Mixed Unscented Kalman Filter and Differential Evolution for Parameter Identification

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 256-259)
• Pages: 2347-2353
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.256-259.2347
• Online since: December 2012
• Authors: Ari Legowo, Zahratu H. Mohamad, Hoon Cheol Park
• Keywords: Differential Evolution, Kalman Filter (KF), Parameter Identification

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