{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T18:40:58Z","timestamp":1789929658881,"version":"4.0.1"},"reference-count":29,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"4","funder":[{"DOI":"10.13039\/501100003801","name":"Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences, University of Heidelberg","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003801","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM J. Sci. Comput."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>Dual control refers to strategies that strike a balance between control and estimation. Combined with nonlinear model predictive control, dual control offers advanced feedback methods for optimal control problems under uncertainties. We present dual control from a new perspective, namely, the interplay between the performance control task and the information gain task in connection with optimal experimental design. A new approach to dual control is proposed in which the covariance matrix of the estimates is weighted by the derivatives of the nominal objective value with respect to unknown parameters and initial states. This quantity can be interpreted as a predictive variance. Applying the idea of biobjective formulation, we use a weighted sum of the original objective function and the predictive variance as a modified objective function. Most notably, we discuss the theoretical background of this approach and provide probabilistic bounds for the controller performance with respect to the original objective function. As applications, we investigate a moon lander problem and a batch bioreactor problem. Numerical results demonstrate the superiority of dual control over nominal control. We also carry out an analysis of the relationship between the performance control task and the information gain task in order to assess when the extra effort for dual control is justified.<\/jats:p>","DOI":"10.1137\/16m1069936","type":"journal-article","created":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T13:22:35Z","timestamp":1499952155000},"page":"B640-B657","source":"Crossref","is-referenced-by-count":13,"title":["Dual Control and Online Optimal Experimental Design"],"prefix":"10.1137","volume":"39","author":[{"given":"H. C.","family":"La","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Potschka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. P.","family":"Schl\u00f6der","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. 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