# Gregory FrancoisUniversity of Applied Sciences and Arts Western Switzerland · Faculty of Engineering and Architecture

Gregory Francois

PhD; HDR (D. Sc)

## About

84

Publications

23,798

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1,057

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Introduction

Additional affiliations

November 2013 - present

Education

December 2014 - December 2014

October 1999 - December 2004

September 1995 - October 1998

## Publications

Publications (84)

In this paper, the problem of predicting blood glucose concentrations (BG) for the treatment of patients with type 1 diabetes, is addressed. Predicting BG is of very high importance as most treatments, which consist in exogenous insulin injections, rely on the availability of BG predictions. Many models that can be used for predicting BG are availa...

Over the past decade, a large number of academics and start-ups have devoted themselves to developing kites as a renewable energy source. Determining the trajectories the kite should follow is a modeling and optimization challenge. We present a dynamic model and analyse how uncertainty affects the resulting optimization problem. We show how measure...

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...

We propose a canonical form of the experimental optimization problem and
review the state-of-the-art methods to solve it. As guarantees of global
convergence to an optimal point via only feasible iterates are absent in these
methods, we present a set of sufficient conditions to enforce this behavior.

The experimental validation of a real-time optimization (RTO) strategy for the optimal operation of a solid oxide fuel cell (SOFC) stack is reported in this paper. Unlike many existing studies, the RTO approach presented here utilizes the constraint-adaptation methodology, which assumes that the optimal operating point lies on a set of constraints...

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to this repetition, there are two independent “time” variables, namely, the run time within a batch and the batch index. Accordingly, the optimization objective can be defined for a given batch or over several batches. This chapter formulates the d...

Typical model-based optimization approaches cannot handle plant-model mismatch, therefore the use of real-time optimization (RTO) schemes which take advantage of measurements from the plant is required. Modifier adaptation (MA) uses the measurements to add a bias to the model which iteratively matches the model with the local gradient estimates of...

Real-time optimization (RTO) has the ability to boost the performance of a process whilst satisfying the constraints by using process measurements, driving the operating conditions towards optimality. Modifier adaptation (MA) is a methodology of RTO which can find the optimal operating point of a process even in the presence of plant-model mismatch...

Modifier adaptation (MA) and output modifier adaptation (MAy) are iterative model-based real-time optimization (RTO) algorithms that have the proven ability to drive plants to their optimal operating condition upon convergence despite disturbances and modeling uncertainty, provided the model at hand satisfies model adequacy conditions. But there is...

Output modifier adaptation (MAy) is an iterative model-based real-time optimization (RTO) method with the proven ability to reach, upon converge, the unknown plant optimal steady-state operating conditions despite structural and parametric plant-model mismatch and disturbances. However, as such, feasibility of the iterates cannot be guaranteed befo...

Modifier adaptation (MA) methods are iterative model-based real-time optimization (RTO) methods with the proven ability to reach, upon converge, the unknown optimal steady-state operating conditions of a plant despite plant-model mismatch and disturbances. So far, MA has been applied to small-scale but never -- to the best of the authors' knowledge...

Modifier adaptation (MA) is a real-time optimization (RTO) method with the built-in guarantee to reach the plant optimal operating conditions upon convergence despite disturbances and modeling uncertainties. MA requires a model that (i) is adequate, i.e., the reduced Hessian of the Lagrangian is positive definite at the plant optimum, and (ii) with...

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter desc...

Modifier adaptation (MA) is an iterative real-time optimization (RTO) method characterized by its ability to enforce plant optimality upon convergence despite the presence of model uncertainty The approach is based on correcting the available model using gradient estimates computed at each iteration. MA uses steady-state measurements and solves a s...

This article presents a kite control and optimization problem intended as a benchmark problem for advanced control and optimization. We provide an entry point to this exciting renewable energy system for researchers in control and optimization methods looking for a realistic test bench, and/or a useful application case for their theory. The benchma...

The contribution of this article is to propose and experimentally validate an optimizing control strategy for power kites flying crosswind. The algorithm ensures the kite follows a reference path (control) and also periodically optimizes the reference path (efficiency optimization). The path-following part of the controller is capable of consistent...

This paper presents an overview of the recent developments of modifier-adaptation
schemes for real-time optimization of uncertain processes. These schemes have the ability to
reach plant optimality upon convergence despite the presence of structural plant-model mismatch.
Modifier Adaptation has its origins in the technique of Integrated System Opti...

Model-based optimization methods suffer from the limited accuracy of the available process models. Because of plant-model mismatch, model-based optimal inputs may be suboptimal or, worse, unfeasible for the plant. Modifier adaptation (MA) overcomes this obstacle by incorporating measurements in the optimization framework. However, the standard MA f...

Model-based optimization methods suffer from the limited accuracy of the available process models. Because of plant-model mismatch, model-based optimal inputs may be suboptimal or, worse, unfeasible for the plant. Modifier adaptation (MA) overcomes this obstacle by incorporating measurements in the optimization framework. However, the standard MA f...

Given a certain response surface function, its Lipschitz constants may be defined as the limits on its largest derivatives (sensitivities), and as such provide important information about the function's behavior. In this paper, it is argued that the explicit use of these constants may prove useful in the domain of experimental optimization. Most no...

A limitation of model-based optimisation methods lies in the potential inaccuracy of the available models. Because of plant-model mismatch, model-based optimal inputs may be suboptimal or, worse, unfeasible for the plant. Modifier adaptation (MA) overcomes this obstacle by incorporating measurements in the optimization framework. However, standard...

The steady advances of computational methods make model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate. The traditional remedy is to update the model parameters, but this generally leads to a difficult parameter estimation problem that must be solved on-line. In...

This paper analyzes the maximum power that a kite, or system of kites, can extract from the wind. First, a number of existing results on kite system efficiency are reviewed. The results that are generally applicable require significant simplifying assumptions, usually neglecting the effects of inertia and gravity. On the other hand, the more precis...

This paper applies a novel two-layer optimizing control scheme to a kite-control benchmark problem. The upper layer is a recent real-time optimization algorithm, called Directional Modifier Adaptation, which represents a variation of the popular Modifier Adaptation algorithm. The lower layer consists of a path-following controller that can follow a...

In practice, the quest for the optimal operation of energy systems is complicated by the presence of operating constraints, which includes the need to produce the power required by the user, and by the need to account for uncertainty. The latter concept incorporates the potential inaccuracies of the models at hand but also degradation effects or un...

In practice, the quest for the optimal operation of energy systems is complicated by the simultaneous presence of operating constraints, among which the need for producing the power required by the user, and of uncertainty. The latter concept incorporates the potential inaccuracies of the models at hand but also degradation effects or unexpected ch...

The material presented in this document is intended as a comprehensive,
implementation-oriented supplement to the experimental optimization framework
presented in a companion document. The issues of physical degradation, unknown
Lipschitz constants, measurement/estimation noise, gradient estimation,
sufficient excitation, and the handling of soft c...

Lower and upper bounds for a given function are important in many
mathematical and engineering contexts, where they often serve as a base for
both analysis and application. In this short paper, we derive piecewise linear
and quadratic bounds that are stated in terms of the Lipschitz constants of the
function and the Lipschitz constants of its parti...

The problem of steering a dynamical system toward optimal steady-state performance is considered. For this purpose, a static optimization problem can be formulated and solved. However, because of uncertainty, the optimal steady-state inputs can rarely be applied directly in an open-loop manner. Instead, plant measurements are typically used to help...

Blood glucose concentrations of patients with type 1 diabetes mellitus are subject to very high inter- and intra-patient variability. This variability may be detrimental to the reliability of the treatment, thus resulting in potentially frequent hypo- or hyperglycemia. Model-based therapies have the potential to improve the quality of the treatment...

The steady advance of computational methods makes model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate, which results in significant plant-model mismatch. An iterative optimization method called "modifier adaptation" overcomes this obstacle by incorporating plan...

This paper’s main contribution is a theoretical result that can be used to evaluate the maximum powergenerating potential of any kite-power system. An upper bound is derived for the power a wing can generate in a given wind speed. It is proven that the angle of the restraining forces on the system modulates this upper bound. In order to derive prac...

The present article looks at the problem of iterative controller tuning, where the parameters of a generalized controller are adapted in an iterative manner to bring the user-defi�ned performance metric to a local minimum for some repetitive process. Speci�cally, we cast the controller tuning problem as a real-time optimization (RTO) problem, which...

Model-based optimization is an increasingly popular way of determining the values of the degrees of freedom in a process. The difficulty is that the available model is often inaccurate. Iterative set-point optimization, also known as modifier adaptation, overcomes this obstacle by incorporating process measurements into the optimization framework....

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...

Real-time optimization (RTO) methods use measurements to offset the effect of uncertainty and drive the plant to optimality. Explicit RTO schemes, which are characterized by solving a static optimization problem repeatedly, typically require multiple iterations to steady state. In contrast, implicit RTO methods, which do not solve an optimization p...

We investigate the general iterative controller tuning (ICT) problem, where the task is to find a set of controller parameters that optimize some user-defined performance metric when the same control task is to be carried out repeatedly. Following a repeatability assumption on the system, we show that the ICT problem may be formulated as a real-tim...

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...

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...

This chapter presents recent developments in the field of process optimization. In the presence of uncertainty in the form of plant-model mismatch and process disturbances, the standard model-based optimization techniques might not achieve optimality for the real process or, worse, they might violate some of the process constraints. To avoid constr...

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...

A new approach for gradient estimation in the context of real-time optimization under uncertainty is proposed in this paper. While this estimation problem is often a difficult one, it is shown that it can be simplified significantly if an assumption on the local quasiconvexity of the process is made and the resulting constraints on the gradient are...

A gradient-descentmethod for the run-to-run tuning ofMPC controllers is proposed. It is
shown that, with an assumption on process repeatability, the MPC tuning parameters may
be brought to a locally optimal set. SISO and MIMO examples illustrate the characteristics
of the proposed approach.

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...

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, reso...

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...

Dynamic optimization can be used to determine optimal input profiles for dynamic processes. Due to plant-model mismatch and disturbances, the optimal inputs determined through model-based optimization will, in general, not be optimal for the plant. Modifier adaptation is a methodology that uses measurements to achieve optimality in the presence of...

Dynamic optimization can be used to determine optimal input profiles for dynamic processes. Due to plant-model mismatch and disturbances, the optimal inputs determined through model-based optimization will, in general, not be optimal for the plant. Modifier adaptation is a methodology that uses measurements to achieve optimality in the presence of...

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. From a control point of view, 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 spe...

The optimal operation of a solid oxide fuel cell stack is addressed in this paper. Real-time optimization, performed at a slow time scale via constraint adaptation, is used to account for uncertainty and degradation eﬀects, while model-predictive control is performed at a faster time scale to reject process disturbances and to safely adapt the syst...

For patients with type 1 diabetes mellitus, appropriate control of blood glucose concentrations is vital. Exercise is one of the disturbances that can affect these concentrations. Therefore, predictions in the presence of exercise are useful among others for model-based control methods, bolus calculators and educational tools. Although several mode...

Various real-time optimization techniques proceed by controlling the gradient to zero. These methods primarily differ in the way the gradient is estimated. This paper compares various gradient estimation methods. It is argued that methods with model-based gradient estimation converge faster but can be inaccurate in the presence of plant-model misma...

Run-to-run control is a technique that exploits the repetitive nature of processes to iteratively adjust the inputs and drive the run-end outputs to their reference values. It can be used to control both static and finite-time dynamic systems. Although the run-end outputs of dynamic systems result from the integration of process dynamics during the...

The optimal operation of a solid oxide fuel cell stack is addressed in this paper. Real-time optimization, performed at a slow time scale via constraint adaptation, is used to account for uncertainty and degradation effects, while model-predictive control is performed at a faster time scale to reject process disturbances and to safely adapt the sys...

With an annual worldwide production well in excess of 100 million metric tons, synthetic polymers constitute a significant part of the modern chemical process industry. Polymer reactors - operated in continuous, batch, or semibatch mode - are therefore important processing units, but there are unique problems associated with controlling them effect...

The experimental validation of a real-time optimization (RTO) strategy for the optimal operation of a solid oxide fuel cell (SOFC) stack is reported in this paper. Unlike many existing studies, the RTO approach presented here utilizes the constraint-adaptation methodology, which assumes that the optimal operating point lies on a set of constraints...

Solid Oxide Fuel Cells (SOFC) are energy conversion devices that produce electrical energy by the reaction of a fuel with an oxidant. Although SOFC have become credible alternatives to non-renewable energy sources, efforts are still needed to extend their applicability to a broader scope of applications such as domestic appliances. SOFC are typical...

La consommation des ressources énergétiques est actuellement un enjeu primordial, notamment dans le secteur du bâtiment, qui représente à lui seul 42 % de l'énergie consommée en France. La mixité énergétique, renouvelable et fossile, ainsi qu'une meilleure gestion, doivent se généraliser. Les préoccupations principales sont de réaliser des économie...

La consommation des ressources energetiques est actuellement un enjeu primordial, notamment dans le secteur du batiment, qui represente à lui seul 42 % de l’energie consommee en France. La mixite energetique, renouvelable et fossile, ainsi qu’une meilleure gestion, doivent se generaliser. Les preoccupations principales sont de realiser des economie...

The improvement of the energetic behavior of buildings has turned into a major issue due to the high level of energy consumption. One of the main objectives of the current French legislation, thanks to the definition of a global performance indicator (expressed in kWh/m2/yr), is to reduce the total energy consumption in buildings. In this context,...

In this article, ways for improving the energetic performance of buildings are investigated. A state of the art leads to the introduction of a performance indicator expressed in kWh/m2/yr. To improve the value of this indicator, a processor-based prototype of a real-time data-acquisition and monitoring system is developed in collaboration with two...

The improvement of the energetic behavior of buildings has turned into a major issue due to the high level of energy consumption. One of the main objectives of the current French legislation, thanks to the definition of a global performance indicator (expressed in kWh/m2/yr), is to reduce the total energy consumption in buildings. In this context,...

Measurements can be used in an optimization framework to compensate the effects of uncertainty in the form of model mismatch or process disturbances. 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 se...

Run-to-run control consists of using the measurements from previous runs to drive the outputs of the current run towards desired set points. From a run-to-run perspective, a dynamic system can be viewed as a static input-output map. For systems where this static map corresponds to a sector nonlinearity, a globally convergent fixed-gain run-to-run c...

In batch chemical processing, dynamic optimization is the method of choice to reduce production costs while satisfying safety constraints and product specifications. Most of the standard optimization techniques are based on a model of the process, while it is extremely difficult to get a reliable dynamic model for industrial batch processes, due to...

The aim of this paper is to present an approach to dynamic off-line optimization of batch emulsion polymerization reactors using a stochastic optimizer. The control objective is to find the optimal temperature profile that minimizes the final batch time constrained by the final conversion and molecular weight. In this study, we evaluate the applica...

The problem of minimizing the batch time of the copolymerization of acrylamide and quaternary ammonium cationic monomers at the industrial level is considered. An adjustable model of the optimal solution is used in the optimization. The model consists of two arcs: The first is isothermal and limited by heat removal constraint, whereas the second is...