{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:23:55Z","timestamp":1751091835247},"reference-count":29,"publisher":"Walter de Gruyter GmbH","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Respiratory support is a key element of modern medical care, ranging from oxygen therapy to full ventilatory support. A central component of mechanical ventilation is the control of the resulting pneumatic quantities such as pressure and flow. In this article the use of robust model predictive control for pressure-controlled mechanical ventilation is proposed, with the goal of increasing the safety of the patient by considering physiological safety constraints. The uncertainty in the estimation of physiological model parameters as well as model uncertainties are considered as disturbances to the system, which are taken into account through the proposed robust model predictive control framework. The practical applicability of this control approach is illustrated in an implementation on a research demonstrator of the ventilation unit from an anaesthesia workstation.<\/jats:p>","DOI":"10.1515\/auto-2020-0087","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T18:07:16Z","timestamp":1604686036000},"page":"941-952","source":"Crossref","is-referenced-by-count":2,"title":["Robust model predictive control of an anaesthesia workstation ventilation unit"],"prefix":"10.1515","volume":"68","author":[{"given":"Georg","family":"M\u00e4nnel","sequence":"first","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , 9191 University of L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marlin","family":"Siebert","sequence":"additional","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , 9191 University of L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Brendle","sequence":"additional","affiliation":[{"name":"Dr\u00e4gerwerk AG & Co. KGaA , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Rostalski","sequence":"additional","affiliation":[{"name":"Institute for Electrical Engineering in Medicine , 9191 University of L\u00fcbeck , L\u00fcbeck , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,10,28]]},"reference":[{"key":"2023033109551442435_j_auto-2020-0087_ref_001_w2aab3b7d771b1b6b1ab2ab1Aa","unstructured":"A.\u2009B. Lumb, Nunn\u2019s Applied Respiratory Physiology. Edinburgh: Elsevier, 2016."},{"key":"2023033109551442435_j_auto-2020-0087_ref_002_w2aab3b7d771b1b6b1ab2ab2Aa","doi-asserted-by":"crossref","unstructured":"G. M\u00e4nnel, C. Hoffmann and P. Rostalski, \u201cA robust model predictive control approach to intelligent respiratory support,\u201d in 2018 IEEE Conference on Control Technology and Applications (CCTA). 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