Abstract
This paper deals with state observers with respect to their usage in the model predictive control (MPC) based on state space model of the controlled system. In case of immeasurable states a state observer (filter) is used to calculate current states in each control step. The paper is especially focused to finite impulse filters (FIR) as these filters do not require knowledge of initial state - contrary to infinite impulse response (IIR) filters. Several linear filters are tested and compared with proposed filters based on quadratic and linear programming. Different filter lengths (horizons) were tested to investigate filters’ performance. Filters were tested in very noisy conditions to evaluate filter robustness and therefore its usability in real-time deployment. The simulations were carried out using data from a real-time laboratory (Amira DR300 Servo system). All the measurements and simulations were carried out in MATLAB/Simulink environment.
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© 2013 Springer International Publishing Switzerland
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Chalupa, P., Januška, P., Novák, J. (2013). State Observers for Model Predictive Control. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_30
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DOI: https://doi.org/10.1007/978-3-319-00542-3_30
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
Online ISBN: 978-3-319-00542-3
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