Abstract
The Model predictive control (MPC) is a generally accepted control method which has been widely employed in the industry processes. This control strategy is based on an established model of the true system. The selection of the appropriate model is often costly. For this reason the state-space model parameters should be estimated with maximum accuracy. To ameliorate the system tracking and reduce the experimental costs, the Kalman filter (KF) was introduced. In this paper a novel technique of the plant parameter estimation for MPC purposes was verified on the multivariable nonlinear water tanks system. The linearized and discretized water tanks model has been employed in the control design. The fundamental objective of the identification experiment was to estimate the plant model parameters subject to additive white noise affecting the output of the model. The introduced scheme was verified using a numerical examples, and the results of the control performance and the state estimates were discussed.
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Acknowledgement
I am deeply indebted to Prof. M. Åšwiercz, Faculty of Electrical Engineering, Bialystok University of Technology, for his guidance during model design. The present study was supported by a grant S/WI/3/18 from the Bialystok University of Technology and funded from the resources for research by the Ministry of Science and Higher Education.
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Jakowluk, W. (2019). Design of a State Estimation Considering Model Predictive Control Strategy for a Nonlinear Water Tanks Process. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_38
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