{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:02:27Z","timestamp":1760238147373,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control\/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry.<\/jats:p>","DOI":"10.3390\/a13080178","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T10:14:06Z","timestamp":1595499246000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects"],"prefix":"10.3390","volume":"13","author":[{"given":"Sebastian","family":"Plamowski","sequence":"first","affiliation":[{"name":"Institute of Control and Computation Engineering, Warsaw University of Technology, Plac Politechniki 1, 00-661 Warszawa, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard W","family":"Kephart","sequence":"additional","affiliation":[{"name":"Emerson Process Management, 200 Beta Dr, Pittsburgh, PA 15238, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Camacho, E.F., and Bordons, C. 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