Reduced-Order Filter Design of Fuzzy Stochastic Systems

2021 ◽  
pp. 215-241
Author(s):  
Xiaojie Su ◽  
Yao Wen ◽  
Yue Yang ◽  
Peng Shi
Keyword(s):  
Stochastic Systems ◽  
Filter Design ◽  
Reduced Order ◽  
Order Filter

Reduced-Order Filter Design for Discrete-Time Systems Corrupted with Multiplicative Noise

2010 ◽  
Vol 33 (4) ◽  
pp. 1287-1293 ◽  
Author(s):  
Adrian-Mihail Stoica ◽  
Mihai Barbelian ◽  
Valentin Pana ◽  
Claudiu Dragasanu
Keyword(s):  
Discrete Time ◽  
Multiplicative Noise ◽  
Filter Design ◽  
Reduced Order ◽  
Order Filter ◽  
Discrete Time Systems ◽  
Time Systems

Reduced Order Thrust Estimation for a Jet Engine

2005 ◽  
Author(s):  
Mattias Henriksson ◽  
Tomas Gro¨nstedt
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Process Model ◽  
Filter Design ◽  
Jet Engine ◽  
Reduced Order ◽  
Engine Model ◽  
Order Filter ◽  
Parameter Variations ◽  
The Difference

A reduced order thrust estimation filter is derived, based on a linearized process model and a low order disturbance description. The reduced filter is obtained by ignoring the measurement error on the two spool speeds. Robustness to engine to engine variability is evaluated by Monte Carlo simulations by use of a nonlinear RM12 jet engine model. Both measurement and process disturbance models are presented and used in the filter design. The difference between the reduced order filter and full order filter is negligible. The Monte Carlo simulations clearly show that Kalman based thrust estimation filters are sensitive to parameter variations caused by engine degradation or engine to engine variation.

L2 – L∞ reduced-order filter design for T-S fuzzy stochastic discrete systems

2018 ◽  
Vol 49 (10) ◽  
pp. 2061-2072 ◽  
Author(s):  
Taha Zoulagh ◽  
Bensalem Boukili ◽  
Abdelaziz Hmamed ◽  
Ahmed El Hajjaji
Keyword(s):  
Filter Design ◽  
Discrete Systems ◽  
Reduced Order ◽  
Order Filter
Sign in / Sign up

Export Citation Format

Share Document