scholarly journals Control of Nonlinear Systems Using Multiple Model Black-Box Identification

2013 ◽  
Vol 46 (23) ◽  
pp. 582-587 ◽  
Author(s):  
Sergey Kolyubin ◽  
Denis Efimov ◽  
Vladimir Nikiforov ◽  
Alexey Bobtsov
Keyword(s):  
Nonlinear Systems ◽  
Black Box ◽  
Multiple Model

scholarly journals Reachability of Black-Box Nonlinear Systems after Koopman Operator Linearization

2021 ◽  
Vol 54 (5) ◽  
pp. 253-258
Author(s):  
Stanley Bak ◽  
Sergiy Bogomolov ◽  
Parasara Sridhar Duggirala ◽  
Adam R. Gerlach ◽  
Kostiantyn Potomkin
Keyword(s):  
Nonlinear Systems ◽  
Black Box ◽  
Koopman Operator

Adaptive Filter Design for FDI in Nonlinear Systems Based on Multiple Model Approach

2003 ◽  
Vol 36 (5) ◽  
pp. 645-650 ◽  
Author(s):  
D. Theilliol ◽  
M. Rodrigues ◽  
M. Adam-Medina ◽  
D. Sauter
Keyword(s):  
Nonlinear Systems ◽  
Adaptive Filter ◽  
Filter Design ◽  
Multiple Model ◽  
Model Approach

On Transient Improvement of Variable Structure Controlled Systems Using Multimodel Switching 1

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Mehmet Akar
Keyword(s):  
Nonlinear Systems ◽  
Sliding Mode ◽  
Variable Structure ◽  
Sliding Mode Controller ◽  
Multiple Model ◽  
Robust Tracking ◽  
Controlled Systems ◽  
Control Scheme ◽  
Transient Improvement ◽  
Simulation Results

This paper presents a multiple model/controller scheme for robust tracking of a class of nonlinear systems in the presence of large plant uncertainties and disturbance. Each model is associated with a sliding mode controller, and a switching logic is designed to pick the model that best approximates the plant at each instant. Theoretically, it is shown that the proposed control scheme achieves perfect tracking despite the existence of disturbance, whereas simulation results verify the improvement in the transient performance.

Fault Detection and Isolation for an Uncertain Takagi-Sugeno Fuzzy System using the Interval Approach

2015 ◽  
pp. 364-389 ◽  
Author(s):  
Hassene Bedoui ◽  
Atef Kedher ◽  
Kamel Ben Othman
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Fuzzy System ◽  
Fault Detection And Isolation ◽  
Upper And Lower Bounds ◽  
Multiple Model ◽  
Interval Approach ◽  
System Uncertainties ◽  
Takagi Sugeno ◽  
Detection And Localization

This work deals with the fault detection and localization in the case of uncertain nonlinear systems. The presented method uses the diagnosis based on mathematical models. To model nonlinear systems, the multiple model approach is used. This method uses the Takagi-Sugeno fuzzy systems principle to obtain a nonlinear system named multiple models. This modeling principle has the advantage of obtaining a general model that can describe any class of nonlinear systems. This modeling principle also allows one to obtain the generalization of many results that are already obtained for linear systems to the nonlinear systems. To model the system uncertainties, the interval approach is used because the faults or disturbances are generally unknown, but it is possible to know their upper and lower bounds. The proposed technique is insensitive to measurement uncertainties and highly reliable in case of a fault affecting the outputs system.

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