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Today the combination of several microscopy techniques over a sample can be performed at the same time on the same region. To fully capitalize on their complementarities and reveal new properties or behaviors as well as to present them in a new and attractive way, enhancing the experimental data exploration is a key issue. Especially, science education at micro and nanoscale is often limited by the complexity of making complicated knowledge accessible and easily remembered by the larger public. We propose an “out of the box” way to introduce complex scientific subjects like cell biology and polymer micro-rheology using haptic display and virtual reality. To reach this ambitious goal, firstly we combined the advantages of the Atomic Force Microscopy (AFM) and Fluorescence Microscopy (FM) in order to get complementary real experimental data on fixed and living isolated animal cells adhering on a protein micro-pattern or on a collagen-coated soft hydrogel. Secondly, thanks to the recent and fast AFM modes (PeakForce and Quantitative Imaging) dedicated to nanomechanical investigations, we achieved high resolution mapping images of the cell morphology, architecture and local mechanical properties. Then this set of data was implemented in a free simulation engine connected to a low-cost haptic device to create a virtual and interactive cell or polymer environment. In this environment, the user can explore the stiffness across three soft samples and can relate it to specific sample components. This dedicated visuo-haptic environment provides a novel sensory approach of the cell biology and/or micro(bio)mechanics through an active exploration of a set of scientific experimental data. This work paves the way to an affordable interactive and multisensory VR platform where various AFM images recorded in Peak Force or Quantitative Imaging, could be load in order to be actively explored; this approach fits into the emerging field of touching data.

The determination of product features, which can be seen as design specifications, is a crucial problem that must be carried out upstream to quickly validate the product configuration according to some attributes in relation to the user perception. To this end, the design methods must evolve toward an analysis compatible with various kind of data that can be qualitative or quantitative. In this paper, a new approach is introduced able to take into account various kind of information in order to determine some quantitative design specifications in accordance with the users perception. This is done through a mathematical formulation that exploit different types of data coming from sensory analysis and physical quantities. This mathematical formulation is then used in an optimization procedure that takes into account a preference order over the sensory attributes. The solution of this optimization problem gives thus the best user‐centered specifications that must be used for the conception of the final product.

Currently, more and more organizations are aware of the increasing potential of nanoscience and nanotechnology, due to the wide field of innovative applications they address from biomedical and molecular technology to semiconductor and material sciences. Nanotechnology is an area where fundamental or applicative research is still in progress and where many industrial applications are waited to come. Despite the progress of this field, many institutions, notably educational ones, cannot afford investing in these technologies allowing an access to nanoscale, and a lack of knowledge therefore remains. The Nanœye project focuses on the widespread of Atomic Force Microscopy or AFM technology, which is the basic access tool to the nanoscale. The main goal is to design an optimal AFM model in terms of manufacturing costs by using rapid prototyping techniques and open-source hardware, and designing it as an educational-oriented equipment. This approach is two-fold: as an AFM-unexperienced user discovering its principle of operation at nanoscale or as a future engineer understanding its multi-disciplinary structure involving mechanics, optics, electronics, software and control sciences. This paper introduces the first functional structure of Nanœye AFM project, focusing on its mechanical structure. It is submitted to the IFAC2017 special feature Automatic Control Demonstrators.

Real time pose estimation of a mobile rigid-body in an unknown environment and without adding constraints (markers, antenna, ultrasound, Radio Frequency IDentification (RFID)...) is a crucial issue for Augmented Reality (AR) applications. One of the most advanced indoor/outdoor pose estimator is the Simultaneous Localization and Mapping algorithm (SLAM) based on monocular or binocular images. The complexity of this algorithm and his processing time present the main drawbacks of this type of pose estimator. It is difficult to use SLAM on mobile or embedded devices in real time applications, because they suffer from low computational resources. On the other hand Inertial Measurement Units (IMU) allow indoor/outdoor localization without important time processing but require signal integration which generates drifts over longer periods of time. In this paper we propose a new method for coupling SLAM with IMU. In this approach we take into account the SLAM processing time in order to avoid incoherences of timestamps in fused SLAM and IMU poses. To this end we propose a retro-correction method that synchronizes all poses with a general clock timestamping all events. In addition the quality of the poses is improved with detection and treatment of inconsistency. For this purpose an Adaptive Complementary Filter (ACF) was developed. Finally simulated and experimental results validate the efficiency of the proposed method.

International audience

A critically important quantity of energy is required nowadays to heat and ventilate our buildings. In order to reduce this energy demand, we need to characterize the convective indoor air movements in large scale spaces like building rooms. 3D Lagrangian Particle Tracking is used here; hence a feedback schema has been designed, it applies multi-view information to correct uncertainties in particle positioning. Moreover, a multi-scale based approach has been developed and tested with experimental and synthetic datasets. The method shows good robustness and efficiency against different kinds of noise and allows a generally applicable algorithm thanks to the use of automatic scale selection. On the other hand, the particle tracking is another challenging problem. Therefore, a new tracking algorithm based on fuzzy Kalman filtering is proposed. The Kalman filter is used to optimally estimate the new position of the particles based on their actual position. A quantitative comparison of the performances of this technique and other commonly used tracking algorithms allows validating the method.

This paper introduces a new control scheme which incorporates the concept of shaping filter together with the use of the ν-gap metric and the robust design of a structured controller. The main motivation in doing this is related to the development of efficient control laws for small size actuators. Designing a structured controller is known to be computationally intractable via the traditional H ∞ method. This is mainly due to the non-convexity of the resulting control problem which is of fixed order or structure type. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations and without using too many “user defined” parameters, we utilize the heuristic Kalman algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. The experimental results validate the proposed technique and demonstrate its convenience for the development of fast and precise positioning systems.

This paper aims at solving difficult optimization problems arising in many engineering areas. To this end, two recently developed optimization method will be introduced: the heuristic Kalman algorithms (HKA) and the quasi geometric programming (QGP) problems. The principle of HKA is to consider the optimization problem as a measurement process intended to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, is developed to improve the quality of the estimate obtained through a measurement process. A significant advantage of HKA against other stochastic methods lies mainly in the small number of parameters which have to be set by the user. In this paper we also introduce an extension of standard geometric programming (GP) problems which we call quasi geometric programming (QGP) problems. The consideration of this particular kind of nonlinear and possibly non smooth optimization problem is motivated by the fact that many engineering problems can be formulated as a QGP. To solve this kind of problems (QGP), an algorithm is proposed which is based on the resolution of a succession of standard GP. An interesting feature of the proposed approach is that it does not need to develop specific program solver and works well with any existing solver able to solve conventional GP. In the last part of the paper, it is to shown that HKA and QGP can be efficiently used to solve difficult non-convex optimization problems. In particular, we have addressed the problem of robust structured control and on-ship spiral inductor design. Numerical experiments exemplify the resolution of this kind of problems.

International audience; he direct transplantation or imitation of lean production has led to difficulties of applying a number of lean principles and practices. Thus, diffusion of one of the main lean principles, just-in-time production, which refers to producing only 'what is really needed, when it is needed, and in the amount needed', seems to be limited. To date, some of the companies produce more than their customers really need. This method of production enables them not only to amortise the high-changeover costs over a large number of products, but also to benefit from commercial opportunities. However, these companies are exposed to financial losses related to storage costs and risks of non-sale. To provide decision elements for determining the best production strategy, we have developed a model for calculating the optimal quantity to be produced. Moreover, we suggest using a fuzzy aggregation system to optimise the consideration of the risk of non-sale. This new approach defines the limits not to exceed by taking into consideration the drawbacks linked to the risks of non-sale.

We present in this paper an method for evaluating the reliability in real time applied to the optimization of preventive maintenance and evaluation of the parameters of dependability. This approach is based on a function Z (t), which assesses the damage from the history of real operating conditions. This assessment is used to calculate the residual reliability, and can then be used to optimize the preventive maintenance and in particular optimize the time between overhaul (TBO). This approach can be used to take more realistic decisions about preventive change and thus led to a better risk management.

The study of the mechanical properties of human skin is a key point to better understand surgery, skin ageing and pathologies. As the skin is a living tissue, it must be studied in vivo, hence analytical solutions are really difficult to obtain. In this study, a new stochastic inverse method for the identification of its mechanical properties is proposed. The developed optimization method is first presented. It is based on an iterative stochastic approach which ensures the identification of a global extremum. The suction actual case study is then analysed through comparisons between experimental data and finite element models of this test. Only the elastic components of the skin are considered here. The solutions for the recursive least squares and Gauss-Newton's problems are finally compared with the proposed approach to conclude this study and to briefly present our future works.

Due to recent progresses in ergonomics, industrial design, neuroscience, marketing, etc. the classical 'techno-centred design method' is now rapidely evolving to the so-called 'human-centred design method', that takes into account the human being sensitivity. This paper introduces a new 'sensory design process', in which human factors such as perception and emotions are quantified in order to match technical specifications as well as human being satisfaction. In this process, three steps are clearly identified. The 'product space' is first constructed via attributes affected to the designed product. Then a quantitative analysis of preference and emotional evaluations is performed in the product space. Finally, an aggregation operator is presented, which leads to a classification of the elements of the product space, and thus gives indications to design the product. A working example illustrates the use of this method for the design of a kitchen glove.

This paper presents a simple but effective synthesis strategy for observers based faults detection in linear time-invariant (LTI) systems which are simultaneously affected by two classes of unknown inputs: Noises having fixed spectral densities and unknown finite energy disturbances. The problem of designing such an observer, also called a residual generator, is formulated as a mixed H 2 / H ∞ optimization problem. This is done to obtain an optimal residual generator, i.e. with minimal sensitivity to unknown inputs. Unfortunately, there is no known solution to this difficult optimization problem. Finding such a residual generator is known to be computationally intractable via the conventional techniques. This is mainly due to the non-convexity of the resulting optimization problem. To solve this kind of problem easily and directly, without using any complicated mathematical manipulations, we utilize the Heuristic Kalman Algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. A numerical example is given to illustrate the advantage of the mixed H 2 / H ∞ optimization approach against techniques based on optimization of H 2 or H ∞ criteria.

In a world in continuous evolution, the different industrial actors need to be reactive to remain competitive and to conquer new market trends. To achieve this, they are constrained to improve their way of industrial management, both at the strategic level, to adapt to technological advances and follow market trends. In this chapter, we introduce a new simulation method that makes it easy to understand the results of a given simulation. This is of crucial importance because the design stage of a manufacturing system usually implies not specialist actors. The objective of the chapter is to present the main advantages of using the virtual reality (VR) to the manufacturing processes simulation. To this end, a state of the art will compose the first part of the chapter. In the second part, we address the issue of the contribution of the VR to the industrial simulation.

ABSRACT. The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization which allows the determination of the rules base and the parameters of the membership functions. We also present for this classifier an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system is not, in general, directly available. It must be extracted from an often considerable mass of information. For this reason, a general methodology for the design of a fuzzy diagnosis system is presented and illustrated on a non-linear plant.

The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant.

Wear of materials is a very complex and partially-formalized phenomenon involving numerous parameters and damage mechanisms. There is a need to characterize the wear in many industrial applications and it has encouraged the current research. In this work, we consider an original strategy which consists in investigating the effect of contact conditions on wear behavior of carburized stainless steels under fretting and reciprocating sliding motion. A physical model is proposed to understand the phenomenon. Then, these pre-treated experimental data are incorporated in a neural network for wear volume modeling. Three models are proposed and compared together, in function of their inputs.

This chapter introduces how a control design problem can be formulated as an optimization problem. To this end, the standard control problem as well as the notion of stabilizing controllers are first briefly reviewed. After that, some closed-loop system performance measures are presented, they are indeed unavoidable to evaluate the quality of a given controller. These performances measures are then used to formulate the optimal controller design problem which is multi-objective in nature. The resulting multi-objective problem is scalarized using the notion of ideal point. The last part of the chapter focuses on the case of structured controllers, i.e., structural constraints have to be taken into account in the optimization problem. These structural constraints make the resulting optimization problem non-smooth and non-convex, which result into intractability. This is why the use of stochastic optimization methods is suggested for finding an acceptable solution. The robustness issue is also briefly discussed. It is pointed out that the optimal robust control problem in addition to being non-smooth and non-convex is also semi-infinite.

In this chapter a new optimization method is presented, called the Heuristic Kalman Algorithm (HKA). This new algorithm is proposed as an alternative approach for solving continuous non-convex optimization problems. The principle of HKA is to consider explicitly the optimization problem as a measurement process intended to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. A significant advantage of HKA against other metaheuristics lies mainly in the small number of parameters which have to be set by the user. In addition, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA is evaluated in detail through several non-convex test problems, both in the unconstrained and constrained cases. The results are then compared to those obtained via other metaheuristics. These various numerical experiments show that the HKA has very interesting potentialities for solving non-convex optimization problems, especially with regard to the computation time and the success ratio.

In Chap. 7, we consider the design of fixed structure controllers for uncertain systems in the H ∞ framework. Although the presented design procedures apply for any kind of structured controller, we focus mainly on the most widely used of them which is the PID. Two design approaches will be considered: the mixed sensitivity method and the H ∞ loop-shaping design procedure. Using these methods, the resulting PID design problem is formulated as an inherently non-convex optimization problem. The resulting tuning method is applicable both to stable and unstable systems, without any limitation concerning the order of the process to be controlled. Various design examples are presented to give practical insights into the methods presented.

Many control objectives can be stated in terms of the size of some particular signals. Therefore, a quantitative treatment of the performance of control systems requires the introduction of appropriate norms, which give measurements of the sizes of the signals considered. Another concept closely related to the size of a signal, is the size of a LTI system. The latter concept is of great practical importance because it is the basis of technical control \(\mathbf{H}_{\infty}\) and the study of robustness (see Chap. 6). These different concepts are detailed in this chapter.

In Chap. 9, we consider the design of a nonlinear structured controller for systems that can be well described by uncertain multi-models. In a first part, the concept of multi-model is introduced and some examples are given to show how this works. After that, the problem of designing a nonlinear structured controller for a given uncertain multi-model is considered. A characterization of the set of quadratically stabilizing controllers is first introduced. This result is then used to design a nonlinear structured controller that quadratically stabilizes the uncertain multi-model, while satisfying a given performance objective. Some design examples are presented to illustrate the main points introduced in this chapter.

The ability of dealing with uncertainty (uncertain system) is an essential part when designing a robust feedback controller. The objective is indeed to determine the controller parameters ensuring acceptable performance of the closed-loop system despite the unknown disturbances affecting the system as well as the uncertainties about the plant dynamics. To this end, it is necessary to be able to take into account the model uncertainties during the design phase of the controller. In this chapter, we briefly describe some basic concepts regarding uncertain systems and robustness analysis. The last part of this chapter is devoted to structured robust control, for which a specific stochastic algorithm is developed.

In Chap. 8, we consider the design of structured controller for uncertain parametric systems in the H 2 and mixed H 2/H ∞ framework. We restrict ourselves to the case of static output feedback (SOF) controllers; this is not restrictive because as seen in Chap. 3, any dynamical controller can be reformulated as SOF for an augmented plant. Some design examples are presented to illustrate the proposed design methods.

This chapter introduces some methods aimed at solving difficult optimization problems arising in many engineering fields. By difficult optimization problems, we mean those that are not convex. Recall that for the class of non-convex problems, there is no algorithm capable of guaranteeing, in a reasonable amount of time (by reasonable time, we mean polynomial in the size of the problem), that the solution found is the global optimum. Under these conditions, we must be content with finding an acceptable solution. After introducing the notion of acceptable solution, a brief overview of the main stochastic methods which can be used for solving continuous non-convex constrained optimization problems is presented i.e.: Pure Random Search Methods (PRSM), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). These methods are designed to produce an acceptable solution. The last part is dedicated to the problem of robust optimization, i.e., optimization in the presence of uncertainties on the problem data.

Today, to cope with the complexity of the global organisation, the industrial companies need to be more structured. New processes have to be developed to meet the more and more ambitious quality requirements. A new problematic arises: what is needed to offer all the customers a product that meets the quality requirements of a local market? The main objective of this paper is to propose a reliability allocation method that matches the market specifications and customer satisfaction. This is in contrast with the traditional allocation methods which are often time-consuming to implement or do not focus on customer satisfaction for the definition of the reliability targets. The method we propose here is inspired from the reliability allocation method and is formulated as a feasibility problem. In this context, the notion of optimality of the solution is not being sought; the objective is 'only' to find out a solution that satisfies the global target reliability. This allows determining some local reliability targets in accordance with industrial data.

This chapter aims at solving difficult optimization problems arising in many engineering areas. To this end, a brief review of the main stochastic methods which can be used for solving continuous non-convex constrained optimization problems is presented i.e.: Simulated annealing (SA), Genetic algorithm (GA), and Particle swarm optimization (PSO). In addition to that, we will present a recently developed optimization method called Heuristic Kalman Algorithm (HKA) which seems to be, in some cases, an interesting alternative to the conventional approaches. The performance of these methods depends dramatically on the feasible search domain used to find out a solution as well as the initialization of the various user defined parameters. From this point of view, some practical indications concerning these issues will be given. Another objective of this chapter is to show that the stochastic methods, notably HKA, can be efficiently used to solve robust synthesis problems in the area of structured control and fault diagnosis systems. More precisely, we will deal with the following problems: the synthesis of a robust controller with a given fixed structure and the design of a robust residual generator. Some numerical experiments exemplify the resolution of this kind of problems.