accurately approximated along the estimated trajectory, and such obtained knowledge can then be utilized to realize nonhigh-gain state estimation for the same or similar sampled-data systems. Simulation is performed to validate the effectiveness of the proposed approach.
Keywords:
Observers
Trajectory
Artificial neural networks
Nonlinear dynamical systems
Sampled data systems
Noise measurement
Radial basis function networks
Deterministic learning
high-gain observer (HGO)
nonhigh-gain observer (NHGO)
radial basis function network (RBFN)
sampled-data system
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
ShareSaveHigh-gain observers in the presence of measurement noise: A switched-gain approach.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveOn the performance of high-gain observers with gain adaptation under measurement noiseAUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveAutocrine TNF-α production supports CML stem and progenitor cell survival and enhances their proliferationBlood
OAAI
BloodIF0
OAAIShareSaveShareSaveA framework for nonlinear sampled-data observer design via approximate discrete-time models and emulationAUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSave
View more