Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model

2001 ◽  
Vol 9 (3) ◽  
pp. 381-392 ◽  
Author(s):  
Chung-Shi Tseng ◽  
Bor-Sen Chen ◽  
Huey-Jian Uang
Keyword(s):  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Tracking Control ◽  
Fuzzy Model ◽  
Control Design ◽  
Nonlinear Dynamic Systems

Evolving Granular Fuzzy Model-Based Control of Nonlinear Dynamic Systems

2015 ◽  
Vol 23 (4) ◽  
pp. 923-938 ◽  
Author(s):  
Daniel Leite ◽  
Reinaldo M. Palhares ◽  
Victor C. S. Campos ◽  
Fernando Gomide
Keyword(s):  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Fuzzy Model ◽  
Nonlinear Dynamic Systems ◽  
Model Based Control ◽  
Model Based

An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems

Automatica ◽  
2008 ◽  
Vol 44 (5) ◽  
pp. 1418-1425 ◽  
Author(s):  
S.P. Moustakidis ◽  
G.A. Rovithakis ◽  
J.B. Theocharis
Keyword(s):  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Tracking Control ◽  
Nonlinear Dynamic Systems ◽  
Neuro Fuzzy

Application of a Fuzzy Model to the Task of Filtering in Nonlinear Dynamic Systems

2020 ◽  
pp. 85-100
Author(s):  
N.P. Demenkov ◽  
D.M. Tran
Keyword(s):  
Kalman Filter ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Fuzzy Model ◽  
Nonlinear Dynamic Systems ◽  
S Model ◽  
Takagi Sugeno ◽  
Softening Coefficient

In this paper, we consider various approaches to the problem of filtering in nonlinear dynamic systems and their algorithms. The Strong Tracking Unscented Kalman Filter, based on the combination of Unscented Kalman Filter and Strong Tracking Kalman Filter, provides stability to the uncertainty of the process model directly using a suboptimal scaling factor (SSF). The softening coefficient is part of the SSF and it improves the smoothness of the system state assessment. The coefficient is determined empirically and is included in the entire filtering process, which leads to a loss of accuracy in the time segments in which the process model is defined. The paper explores the use of Takagi --- Sugeno fuzzy model (T-S model) to adjust in real time the softening coefficient when the object's dynamics changes. As a result of a comparative analysis of the accuracy of the studied filters for the nonlinear model, it was found that the new filter using a fuzzy logical adaptive system possesses good smoothness of assessment and the greatest accuracy

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