Admission requirements.
To give students insight and knowledge necessary for practical system identification of static and dynamic models from recorded input-output data, and the use of such models for estimation and prediction of state variables and measurements. Further to give basic knowledge of optimal estimation of variables in discrete and continuous time dynamic systems, based on dynamic models and theory for stochastic systems.
Lectures, exercises with the use of PC software, laboratory task.
Description of stochastic processes. Discrete time process and measurement noise. Observability and detectability. Kalman filter for state and parameter estimation in linear and nonlinear systems. Kalman filter on prediction and innovation form. Deterministic and stochastic models, combined deterministic-stochastic models. Dynamic and static models. Modeling for control, prediction, simulation, operator support, diagnosis, failure detection, etc. Prefiltering of data, identification of trends etc. Experiment design for dynamic systems and persistent excitation. Least-squares system identification with statistical analysis. Singular value decomposition (SVD), principal component analysis and regression (PCA, PCR). Partial least squares (PLS). Identification of stochastic and dynamic systems from known input-output data. Details in subspace system identification methods such as DSR, and details in prediction error methods. Recursive system identification methods. Utilization of a priori knowledge. Handling of known nonlinearities and use of linearizing functions. Introduction to statistical process control (SPC) and data reconciliation.
An intermediate test counts 30% and a final test counts 70% in the final grade.
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