Design of a State Estimation Considering Model Predictive Control Strategy for a Nonlinear Water Tanks Process
AbstractThe 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.
|Publication size in sheets||0.55|
|Book||Saeed Khalid, Rituparna Chaki, Janev Valentina (eds.): Computer Information Systems and Industrial Management : 18th International Conference : CISIM 2019 : proceedings, Lecture Notes In Computer Science, no. 11703, 2019, Springer, ISBN 978-3-030-28956-0, 540 p., DOI:10.1007/978-3-030-28957-7|
|Keywords in English||Optimal control Kalman filtering Model predictive control Parameter identification|
|Internal identifier||ROC 19-20|
|Score||= 40.0, 29-03-2020, ChapterFromConference|
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