Grégory François

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Process measurements can be used in an optimization framework to compensate the effects of run-time uncertainty. Among the various options for input adaption, a promising approach consists of directly enforcing the Necessary Conditions of Optimality (NCO) that include two parts: the active constraints and the sensitivities. In this paper, the variations of(More)
Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also,(More)
The idea of iterative process optimization based on collected output measurements, or “real-time optimization” (RTO), has gained much prominence in recent decades, with many RTO algorithms being proposed, researched, and developed. While the essential goal of these schemes is to drive the process to its true optimal conditions without violating any(More)
Real-Time Optimization (RTO) via modifier adaptation is a class of methods for which measurements are used to iteratively adapt the model via input-affine additive terms. The modifier terms correspond to the deviations between the measured and predicted constraints on the one hand, and the measured and predicted cost and constraint gradients on the other.(More)
The subject of real-time, steady-state optimization under significant uncertainty is addressed in this paper. Specifically, the use of constraint-adaptation schemes is reviewed, and it is shown that, in general, such schemes cannot guarantee process feasibility over the relevant input space during the iterative process. This issue is addressed via the(More)
Real-time optimization (RTO) methods use measurements to offset the effect of uncertainty and drive the plant to optimality. RTO schemes differ in the way measurements are incorporated in the optimization framework. Explicit RTO schemes solve a static optimization problem repeatedly, with each iteration requiring transient operation of the plant to steady(More)
Type 1 Patients with Diabetes (Type 1 PwDs) have to frequently adjust their insulin dosage to keep their Blood Glucose concentration (BG) within normal bounds. Meal intakes represent the most important disturbance that has to be accounted for. Its effect differs for every individual as well as for every meal. These specificities are automatically taken into(More)
The present article looks at the problem of iterative controller tuning, where the parameters of a given controller are adapted in an iterative manner to bring a user-defined performance metric to a local minimum for some repetitive process. Specifically, we cast the controller tuning problem as a real-time optimization (RTO) problem, which allows us to(More)
Obtaining a reliable gradient estimate for an unknown function when given only its discrete measurements is a common problem in many engineering disciplines. While there are many approaches to obtaining an estimate of a gradient, obtaining lower and upper bounds on this estimate is an issue that is often overlooked, as rigorous bounds that are not overly(More)
Real-time optimization (RTO) is a class of methods that use measurements to reject the effect of uncertainty on optimal performance. This article compares six implicit RTO schemes, that is, schemes that implement optimality not through numerical optimization but rather via the control of appropriate variables. For unconstrained processes, the ideal(More)