This regulator-observer permits to the output system to track a desired trajectory without using an observer dynamics then the problem of pole placement, which consists in imposing close[r]

with Cemagref in Montpellier and the International Water Management Institute in Sri Lanka. He joined Cemagref in 1989, where he contributed to the development of the SIC software and completed, in parallel, the Ph.D. with the LAAS (Laboratoire d’Automatique et d’Analyse des Systèmes) in Toulouse and the Engref Engineering School in 1994. He held a visiting researcher position in the **control** group of the Iowa State University in 1999-2000. He is the Transcan Research Group leader at Cemagref Montpellier from 1995, where his research focuses on modeling and **control** of open-channel hydraulic systems such as rivers and irrigation canals. Alexandre Bayen received the Engineering Degree in applied mathematics from the Ecole Polytechnique, France, in July 1998, the M.S.and Ph.D. degrees in aeronautics and astronautics from Stanford University in June 1999, and December 2003. He was a visiting researcher at NASA Ames Research Center from 2000 to 2003. Between January 2004 and December 2004, he worked as the Research Director of the Autonomous Navigation Laboratory at the Laboratoire de Recherches Balistiques et Aerodynamiques, (Ministere de la Defense, Vernon, France), where he holds the rank of Major. He has been an assistant professor in the Department of Civil and Environmental Engineering at UC Berkeley since January 2005. He is the recipient of the Ballhaus Award from Stanford University, 2004. His project Mobile Century received the 2008 Best of ITS Award for ‘Best Innovative Practice’, at the Intelligent Transportation Systems (ITS) World Congress in New Work. He is a recipient of a CAREER award from the National Science Foundation, 2009.

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Besides stabilizing the actuator around the set-point of op- erations, many MEMS-**based** applications impose stringent re- quirements on the transient behavior of actuator, such as set- tling time, overshoot and oscillation. Furthermore, the **control** schemes should also be robust vis-à-vis manufacturing toler- ance, operation points, modeling errors, parameter uncertain- ties, and environmental disturbances. It is indeed a very com- plex task to incorporate all the aforementioned factors into the design of **control** algorithms under the framework of linear con- trol theory, and compromising the optimality of the system is inevitable [17]. This motivates the application of nonlinear con- trol techniques to improve the overall performance for MEMS devices.

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5 Conclusion
In this paper, a **flatness**-**based** **control** for tracking desired trajectories in the case of MIMO LTV systems is proposed and developed. The proposed controller is **based** on an exact observer with a direct calculation of the state vector which contains the flat output and its derivatives. This regulator- observer permits to the system outputs to track desired tra- jectories without using observer dynamics. The proposed method leads to a **control** design which can be seen as a 2DOF controller but without the resolution of Bézout’s equation. The **control** law applied on a nonlinear model of a satellite gives a high level of performances in terms of the trajectory tracking. Beyond the framework of LTV sys- tems, the result presented here open the way to the **control** of nonlinear systems using their linearizations around given trajectories.

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1, 2 Ecole Centrale de Nantes, LS2N, Nantes, France 3 R´ eseau de Transport d’Electricit´ e (RTE), Paris, France
Abstract
In this research, we aim to use the **flatness** **control** theory to develop a useful **control** scheme for a single machine connected to an infinite bus (SMIB) system taking into account input magnitude and rate saturation constraints. We adopt a fourth-order nonlinear SMIB model along an ex- citer and a turbine governor as actuators. According to the **flatness**-**based** **control** strategy, first we show that the adopted nominal SMIB model is a flat system. Then, we develop a full linearizing state feedback as well as an outer integral-type loop to ensure suitable tracking performances for the power and voltage as well as the angular velocity outputs. We assume that only the angular velocity of the generator is available to be measured. So, we provide a linear Luenberger observer to estimate the remaining states of the system. Also, the saturation nonlinearities are transferred to the linear part of the system and they are canceled out using their estimations. The efficiency and usefulness of the proposed observer-controller against faults are illustrated using simulation tests in Eurostag and Matlab. The results show that the clearing critical time of the introduced methodology is larger than the classical **control** approaches and the proposed observer- **based** **flatness** controller exhibits over much less **control** energy compared to the classic IEEE controllers.

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6. Conclusion
This paper presented a solution to the problem of set-point **control** of temperature distribution with in-domain actuation described by an inhomogeneous parabolic PDE. To apply the principle of superposition, the system is presented in a parallel connection form. The dynamic **control** problem introduced by the ZDI design is solved by using the technique of flat systems motion planning. As the **control** with multiple in-domain actuators results in a MIMO problem, a Green’s function-**based** reference trajectory decomposition is intro- duced, which considerably simplifies the **control** design and implementation. Convergence and solvability analysis con- firms the validity of the **control** algorithm and the simulation results demonstrate the viability of the proposed approach. Finally, as both ZDI design and **flatness**-**based** **control** can be carried out in a systematic manner, we can expect that the approach developed in this work may be applicable to a broader class of distributed parameter systems.

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Among the reported **control** strategies, the **flatness**-**based** **control** (FBC) appears as an interesting nonlinear **control** approach due to its useful performances when explicit trajectory planning is required under different operating conditions [31]. Indeed, the **flatness** theory allows an accurate description of the transient and the steady state dynamic of the overall system state variables **based** on differentially flat outputs. In addition, and as reported in various works [32-36], the FBC demonstrates stable performances even under large operating point and system parameters variation. For instance, the use of FBC has been proposed for three phase three leg VSIs and its effectiveness has been highlighted through comparisons with both proportional integral and feedback linearization controllers [34]. Also, the **control** of parallel three-phase VSIs is proposed in [35], where the **flatness** theory is used with the aim to minimize the effect of circulating currents and reduce voltage distortion at the Point of Common Coupling (PCC). A literature review of the main properties and applications of the FBC in power systems is proposed in [36].

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gion. This Carleman approach proves very efficient also with semilinear parabolic equations [7]. By contrast the interest for the numerical investigation of the null con- trollability of the heat equation (or of parabolic equa- tions) is fairly recent: apart from [3], the first significant contributions are [32,31,24,2,23,1,5]; see also [8] for an application to some inverse problems. All the above re- sults rely on some observability inequalities for the ad- joint system. A direct approach which does not involve the adjoint problem was proposed in [11,15,14,16]. In [11] a fundamental solution for the heat equation with com- pact support in time was introduced and used to prove null controllability. The results in [11,27] can be used to derive **control** results on a bounded interval with two or one boundary **control** in some Gevrey class, or on a bounded domain of R N with a **control** supported on the

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† Alcatel-Lucent Bell Labs, Nozay, France
Email: ludovic.noirie@alcatel-lucent.com
Abstract—Many methods have been proposed in the literature to perform admission **control** in order to provide a sufficient level of Quality of Service (QoS) to accepted flows. In this paper, we introduce a novel data-driven method **based** on a time- varying model that we refer to as Knowledge-**Based** Admission **Control** solution (KBAC). Our KBAC solution consists of three main stages: (i) collect measurements on the on-going traffic over the communication link; (ii) maintain an up-to-date broad view of the link behavior, and feed it to a Knowledge Plane; (iii) model the observed link behavior by a mono-server queue whose parameters are set automatically and which predicts the expected QoS if a flow requesting admission were to be accepted. Our KBAC solution provides a probabilistic guarantee whose admission threshold is either expressed, as a bounded delay or as a bounded loss rate. We run extensive simulations to assess the behavior of our KBAC solution in the case of a delay threshold. The results show that our KBAC solution leads to a good trade-off between flow performance and resource utilization. This ability stems from the quick and automatic adjustment of its admission policy according to the actual variations on the traffic conditions. 1

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= 10 ms and
D ∗
= 20 ms. We evaluate the performance of our KBAC solution using ns-3 simulations. Each simulation is run for a period of 30 minutes. It is also worth noting that we compare our KBAC solution with two other solutions (i.e., Measured Sum and Aggregate Traffic Envelopes), and with an ideal admission **control**. Table 1 relates the parameter values selected for each solution. To properly assess the behavior of each admission **control**, we consider several metrics: (i) the “instantaneous" values of the packet delay computed on a sliding window of length equal to 4 s; (ii) the percentage of accepted flows; (iii) the percentage of violation that represents the ratio of time during which the QoS target is violated. These two latter values are computed over the entire duration of the simulation.

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To cite this document: Bordeneuve-Guibé, Joël and Bako, Laurent and Miksch, Roland and Jeanneau, Matthieu Flexible aircraft **control** **based** on an adaptive output feedback
**control** scheme. (2009) In: IFAC Workshop on **Control** of Distributed on Parameter
Systems, 20 July 2009 - 24 July 2009 (Toulouse, France). (Unpublished)

Data-assimilation method for RANS-driven mean flow reconstruction 3 In the present paper, contrary to these authors, we directly consider snapshots with velocity components (for example obtained with a PIV technique). Also, we do not aim at reconstructing a series of flow snapshots obtained at successive times but rather the time-average of these snapshots – the mean-flow –, and their second order statistics – the Reynolds stresses. For this, we will use as a regularization the full Reynolds-Averaged- Navier-Stokes (RANS) equations. It is important to note that this choice of regularization operator (or kernel) is not unique. However, we choose the RANS equations as we believe they describe the physics of the problem accurately. The computational cost should therefore remain reasonable, even in three-dimensional configurations, since only steady- state solutions of the RANS equations and adjoint solutions, which do not involve time, need to be evaluated numerically. The approach employed in this paper can be applied to any unsteady (not necessarily turbulent) flow. Such a flow can, in a first instance, be described by its first statistical moment, the mean-flow. Even if the original flow can only be fully understood using both the mean and the unsteady components, we can gain some information about the flow by replacing the full unsteady terms by the second-order momentum, i.e. the Reynolds stress tensor. The goal of the present study is to investigate the possibilities of state-vector reconstruction from sparse mean flow measurements. We presume that the mean (or time-averaged) flow satisfies the RANS equations. In this set of equations, the Reynolds stress tensor appears as an additional unknown, and its definition in terms of the mean quantities is known as the closure problem. However, in our case, this unknown is chosen as a design variable (sometimes referred to as the **control** parameter) in an optimization process and will be considered as an unknown forcing term in the standard, steady Navier-Stokes equations. We thus identify the full mean flow from sparse data measurements (taken from a direct numerical simulation), together with the corresponding optimal forcing, that ensures the averaged flow to be a solution of the RANS equations.

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APPROACH by
Philippe Martin, Lionel Rosier & Pierre Rouchon
Abstract. — We consider linear one-dimensional parabolic equations with space dependent coefficients that are only measurable and that may be degenerate or singular. We prove the null controllability with one boundary **control** by following the **flatness** approach, which provides explicitly the **control** and the associated trajectory as series. As an application, we consider the heat equation with a discontinuous coefficient in the principal part. The note ends with a numerical experiment which demonstrates the effectiveness of the method.

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Figure 3 shows the results averaged across subjects, runs and datasets. Values of 400 means the confidence threshold was not reached after 400 iterations. Our proposed method, **based** on the uncertainty on the task and the signals inter- pretation, allows to lead the system to regions that improve disambiguation among hypotheses in a faster way. Trying to follow the most probable task does not allow the system to explore sufficiently (Greedy), and at least some random exploration is necessary to allow a correct identification of the task (ε-greedy). Assessing uncertainty only on the task performs poorly as it does not take into account the signal interpretation ambiguity inherent to our problem. The large variability in the results is mainly due to the large variations in classification accuracy across subjects and datasets. Given

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2. Propagation process with 4 capacitive sensors and an electronic level.
The measurement principle is **based** on a propagation process. We displace a matrix of sensors along the profile to measure [GAO96]. To introduce measurement redundancy, we use a matrix with 4 capacitive sensors. The distance d between each sensor is 20 mm (see figure 2). For each position j on the profile, we measure the

1
some examples of rolling ball tracking and other objects tracking are available on the lagadic team website : http://www.irisa.fr/lagadic/demo.html
motion oscillates. This is directly due to the stepping motion. This lateral motion can be also found in the bottom right figure that is the output velocity of the pattern generator. The walking **control** guaranties this sway motion to be minimal. Furthermore, the upper body of the HRP2 robot can not compensate for this sway motion due to a lack of degrees of freedom. Anyway, the model tracker proved to be robust enough to track an object even when the camera oscillates under the sway motion. The top right figure first shows an increase of the error and then a visual servoing classical exponential decrease. The increase of the error is directly related to the variation of the pose estimation that can be observed in the top left figure. Both changes are due to the motion induced by the robot first steps. Usually, the robot needs two steps to reach the desired velocity and make the error decrease. The bottom left figure presents the visual **control** law that is the reference pattern generator velocity.

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Author biographies
Mourad Benoussaad is an associate professor at Ecole Nationale d’Ing´enieurs de Tarbes (ENIT; Na- tional Engineering School of Tarbes) since 2014. He re- ceived a Ph.D in Robotics and **Control** Theory from the University of Montpellier 2, at LIRMM-CNRS-INRIA, in 2009. He was a postdoc researcher at The Univer- sity of Tokyo (JSPS-Japan) in 2010-2011 and then a postdoc researcher at Heidelberg University (Germany) in 2012-2013. In 2013-2014, he joint LIRMM-CNRS- INRIA as a researcher in Robotics Lab. His research focus mainly on Physical Human-Robot Interaction (pHRI) in context of collaborative robotics (cobotics) and active exoskeleton. He also works on human motion and **control** of human musculoskeletal system.

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Keywords: access **control**, data secrecy, blockchain, smart contract, Ethereum
Abstract: Recent years have witnessed the trend of increasingly relying on remote and distributed infrastructures. This increased the number of reported incidents of security and privacy breaches, mainly due to the loss of data **control**. Towards these challenges, we propose a new access **control** scheme **based** on emerging blockchain infrastructures. Our approach relies on the use of smart auditable contracts deployed in blockchain infrastruc- tures. Thus, it offers transparent and controlled access to outsourced data, such that malicious entities cannot process data without data owners’ authorization. In fact, the effectiveness of the authentication relies on the blockchain intrinsic properties. Moreover, an implementation of the proposed solution **based** on Ethereum Blockchain is presented to show the applicability of our scheme in real-world scenarios.

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Definition 9. (Access Privilege) An Access Privilege (AP) is a set of allowed opera- tions on the protected resources of the form AP = {Create, Read,U pdate, Delete}.
We model the Access Privileges as four classes of operations in order to maintain a close relationship with CRUD-oriented access **control** systems. This relationship allows a finer-grained access **control** than simple read/write privileges as in WAC, and it suggests to the data providers how to specify the access privileges, following the example of CRUD-oriented systems, as we will discuss in relation to the user interface. The idea is that in the Social Semantic Web, there is a difference in allowing the users who ask to access my data to update my data or to delete my data. We distinguish the Update, Create and Delete operation to let the user to specify with a deeper degree of detail what the consumers are allowed to perform on her data. Moreover, we relate the four privilege classes to the SPARQL 1.1 query and update language. This matching is realized with the skos:related property through the SPIN ontology. The latter models the primitives of the SPARQL query and update languages (e.g., SELECT, INSERT DATA, etc.) as SPIN classes. We show how this matching is actually used by our framework in Section 2.2.2.

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III. S ENSOR - **BASED** **CONTROL** USING A STACK OF TASKS
In this section, the **control** law of the first controller is de- signed. This controller is **based** on a stack of tasks, composed of the current active tasks, and on the constraints which have to be taken into account. This stack makes possible very simple actions on the robot, such as activate a task (put a task in the stack), remove a task or swap the priority between two tasks. We explain first how to sequence tasks and to maintain the tasks already achieved. Section III-A recalls the redundancy formalism [21], [14]. It has first been used for sensor-**based** **control** in [35] and in numerous applications since (e.g. visual servoing in [11], force distribution for the legs of a walking machine [18], or human-machine cooperation using vision **control** [13]). The idea is to use the DOF left by a first task to realize a secondary task at best without disturbing the first one. The major advantage of the redundancy formalism with respect to other methods that join two objectives in one **control** law

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