Recursive state estimators for general discrete-time stochastic nonlinear models

2002 ◽  
Author(s):  
E.E. Yaz ◽  
M.J. Bari
Keyword(s):  
Discrete Time ◽  
Nonlinear Models ◽  
State Estimators

and mixed control of discrete-time T–S fuzzy systems with local nonlinear models

2011 ◽  
Vol 164 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Jiuxiang Dong ◽  
Youyi Wang ◽  
Guang-Hong Yang
Keyword(s):  
Discrete Time ◽  
Fuzzy Systems ◽  
Nonlinear Models ◽  
Mixed Control

scholarly journals State estimation in a decentralized discrete time LQG control for a multisensor system

2017 ◽  
Vol 27 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Zdzislaw Duda
Keyword(s):  
Discrete Time ◽  
Global State ◽  
Linear Quadratic ◽  
Control Laws ◽  
Decentralized System ◽  
Multisensor System ◽  
State Estimators ◽  
Local Measurements ◽  
Information Pattern ◽  
The Cost

Abstract In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes. In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node.

scholarly journals Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

2016 ◽  
Vol 182 ◽  
pp. 18-24 ◽  
Author(s):  
Yajing Yu ◽  
Hongli Dong ◽  
Zidong Wang ◽  
Weijian Ren ◽  
Fuad E. Alsaadi
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Parameter Uncertainties ◽  
Fragile State ◽  
Delayed Neural Networks ◽  
State Estimators

A Dual-Frequency Approach Based on the Subspace Method for Identifying Nonlinear Vibration Structures

2016 ◽  
Author(s):  
M. W. Zhang ◽  
Y. M. Wei ◽  
S. Wei ◽  
Z. K. Peng ◽  
X. J. Dong ◽  
...  
Keyword(s):  
State Space ◽  
Discrete Time ◽  
Nonlinear Models ◽  
State Space Model ◽  
Accurate Estimation ◽  
Structural System ◽  
Dual Frequency ◽  
Continuous Time Model ◽  
Time Model ◽  
State Space System

Subspace-based state space system identification method is well known for its computational efficiency and robustness. However, when directly applied to nonlinear models, intrinsic drawbacks exist and may lead to less accurate estimation. This paper explains how the error is generated when transforming continuous-time model to discrete-time one and then presents an approach to identify nonlinear systems by choosing two different sample frequencies for different parts of the discrete-time state-space model. The method allows to identify the nonlinear coefficients and the underlying linear system more robustly based on only the input and output of the structural system. Several numerical examples are given to illustrate the correctness of the analysis and the effectiveness of the method.

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