scholarly journals New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

2011 ◽  
Vol 33 (5) ◽  
pp. 552-575 ◽  
Author(s):  
Basil M. Al-Hadithi ◽  
Agustín Jiménez ◽  
Fernando Matía
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Nonlinear Systems ◽  
Extended Kalman Filter ◽  
New Methods ◽  
Model Based ◽  
Sugeno Model ◽  
Takagi Sugeno

New Concepts for the Estimation of Takagi-Sugeno Model Based on Extended Kalman Filter

2014 ◽  
pp. 3-24 ◽  
Author(s):  
Basil Mohammed Al-Hadithi ◽  
Agustín Jiménez ◽  
Fernando Matía ◽  
José Manuel Andújar ◽  
Antonio Javier Barragán
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Model Based ◽  
New Concepts ◽  
Sugeno Model ◽  
Takagi Sugeno

Fuzzy optimal control using generalized Takagi–Sugeno model for multivariable nonlinear systems

2015 ◽  
Vol 30 ◽  
pp. 205-213 ◽  
Author(s):  
Basil Mohammed Al-Hadithi ◽  
Agustín Jiménez ◽  
Ramón Galán López
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Sugeno Model ◽  
Takagi Sugeno ◽  
Multivariable Nonlinear Systems

scholarly journals Tracking: B

2021 ◽  
pp. 193-204
Author(s):  
Jean Walrand
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
Extended Kalman Filter ◽  
Random Variable

AbstractIn Chapter Tracking: A, we explained the estimation of a random variable based on observations. We also described the Kalman filter and we gave a number of examples. In this chapter, we derive the Kalman filter and explain some of its properties. We also discuss the extended Kalman filter.Section 10.1 explains how to update an estimate as one makes additional observations. Section 10.2 derives the Kalman filter. The properties of the Kalman filter are explained in Sect. 10.3. Section 10.4 shows how the Kalman filter is extended to nonlinear systems.

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