A Robust Unscented Kalman Filter for Nonlinear Dynamical Systems with Colored Noise

2014 ◽  
Vol 2 (3) ◽  
pp. 310-315 ◽  
Author(s):  
M. Masoumnezhad ◽  
A. Jamali ◽  
N. Nariman-zadeh
Keyword(s):  
Kalman Filter ◽  
Dynamical Systems ◽  
Colored Noise ◽  
Unscented Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Nonlinear Dynamical

THE UNSCENTED KALMAN FILTER, A POWERFUL TOOL FOR DATA ANALYSIS

2004 ◽  
Vol 14 (06) ◽  
pp. 2093-2105 ◽  
Author(s):  
A. SITZ ◽  
U. SCHWARZ ◽  
J. KURTHS
Keyword(s):  
Kalman Filter ◽  
Data Analysis ◽  
Dynamical Systems ◽  
Unscented Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Simultaneous Estimation ◽  
Bayesian Filter ◽  
Nonlinear Dynamical

We present a derivation of the unscented Kalman filter (UKF) as an approximation to the optimal Bayesian filter equations. The potentials of the UKF are then demonstrated for the problem of simultaneous estimation of states and parameters from noise corrupted data of nonlinear dynamical systems. The UKF even works for the chaotic Chua system which includes nondifferentiable terms.

Transient responses of nonlinear dynamical systems under colored noise

2019 ◽  
Vol 127 (2) ◽  
pp. 24004
Author(s):  
Xiaole Yue ◽  
Yanyan Wang ◽  
Qun Han ◽  
Yong Xu ◽  
Wei Xu
Keyword(s):  
Dynamical Systems ◽  
Colored Noise ◽  
Nonlinear Dynamical Systems ◽  
Transient Responses ◽  
Nonlinear Dynamical

Mixed Unscented Kalman Filter and Differential Evolution for Parameter Identification

2012 ◽  
Vol 256-259 ◽  
pp. 2347-2353
Author(s):  
Ari Legowo ◽  
Zahratu H. Mohamad ◽  
Hoon Cheol Park
Keyword(s):  
Kalman Filter ◽  
Differential Evolution ◽  
Unscented Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Spray Coating ◽  
Parameters Estimation ◽  
Effluent Treatment ◽  
Nonlinear Dynamical ◽  
De Algorithm ◽  
Tank System

This paper presents parameters estimation techniques for coupled industrial tanks using the mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) method. UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. Meanwhile, liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. The aim is to model the coupled tank system using mixed UKF and DE method to estimate the parameters of the tank. First, a non-linear mathematical model is developed. Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. The obtained results demonstrate good performances.

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