http://hdl.handle.net/2282/257 Search the Channel search http://teora.hit.no:8443/dspace/simple-search http://hdl.handle.net/2282/399 Title: Modified Smith-predictor multirate control utilizing secondary process measurements <br/> <br/>Authors: Ergon, Rolf <br/> <br/>Abstract: The Smith-predictor is a well-known control structure for industrial time delay systems, where the basic idea is to estimate the non-delayed process output by use of a process model, and to use this estimate in an inner feedback control loop combined with an outer feedback loop based on the delayed estimation error. The model used may be either mechanistic or identified from input-output data. The paper discusses improvements of the Smith-predictor for systems where also secondary process measurements without time delay are available as a basis for the primary output estimation. The estimator may then be identified also in the common case with primary outputs sampled at a lower rate than the secondary outputs. A simulation example demonstrates the feasibility and advantages of the suggested control structure. Sun, 29 Oct 2006 22:58:59 GMT http://hdl.handle.net/2282/398 Title: A didactically motivated PLS prediction algorithm <br/> <br/>Authors: Ergon, Rolf; Esbensen, Kim H. <br/> <br/>Abstract: The intention of this paper is to develop an easily understood PLS prediction algorithm, especially for the control community. The algorithm is based on an explicit latent variables model, and is otherwise a combination of the previously published Martens and Helland algorithms. A didactic connection to Kalman filtering theory is provided for a methodological overview. Sun, 29 Oct 2000 22:58:59 GMT http://hdl.handle.net/2282/397 Title: Noise handling capabilities of multivariate methods <br/> <br/>Authors: Ergon, Rolf <br/> <br/>Abstract: The noise handling capabilities of principal component regression (PCR) and partial least squares regression (PLSR) are somewhat disputed issues, especially regarding regressor noise. In an attempt to indicate an answer to the question, this article presents results from Monte Carlo simulations assuming a multivariate mixing problem with spectroscopic data. Comparisons with the best linear unbiased estimator (BLUE) based on Kalman filtering theory are included. The simulations indicate that both PCR and PLSR perform comparatively well even at a considerable regressor noise level. The results are also discussed in relation to estimation of pure spectra for the mixing constituents, i.e. to identification of the data generating system. In this respect solutions to well-posed least squares problems serve as references. Mon, 29 Oct 2001 22:58:59 GMT http://hdl.handle.net/2282/396 Title: Dynamic system multivariate calibration based on multirate sampling data <br/> <br/>Authors: Ergon, Rolf; Halstensen, Maths <br/> <br/>Abstract: The statistical principal component regression (PCR) and chemometric partial least squares regression (PLSR) algorithms based on latent variables (LV) modeling are effective tools for handling ill-conditioned regression data. In many process related cases the data form time series, and it may then be possible to improve the prediction/estimation results by utilizing the autocorrelation in the observations. This can be done by use of estimators found from experimental data by use of a combination of statistical/chemometric and system identification methods. In important industrial cases, the response variables are product qualities which also in the experimental data are sampled at a low and possibly irregular rate, while the regressor variables are sampled at a higher rate. After a discussion of the options available, the paper shows how the autocorrelation of the regressor variables in such multirate sampling cases may be utilized by identification of latent variables based output error (LV + OE) estimators. An example using acoustic power spectrum regressor data is finally presented. Sun, 29 Oct 2000 22:58:59 GMT