Abstract
In the paper the fuzzy Kalman filter (KF) is proposed to allow for adaptation to changing properties of the controlled process. The fuzzy KF is used to estimate both states and unmeasured disturbances of the nonlinear process. Further, a Model Predictive Control (MPC) based on the fuzzy representation of the nonlinear process is formulated. The performance of the proposed estimation fuzzy scheme and predictive controller is evaluated through computer simulations of the pH neutralization process. The pH neutralization process is widely recognized as a difficult control problem due to the strong nonlinearity of the process.
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Novák, J., Chalupa, P. (2013). Nonlinear State Estimation and Predictive Control of pH Neutralization Process. 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_29
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DOI: https://doi.org/10.1007/978-3-319-00542-3_29
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
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