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Mathematical Problems in Engineering
Volume 2015, Article ID 382324, 13 pages
Research Article

ANN Approach for State Estimation of Hybrid Systems and Its Experimental Validation

Department of Electrical Engineering, National Institute of Technology, Calicut, Kerala 673601, India

Received 1 October 2014; Accepted 2 February 2015

Academic Editor: Hak-Keung Lam

Copyright © 2015 Shijoh Vellayikot and M. V. Vaidyan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel artificial neural network based state estimator has been proposed to ensure the robustness in the state estimation of autonomous switching hybrid systems under various uncertainties. Taking the autonomous switching three-tank system as benchmark hybrid model working under various additive and multiplicative uncertainties such as process noise, measurement error, process–model parameter variation, initial state mismatch, and hand valve faults, real-time performance evaluation by the comparison of it with other state estimators such as extended Kalman filter and unscented Kalman Filter was carried out. The experimental results reported with the proposed approach show considerable improvement in the robustness in performance under the considered uncertainties.