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

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

scholarly journals A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2208 ◽  
Author(s):  
Xiaoli Wang ◽  
Liangqun Li ◽  
Weixin Xie
Keyword(s):  
Kalman Filter ◽  
Particle Filtering ◽  
Unscented Kalman Filter ◽  
Fuzzy Model ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Model Probability ◽  
Fuzzy Parameter ◽  
Takagi Sugeno

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).

Development of an algorithm for tuning a suboptimal scaling factor in the SINS filtering problem

2020 ◽  
Keyword(s):  
Process Model ◽  
Unscented Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Scaling Factor ◽  
Fuzzy Filter ◽  
Nonlinear Dynamical ◽  
Sugeno Model ◽  
Takagi Sugeno ◽  
Softening Coefficient ◽  
Filtering Problem

A modified strong tracking unscented Kalman filter for nonlinear dynamical systems is proposed. A matrix of the suboptimal scaling factor is introduced into the prediction covariance to ensure evaluation stability and smoothness at appearance of the process model uncertainty. It is shown that the use of a fuzzy algorithm to adjust the softening coefficient in real time allows to avoid the loss of accuracy in the segments in which the process model is defined. As a result of modeling the SINS correction task, it was found that the proposed fuzzy filter has good evaluation smoothness and high accuracy. Keywords SINS; suboptimal scaling factor; softening coefficient; fuzzy Takagi — Sugeno model

FUZZY SUPERVISED NONLINEAR PID CONTROL OF A CLASS OF UNKNOWN NONLINEAR SYSTEMS

2010 ◽  
Vol 19 (08) ◽  
pp. 1847-1862 ◽  
Author(s):  
L. TOUNSI REKIK ◽  
MOHAMED CHTOUROU
Keyword(s):  
Dynamic Systems ◽  
Fuzzy System ◽  
Nonlinear Dynamic ◽  
Design Method ◽  
Fuzzy Model ◽  
Nonlinear Process ◽  
Nonlinear Dynamic Systems ◽  
Identification Algorithm ◽  
Potential Applications ◽  
Fuzzy Supervisor

Fuzzy control has been successfully applied in many cases to which conventional control algorithms are difficult to be applied. Recently, it was proven that fuzzy systems are capable of approximating any real continuous function to arbitrary accuracy. This result motivates us to use the fuzzy identifiers for nonlinear dynamic systems and then design the fuzzy supervised nonlinear PID controller based on the fuzzy system. There are two main objectives in this paper: (1) We use the Takagi and Sugeno's fuzzy models as an identifier for nonlinear dynamic systems, and derive the identification algorithm, (2) the fuzzy supervisor design method for tracking control is proposed based on this fuzzy system. In order to improve the dynamic response of the closed loop fuzzy model, the optimization of the performance of the fuzzy supervisor will be considered. To prove the potential applications of the proposed strategy, simulation was carried out for the speed control of a DC motor with serial excitation and a first order nonlinear process.

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