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International audience; Adaptive control using the -modification provides an easily implementable way to stabilize systems with uncertain or fluctuating parameters. Motivated by a specific application from neuroscience, we extend here this methodology to nonlinear time-delay systems ruled by globally Lipschitz dynamics. In order to make the result more handy in practice, we provide an explicit construction of a Lyapunov–Krasovskii functional (LKF) with linear bounds and strict dissipation rate based on the knowledge of an LKF with quadratic bounds and point-wise dissipation rate. When applied to a model of neuronal populations involved in Parkinson’s disease, the benefits with respect to a pure proportional stabilization scheme are discussed through numerical simulations.

International audience; Objective: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia. Methods: Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation. Results: The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes).Conclusion: The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels. Significance: The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

International audience; This paper is prepared within a collaboration between the Instituto Politécnico Nacional, which is a Mexican research institute that manages research on sliding-mode control theory, and the ARIA research team of the Intégration du Matériau au Système Lab., a French research groupthat engages research on model-based fault diagnosis and fault-tolerant control theories. The paper reviews the application of sliding mode control techniques to fault tolerant control and provides perspectives leading to posing some open problems. Operating principles, definitions of the basic concepts are recalled along with the control objectives and design procedures. The evolution of the sliding mode control technique through five generations (as classified by Fridman, Moreno and coworkers) is reviewed. Their respective design procedures, limitations, and robustness properties are also highlighted. The application of the five generations of sliding-mode controllers to fault-tolerant control is discussed. The focus is on some open problems that are judged to commonly be overlooked. Some applications in real-world systems are also presented.

Motivated by uncertain parameters encountered in Markov decision processes (MDPs) and stochastic games, we study the effect of parameter uncertainty on Bellman operator-based algorithms under a set-based framework. Specifically, we first consider a family of MDPs where the cost parameters are in a given compact set; we then define a Bellman operator acting on a set of value functions to produce a new set of value functions as the output under all possible variations in the cost parameter. We prove the existence of a fixed point of this set-based Bellman operator by showing that it is contractive on a complete metric space, and explore its relationship with the corresponding family of MDPs and stochastic games. Additionally, we show that given interval set bounded cost parameters, we can form exact bounds on the set of optimal value functions. Finally, we utilize our results to bound the value function trajectory of a player in a stochastic game., 15 pages, 4 figures

In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations.

This paper deals with the problem of string stability in a chain of acceleration-controlled vehicles, i.e. how input disturbances affect the distributed system for very long chains. There exist variants of string stability, like avoiding that a local disturbance gets amplified along the chain, or more strongly ensuring that the output vector's p-norm remains bounded for any bounded vector of input disturbances independently of the string length. They are all impossible to achieve with any linear controller if the vehicles only use relative information of few vehicles in front. Previous work has shown that adding absolute velocity into the controller, allows to at least avoid amplification of a local disturbance. In this paper, we consider the stronger definitions of string stability, under this same relaxation of using absolute velocity in the controller. We prove that the influence from input vector to output vector cannot be bounded independently of chain length in the most popular 2-norm sense, with any bounded stabilizing linear controller; while a proportional derivative (PD) controller can guarantee it in the practically relevant infinity-norm sense. Moreover, we identify the disturbance acting on the leader as the main issue for string stability., Comment: presented at IEEE-CDC 2018, Miami; more complete version and correcting a few typos

International audience; In this paper, we study longitudinal motion controlof car-like vehicles platoon navigating in an urban environmentwith minimum communication links. To achieve a higher trafficflow, a constant-spacing policy between successive vehicles iscommonly used but this is at a cost of an increased number ofcommunication links as any vehicle information must broadcastto all its followers. Therefore, we propose a distributed observer-based control law that depends both on communicated andmeasured information. Our formulation allows designing thecontrol law directly in the curvilinear coordinates. Internal andstring stability analysis are conducted. We provide simulationresults, through dynamic vehicular mobility simulator, to illus-trate the feasibility of the proposed approach and corroborate our theoretical findings.

A novel binary decision architecture (BDA) for broadcast news audio classification task is presented in this paper. The idea of developing such architecture came from the fact that the appropriate combination of multiple binary classifiers for two-class discrimination problem can reduce a miss-classification error without rapid increase in computational complexity. The core element of classification architecture is represented by a binary decision (BD) algorithm that performs discrimination between each pair of acoustic classes, utilizing two types of decision functions. The first one is represented by a simple rule-based approach in which the final decision is made according to the value of selected discrimination parameter. The main advantage of this solution is relatively low processing time needed for classification of all acoustic classes. The cost for that is low classification accuracy. The second one employs support vector machine (SVM) classifier. In this case, the overall classification accuracy is conditioned by finding the optimal parameters for decision function resulting in higher computational complexity and better classification performance. The final form of proposed BDA is created by combining four BD discriminators supplemented by decision table. The effectiveness of proposed BDA, utilizing rule-based approach and the SVM classifier, is compared with two most popular strategies for multiclass classification, namely the binary decision trees (BDT) and the One-Against-One SVM (OAOSVM). Experimental results show that the proposed classification architecture can decrease the overall classification error in comparison with the BDT architecture. On the contrary, an optimization technique for selecting the optimal set of training data is needed in order to overcome the OAOSVM.

International audience; Spacecrafts such as Stardust (NASA, 2006) are protected by an ablative Thermal Protection System (TPS) for their hypersonic atmospheric entry. A new generation of TPS material, called Phenolic Impregnated Carbon Ablator (PICA), has been introduced with the Stardust mission. This new generation of low density carbon-phenolic composites is now widely used in the aerospace industry. Complex heat and mass transfer phenomena coupled to phenolic pyrolysis and pyrolysis gas chemistry occur in the material during atmospheric entry. Computer programs, as the Porous material Analysis Toolbox based on OpenFoam (PATO) released open source by NASA, allow to study the material response. In this study, a non-intrusive Anchored Analysis of Variance (Anchored-ANOVA) method has been interfaced with PATO to perform low-cost sensitivity analysis on this problem featuring a large number of uncertain parameters. Then, a Polynomial-Chaos method has been employed in order to compute the statistics of some quantities of interest for the atmospheric entry of the Stardust capsule, by taking into account uncertainties on effective material properties and pyrolysis gas composition. This first study including pyrolysis gas composition uncertainties shows their key contribution to the variability of the quantities of interest.

International audience; A recent force-fatigue parameterized mathematical model, based on the seminal contributions of V. Hill to describe muscular activity, allows to predict the muscular force response to external electrical stimulation (FES) and it opens the road to optimize the FES-input to maximize the force response to a pulse train, to track a reference force while minimizing the fatigue for a sequence of pulse trains or to follow a reference joint angle trajectory to produce motion in the non-isometric case. In this article, we introduce the geometric frame to analyze the dynamics and we present Pontryagin types necessary optimality conditions adapted to digital controls, used in the experiments, vs permanent control and which fits in the optimal sampled-data control frame. This leads to Hamiltonian differential variational inequalities, which can be numerically implemented vs direct optimization schemes.

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.

International audience; This paper investigates the problem of observer design for a general class of linear singular time-delay systems, in which the time delays are involved in the state and its derivatives, the output and the known input (if there exists). The involvement of the delay could be multiple which however is rarely studied in the literature. Sufficient conditions are proposed which guarantees the existence of a Luenberger-like observer for the general system.

International audience; The Inventory Routing Problem has been mainly studied in recent decades under an economic performance perspective. In this paper, we develop a multi-objective mathematical framework for the IRP to link: (i) the economic performance, (ii) the achieved server level in terms of shortage and delivery delays and (iii) the environmental footprint. The framework developed addresses the uncertainty by considering fuzzy distributions for certain problem inputs, such as the demand and the transportation costs. We show the negative impact on the economic performance when service level targets are exogenously chosen without coordination with the logistics components (inventory and distribution). (C) 2017 Elsevier Ltd. All rights reserved.

International audience; Reinforcement learning problems are hard to solve in a robotics context as classical algorithms rely on discrete representations of actions and states, but in robotics both are continuous. A discrete set of actions and states can be defined, but it requires an expertise that may not be available, in particular in open environments. It is proposed to define a process to make a robot build its own representation for a reinforcement learning algorithm. The principle is to first use a direct policy search in the sensori-motor space, i.e. with no predefined discrete sets of states nor actions, and then extract from the corresponding learning traces discrete actions and identify the relevant dimensions of the state to estimate the value function. Once this is done, the robot can apply reinforcement learning (1) to be more robust to new domains and, if required, (2) to learn faster than a direct policy search. This approach allows to take the best of both worlds: first learning in a continuous space to avoid the need of a specific representation, but at a price of a long learning process and a poor generalization, and then learning with an adapted representation to be faster and more robust.

This paper frames a model which summarizes the principal factors and relationships to incorporate human element, as per fatigue engagement, in the strategic and tactical decision making. A characterization of humans as per a dynamic interferer in system is presented. The findings are tested in an U-shaped assembly division of an international motor company. Dual-Resource Constrained sets are proposed. A Multi Agent model architecture, incorporating system dynamics modelling per humans, is implemented. ANOVA is executed for interaction analyses. Results designed optimal job sequence, rest break and task’s sequence, recovery rate and load characters as per enhanced system’s performances. Stable fatigued conditions are under complete recovery. Production rate in system is affected by fatigue that in turn increases with rise in switching rate. Chaotic market behavior prefers decentralized assignment of worker to task. Continuous switching suffers from learning attitude. Chaotic demand impairs fatigue across workforce reporting instable operator fatigue under different allocation rules. Under loudly physical tasks, random assignment is preferable for productivity outcomes. Autonomous control of workers at work considerably impairs fatigue under complete resting schema.

Creep-age-forming of a thick Al-Cu-Li sheet is studied. An industrial stamping press is used to form a double curvature panel at a reduced scale. This forming, which includes several relaxation steps, is modelled using ABAQUS. A material model describing an elasto-viscoplastic behaviour with anisotropy effect has been identified and implemented in ABAQUS using Fortran subroutine. The numerical model is validated by comparing experiments and numerical results in terms of deformed shapes and an improved forming cycle is suggested. This paper is part of the METAFOR project managed by IRT Jules Verne (French Institute in Research and Technology in Advanced Manufacturing Technologies for Composite, Metallic and Hybrid Structures). The authors wish to associate the industrial and academic partners of this project; respectively ACB, STELIA, AIRBUS, CONSTELLIUM, ENSAM.

International audience; This paper investigates the interval observer design for a class of nonlinear continuous systems, which can be represented as a superposition of a uniformly observable nominal subsystem with a Lipschitz nonlinear perturbation. It is shown in this case there exists an interval observer for the system that estimates the set of admissible values for the state consistent with the output measurements. An illustrative example of the observer application is given with simulation results.

International audience; Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results showthat MPC can be effectively used for control inwastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.

International audience; This paper proposes a Fuzzy Dedicated Observers (FDOS) method using a Nonlinear Unknown Input Fuzzy Observer (UIFO) with a Fuzzy Fault Tolerant Control (FFTC) algorithm for fuzzy Takagi-Sugeno (TS) systems subject to sensor faults. FDOS provide residuals for detection and isolation of sensor faults which can affect a TS model. Parallel Distributed Compensation (PDC) is employed to design the fuzzy Fault Tolerant Controller from a TS fuzzy model. Sufficient stabilization conditions of the FFTC systems are given, which are formulated in terms of Linear Matrix Inequalities (LMIs). The LMIs can be efficiently solved using convex optimization techniques. Based on the derivation and simulation of the proposed FFTC approach, the stability of the closed-loop system in the event of sensor failures and unknown inputs is guaranteed. A wind Energy System (WES) with a Doubly-Fed Induction Generator (DFIG) example is illustrated to show the effectiveness of the proposed design method.

The better understanding of the material cutting process has been shown with the benefit of the forces and moments measurement since some years ago. In paper, simultaneous six mechanical components and chip orientation measurements were realized during turning tests. During these tests, the influence of the depth of cut or feed rate has been observed and a link between the chip orientation and the moment vector orientation or the central axis characteristics has been shown. Nomenclature * corresponding author

International audience; The six axis robots are widely used in automotive industry for their good repeatability (as defined in the ISO92983) (painting, welding, mastic deposition, handling etc.). In the aerospace industry, robot starts to be used for complex applications such as drilling, riveting, fiber placement, NDT, etc. Given the positioning performance of serial robots, precision applications require usually external measurement device with complexes calibration procedure in order to reach the precision needed. New applications in the machining field of composite material (aerospace, naval, or wind turbine for example) intend to use off line programming of serial robot without the use of calibration or external measurement device. For those applications, the position, orientation and path trajectory precision of the tool center point of the robot are needed to generate the machining operation. This article presents the different conditions that currently limit the development of robots in robotic machining applications. We analyze the dynamical behavior of a robot KUKA KR240-2 (located at the University of Bordeaux 1) equipped with a HSM Spindle (42000 rpm, 18kW). This analysis is done in three stages. The first step is determining the self-excited frequencies of the robot structure for three different configurations of work. The second phase aims to analyze the dynamical vibration of the structure as the spindle is activated without cutting. The third stage consists of vibration analysis during a milling operation.

International audience; This paper proposes a method to vibration analysis in order to on-line monitoring of milling process quality. Adapting envelope analysis to characterize the milling tool materials is an important contribution to the qualitative and quantitative characterization of milling capacity and a step by modeling the three-dimensional cutting process. An experimental protocol was designed and developed for the acquisition, processing and analyzing three-dimensional signal. The vibration envelope analysis is proposed to detect the cutting capacity of the tool with the optimization application of cutting parameters. The research is focused on Hilbert transform optimization to evaluate the dynamic behavior of the machine/ tool/workpiece.

International audience; In this paper, we study under which conditions the trajectories of a mechanical control system can track any curve on the configuration manifold. We focus on systems that can be represented as forced affine connection control systems and we generalize the sufficient conditions for tracking known in the literature. The sufficient conditions are expressed in terms of convex cones of vector fields defined through particular brackets of the control vector fields of the system. The tracking control laws obtained by our constructions depend on several parameters. By imposing suitable asymptotic conditions on such parameters, we construct algorithmically one-parameter tracking control laws. The theory is supported by examples of control systems associated with elliptic hovercrafts and ellipsoidal submarines.

International audience; A mobile robot represented by a point moving in the plane has to explore an unknown flat terrain with impassable obstacles. Both the terrain and the obstacles are modeled as arbitrary polygons. We consider two scenarios: the unlimited vision, when the robot situated at a point p of the terrain explores (sees) all points q of the terrain for which the segment pq belongs to the terrain, and the limited vision, when we require additionally that the distance between p and q be at most 1. All points of the terrain (except obstacles) have to be explored and the performance of an exploration algorithm, called its complexity, is measured by the length of the trajectory of the robot. For unlimited vision we show an exploration algorithm with complexity $O(P+D\sqrt{k})$, where P is the total perimeter of the terrain (including perimeters of obstacles), D is the diameter of the convex hull of the terrain, and k is the number of obstacles. We do not assume knowledge of these parameters. We also prove a matching lower bound showing that the above complexity is optimal, even if the terrain is known to the robot. For limited vision we show exploration algorithms with complexity $O(P+A+\sqrt{Ak})$, where A is the area of the terrain (excluding obstacles). Our algorithms work either for arbitrary terrains, if one of the parameters A or k is known, or for c-fat terrains, where c is any constant (unknown to the robot) and no additional knowledge is assumed. (A terrain ${\mathcal T}$ with obstacles is c-fat if R/r ≤ c, where R is the radius of the smallest disc containing ${\mathcal T}$ and r is the radius of the largest disc contained in ${\mathcal T}$.) We also prove a matching lower bound $\Omega(P+A+\sqrt{Ak})$ on the complexity of exploration for limited vision, even if the terrain is known to the robot.

International audience; Being an optimizing technology, model predictive control (MPC) can now be found in a wide variety of application fields. The main and most obvious control goal to be achieved in a wastewater treatment plant is to fulfill the effluent quality standards, while minimizing the operational costs. In order to maintain the effluent quality within regulation-specified limits, the MPC strategy has been applied to the Benchmark Simulation Model 1 (BSM1) simulation benchmark of wastewater treatment process. After the discussion of open loop responses of outputs to manipulated inputs and measured influent disturbances, the strategies of feedback by linear dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC) and nonlinear model predictive control (NLMPC), and improvement by feedforward based on influent flow rate or ammonium concentration have been investigated. The simulation results indicate that good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. Compared to DMC and QDMC, NLMPC with penalty function brings little improvement. Two measured disturbances have been used for feedforward control, the influent flow rate and ammonium concentration. It is shown that the performance of feedforward with respect to the influent ammonium concentration is much higher than for the feedforward with respect to the influent flow rate. However, this latter is slightly better than the DMC feedback. The best performance is obtained by combining both feedforward controllers with respect to the influent ammonium concentration and flow rate. In all cases, the improvement of performance is correlated with more aeration energy consumption

Acknowledgements The authors would like to thank the French “Ministère des Affaires étrangères et européennes” for the financial support of this project under the program ECO-NET project N° 21367XL; International audience; A testing device in turning including, in particular, a six-component dynamometer, is used to measure the complete torque of the cutting actions, in a case of self-excited vibrations. For the tests, the used tool was a noncoated carbide tool (TNMA 160412) without chip breaker. The cutting material is a chrome molybdenum alloy type (ASI 4140). The cylindrical test tubes have a diameter of 120 mm and a length of 30 mm. For the first time, we present an analysis of forces and moments for different depths of cut and different feed rates.

The PrandtlPlane (PrP) aircraft wing-box least-weight design is presented in this work. This design problem is formulated as a constrained non-linear programming problem (CNLPP), by integrating static, buckling, fatigue and manufacturability requirements, under different loading conditions. The solution search is carried out by means of a suitable multi-scale optimisation (MSO) approach. The physical responses involved into the CNLPP formulation are evaluated at the wing-box architecture level (macroscopic scale) and at the stiffened panel level (component scale), as well. The scale transition is ensured by means of a suitable global-local (GL) modelling approach, while the CNLPP is solved by means of an in-house genetic algorithm. The effectiveness of the proposed approach is tested on the PrP wing-box structure, but the presented strategy can be easily extended to conventional aircraft wings. This paper presents part of the activities carried out within the research project PARSIFAL (PrandtlPlane ARchitecture for the Sustainable Improvement of Future AirpLanes), which has been funded by the European Union under the Horizon 2020 Research and Innovation Program (Grant Agreement n.723149).