NeCS is a joint Inria/Gipsa-Lab team, bi-located at the Inria-Rhône-Alpes center in Montbonnot and at Gipsa-Lab (http://

The research field of Networked Controlled Systems deals with feedback systems controlled over networks, but also concerns systems that naturally exhibit a network structure (e.g., traffic, electrical networks, etc.).

The first system category results from the arrival of new control problems posed by the consideration of several factors, such as: new technological components (e.g., wireless, RF, communications, local networks, etc.), increase of systems complexity (e.g., increase in vehicle components), the distributed location of sensor and actuator, and computation constraints imposed by their embedded nature. In this class of systems, the way that the information is transferred and processed (information constraints), and the manner in which the computation resources are used (resources management), have a substantial impact in the resulting stability and performance properties of the feedback controlled systems. One main challenge here is the co-design of control and one or more other components of different nature are considered. The NeCS team has tackled co-design problems concerning:

Control under communications and network constraints;

Control under resources constraints.

The second category of systems is motivated by the natural network structure in which the original systems are built. Examples are biologic networks, traffic networks, and electrical networks. The complex nature of such systems makes the classical centralized view of the control design obsolete. New distributed and/or collaborative control and estimation algorithms need to be devised as a response to this complexity. Even if the dynamic behavior of each individual system is still important, the aggregated behavior (at some macroscopic level), and its interconnection graph properties become of dominant importance. To build up this research domain, the team has put a strong focus on traffic (vehicular) networks, and in some associated research topics capturing problems that are specific to these complex network systems (distributed estimation, graph-discovering, etc).

In 2013, Carlos Canudas de Wit, leader of the NeCS team, has become:

President Elect of the European Control Association EUCA (http://

IEEE CSS distinguished lecturer;

Associate Editor of IEEE Transactions on Control System Technology (since January 2013) and of IEEE Transactions on Control of Network Systems (since June 2013).

NeCS team deals with Networked Control Systems. Since its foundation in 2007, the team has been addressing issues of control under imperfections
and constraints deriving from the network (limited computation resources of the embedded systems, delays and
errors due to communication, limited energy resources), proposing co-design strategies.
The team has recently moved its focus towards general problems on *control of network systems*, which involve the analysis and control of dynamical systems with a network structure or whose operation is supported by networks. This is a research domain with substantial growth and is now recognized as a priority sector by the IEEE Control Systems Society: IEEE has started in a new journal, IEEE Transactions on Control of Network Systems, whose first issue will appear in 2014.

More in detail, the research program of NeCS team is along lines described in the following sections.

This research topic concerns distributed data combination from multiple sources (sensors) and related information fusion, to achieve more specific inference than could be achieved by using a single source (sensor). It plays an essential role in many networked applications, such as communication, networked control, monitoring, and surveillance. Distributed estimation has already been considered in the team. We wish to capitalize and strengthen these activities by focusing on integration of heterogeneous, multidimensional, and large data sets:

Heterogeneity and large data sets. This issue constitutes a clearly identified challenge for the future. Indeed, heterogeneity comes from the fact that data are given in many forms, refer to different scales, and carry different information. Therefore, data fusion and integration will be achieved by developing new multi-perception mathematical models that can allow tracking continuous (macroscopic) and discrete (microscopic) dynamics under a unified framework while making different scales interact with each other. More precisely, many scales are considered at the same time, and they evolve following a unique fully-integrated dynamics generated by the interactions of the scales. The new multi-perception models will be integrated to forecast, estimate and broadcast useful system states in a distributed way. Targeted applications include traffic networks and navigation, and concern recent grant proposals that team has elaborated, among which the SPEEDD EU FP7 project, which has been accepted and will start in February 2014.

Multidimensionality. This issue concerns the analysis and the processing of multidimensional data, organized in multiway array, in a distributed way. Robustness of previously-developed algorithms will be studied. In particular, the issue of missing data will be taken into account. In addition, since the considered multidimensional data are generated by dynamic systems, dynamic analysis of multiway array (or tensors) will be considered. The targeted applications concern distributed detection in complex networks and distributed signal processing for collaborative networks. This topic is developed in strong collaboration with UFC (Brazil).

This is a research topic at the boundaries between graph theory and dynamical systems theory.

A first main line of research will be to study complex systems whose interactions are modeled with graphs, and to unveil the effect of the graph topology on system-theoretic properties such as observability or controllability. In particular, on-going work concerns observability of graph-based systems: after preliminary results concerning consensus systems over distance-regular graphs, the aim is to extend results to more general networks. A special focus will be on the notion of `generic properties', namely properties which depend only on the underlying graph describing the sparsity pattern, and hold true almost surely with a random choice of the non-zero coefficients. Further work will be to explore situations in which there is the need for new notions different from the classical observability or controllability. For example, in social networks or in birds flocking the potential leader might have a goal different from classical controllability, because on the one hand he might have a goal much less ambitious than being able to drive the system to any possible state (e.g., he might want to drive everybody near its own opinion, only), and on the other hand he might have much weaker tools to construct its input (e.g., he might not know the whole system's dynamics, but only a few things, possibly that the system is linear and one row of the matrix only). Another example is the question of detectability of an unknown input under the assumption that such an input has a sparsity constraint, a question arising from the fact that a cyber-physical attack might be modeled as an input aiming at controlling the system's state, and that limitations in the capabilities of the attacker might be modeled as a sparsity constraint on the input.

A second line of research will concern graph discovery, namely algorithms aiming at reconstructing some properties of the graph (such as the number of vertices, the diameter, the degree distribution, or spectral properties such as the eigenvalues of the graph Laplacian), using some measurements of quantities related to a dynamical system associated with the graph. It will be particularly challenging to consider directed graphs, and to impose that the algorithm is anonymous, i.e., that it does not makes use of labels identifying the different agents associated with vertices.

This research line deals with the problem of designing controllers with a limited use of the network information (i.e. with restricted feedback), and with the aim to reach a pre-specified global behavior. This is in contrast to centralized controllers that use the whole system information and compute the control law at some central node. Collaborative control has already been explored in the team in connection with the underwater robot fleet, and to some extent with the source seeking problem. It remains however a certain number of challenging problems that the team wishes to address:

Design of control with limited information, able to lead to desired global behaviors. Here the graph structure is imposed by the problem, and we aim to design the “best” possible control under such a graph constraint

Clustering control for large networks. For large and complex systems composed of several sub-networks, feedback design is usually treated at the sub-network level, and most of the times without taking into account natural interconnections between sub-networks. The team wishes to explore new control strategies, exploiting the emergent behaviors resulting from new interconnections between the network components. This requires first to build network models operating in aggregated clusters, and then to re-formulate problems where the control can be designed using the cluster boundaries rather than individual control loops inside of each network. Examples can be found in the transportation application domain, where a significant challenge will be to obtain dynamic partitioning and clustering of heterogeneous networks in homogeneous sub-networks, and then to control the perimeter flows of the clusters to optimize the network operation.

This is currently the main application domain of the NeCS team. Several interesting problems in this area capture many of the generic networks problems described above. For example, distributed collaborative algorithms can be devised for ramp-metering control and traffic-density balancing can be achieved using consensus concepts. The team is already strongly involved in this field, both this theoretical works on traffic prediction and control, and with the Grenoble Traffic Lab platform. These activities will be continued and strengthened.

Sensor and actuator networks are ubiquitous in modern world, thanks to the advent of cheap small devices endowed with communication and computation capabilities. Potential application domains for research in networked control and in distributed estimation are extremely various, and include the following examples.

Intelligent buildings, where sensor information on

Smart grids: the operation of electrical networks is changing from a centralized optimization framework towards more distributed and adaptive protocols, due to the high number of small local energy producers (e.g., solar panels on house roofs) that now interact with the classic large power-plants.

Disaster relief operations, where data collected by sensor networks can be used to guide the actions of human operators and/or to operate automated rescue equipment.

Pedestrian navigation, providing guidance e.g. to first responders after a disaster, or to blind people walking in unfamiliar environments. This tasks is particularly challenging in-door, where no GPS is available.

Surveillance using swarms of Unmanned Aerial Vehicles (UAVs), where sensor information (from sensors on the ground and/or on-board) can be used to guide the UAVs to accomplish their mission.

Environmental monitoring and exploration using self-organized fleets of Autonomous Underwater Vehicles (AUVs), collaborating in order to reach a goal such as finding a pollutant source or tracing a seabed map.

Infrastructure security and protection using smart camera networks, where the images collected are shared among the cameras and used to control the cameras themselves (pan-tilt-zoom) and ensure tracking of potential threats.

In particular, NeCS team is currently focusing in the areas described in detail below.

Throughout the world, roadways are notorious for their congestion, from dense urban network to large freeway systems. This situation tends to get worse over time due to the continuous increase of
transportation demand whereas public investments are decreasing and space is lacking to build new infrastructures. The most obvious impact of traffic congestion for citizens is the increase of travel
times and fuel consumption. Another critical effect is that infrastructures are not operated at their capacity during congestion, implying that fewer vehicles are served than the amount they were designed for. Using macroscopic fluid-like models, the NeCS team has initiated new researches to develop innovative traffic management policies able to improve the infrastructure operations.
The research activity is on two main challenges: forecasting, so as to provide accurate information to users, e.g., travel times; and control, via ramp-metering and/or variable speed limits.
The Grenoble Traffic Lab (see Sect. and http://

More recently, in the ANR project VOLHAND (2009-2013), the team has been developing a new generation of electrical power-assisted steering specifically designed for disabled and aged persons.

Currently, on-going work under a grant with IFPEN studies how to save energy and reduce pollution, by controlling a vehicle's speed in a smart urban environment, where infrastructure-to-vehicle and vehicle-to-vehicle communications happen and can be taken into account in the control.

Due to the cost or the risks of using human operators, many tasks of exploration, or of after-disaster intervention are performed by un-manned drones. When communication becomes difficult, e.g., under water, or in spatial exploration, such robots must be autonomous. Complex tasks, such as exploration, or patrolling, or rescue, cannot be achieved by a single robot, and require a self-coordinated fleet of autonomous devices. NeCS team has studied the marine research application, where a fleet of Autonomous Underwater Vehicles (AUVs) self-organize in a formation, adapting to the environment, and reaching a source, e.g., of a pollutant. This has been done in collaboration with IFREMER, within the national project ANR CONNECT and the European FP7 project FeedNetBack. On-going research in the team concerns source localization, with a fleet of mobile robots, including wheeled land vehicles.

The Grenoble Traffic Lab (GTL) initiative, led by the NeCS team, is a real-time traffic data center (platform) that collects traffic road infrastructure information in real-time with minimum latency and fast sampling periods. The main elements of the GTL are: a real-time data-base, a show room, and a calibrated micro-simulator of the Grenoble South Ring. Sensed information comes from a dense wireless sensor network deployed on Grenoble South Ring, providing macroscopic traffic signals such as flows, velocities, densities, and magnetic signatures. This sensor network was set in place in collaboration with Inria spin-off Karrus-ITS, local traffic authorities (DIR-CE, CG38, La Metro), and specialized traffic research centers. In addition to real data, the project also uses simulated data, in order to validate models and to test the ramp-metering; the micro-simulator is a commercial software (developed by TSS AIMSUN ©).

More details at http://

NeCSCar is an electrical vehicle (scale 1:3) used as an experimental platform to study new control architectures. The vehicle is designed to be remotely tele-operated from our active steering-wheel platform, ant it will be equipped of a 3D vision system to provide the operator with stereo vision capabilities. Bilateral teleoperation can be performed using wheel contact torque measurements, fed back for force deflexion; wireless connection allows us to test coding algorithms, resource sharing, and robustness against transmission delays.

NeCSCar has been recently used for simulation tests in the framework of the VolHand project, a multi-disciplinary project with the goal to develop a new generation of electronic power assistance steering (EPAS) systems for disabled people.

The source-seeking algorithms developed in the thesis of Ruggero Fabbiano have been implemented in hardware, with a wheeled robot performing 2-dimensional search.
The considered scenario is a source of pollutant in the ocean, where the pollutant can be detected thanks to the fact that it is warmer than water, so that data from an infra-red camera can be used by one or multiple helicopters to move along the ocean surface towards the source. In our experimental equipment, the 2-dimensional movement has been performed with a wheeled vehicle, and the camera was a regular camera, taking pictures of a color-coded image from an actual infra-red image of a pollutant leak.
Videos of the experiments are available online: http://

The availability of information on the communication topology of a wireless sensor network is essential for the design of the estimation algorithms. In the context of distributed self-organized sensor networks, there is no central unit with the knowledge of the network, and the agents must run some distributed network discovery algorithm.

We have studied the problem of estimating the eigenvalues of the Laplacian matrix associated with a graph modeling the interconnections between the nodes of a given network. Our approach is based on properties resulting from the factorization of the average consensus matrix. Indeed, as recently shown , the average consensus matrix can be written as a product of Laplacian based consensus matrices whose stepsizes are given by the inverse of the nonzero Laplacian eigenvalues. By distributively solving the factorization of the average consensus matrix, we have shown that the Laplacian eigenvalues can be computed as the inverse of the stepsizes in each estimated factor, where these factors are constrained to be structured as Laplacian based consensus matrices. A constrained optimization problem was formulated and distributed gradient descent methods have been formulated. As formulated, the problem can be viewed as a consensus problem with equality constraints. In contrast to the state-of-the-art, the proposed algorithm does not require great resources in both computation and storage. This algorithm can also be viewed as a way for decentralizing the design of finite-time average consensus protocol recently proposed in the literature.

Laplacian eigenvalues have several interesting properties that can help to study networks, however they cannot uniquely characterize the topology of the network. Therefore, we have directly studied the problem of topology identification in . The considered set-up concerns a collaborative wireless sensor network where nodes locally exchange coded informative data before transmitting the combined data towards a remote fusion center equipped with an antenna array. For this communication scenario, a new blind estimation algorithm was developed for jointly recovering network transmitted data and connection topology at the fusion center. The proposed algorithm is based on a two-stage approach. The first stage is concerned with the estimation of the channel gains linking the nodes to the fusion center antennas. The second stage performs a joint estimation of network data and connection topology matrices by exploiting a constrained (PARALIND) tensor model for the collected data at the fusion center.

Distributed network-discovery algorithms become even more challenging in the case where the algorithm must be anonymous, namely in the case when the agents do not have or do not want to disclose their identifiers (id.s), either for technological reasons (in time-varying self-organized networks, assigning unique identifiers to agents is a challenge) or for privacy concerns. In anonymous networks, even simple tasks such as counting the number of agents are challenging problems. In we have proposed an algorithm for node-counting in anoymous networks. It is based on a graph-constrained LTI system similar to linear consensus, and on system identification: the idea is that the order of the system is the number of agents, and based on local observations each agent tries to identify the order of the system, testing the rank of the Hankel matrix from the output data.

Studying the observability problem of a system consists in answering the question: is it possible, for a given node, to reconstruct the entire network state just from its own measurements and those of its neighbors? Studying observability for arbitrary graphs is particularly a tough task, therefore, studies are generally restricted to some families of graphs; for instance, recently, observability has been studied for paths and circular graphs and also grids where the study was carried out based on rules on number theory. We have considered families of graphs admitting an association scheme such that strongly regular graphs and distance regular graphs. The regularity properties of these kinds of graphs can particularly be useful for robustifying the network as for cryptographic systems. Based on the so-called Bose-Mesner algebra, we have stated observability conditions on consensus networks modeled with graphs modeled with strongly regular graphs and distance regular graphs; for this purpose, we have introduced the notion of local observability bipartite graph that allows characterizing the observability in consensus networks. We have shown that the observability condition is given by the nullity of the so-called "local bipartite observability graph"; when the nullity of the graph cannot be derived directly from the structure of the local bipartite observability graph, the rank of the associated bi-adjacency matrix allows evaluating the observability. The bi-adjacency matrix of the local bipartite observability graph must be full column rank for guaranteeing observability. From this general necessary and sufficient condition, we have deduced sufficient conditions for strongly regular graphs and distance regular graphs .

Nowadays, several distributed estimation algorithms are based on the average consensus concept. Average consensus can be reached using a linear iterations scheme where each node repeatedly updates its value as a weighted linear combination of its own value and those of its neighbors; the main benefit of using a linear iterations scheme is that, at each time-step, each node only has to transmit a single value to each of its neighbors. Based on such a scheme, several algorithms have been proposed in the literature; however, in the most proposed algorithms the weights are chosen so that all the nodes asymptotically converge to the same value. Sometimes, consensus can be embedded as a step of more sophisticated distributed; obviously, asymptotic convergence is not suitable for these kinds of distributed methods, and therefore it is interesting to address the question of exact consensus in finite-time. For time-invariant network topologies and in the perfect information exchange case, i.e., without channel noise nor quantization, we have shown that the finite-time average consensus problem can be solved as a matrix factorization problem with joint diagonalizable matrices depending on the graph Laplacian eigenvalues; moreover, the number of iterations is equal to the number of distinct nonzero eigenvalues of the graph Laplacian matrix. The design of such a protocol requires the knowledge of the Laplacian spectrum, which can be carried out in a distributed way (see Section . The matrix factorization problem is solved in a distributed way, in particular a learning method was proposed and the optimization problem was solved by means of distributed gradient backpropagation algorithms. The factor matrices are not necessarily symmetric and the number of these factor matrices is exactly equal to the diameter of the graph .

Traditional analysis of linear average-consensus algorithms studies, for a given communication graph, the convergence rate, given by the essential spectral radius of the transition matrix (i.e., the second largest eigenvalues' modulus). For many graph families, such analysis predicts a performance which degrades when the number of agents grows, basically because spreading information across a larger graph requires a longer time; however, when considering other well-known quadratic performance indices (involving all the eigenvalues of the transition matrix), the scaling law with respect to the number of agents can be different. This is consistent with the fact that, in many applications, for example in estimation problems, it is natural to expect that a larger number of cooperating agents has a positive, not a negative effect on performance. It is natural to use a different performance measure when the algorithm is used for different purposes, e.g., within a distributed estimation or control algorithm. We are interested in evaluating the effect of the topology of the communication graph on performance, in particular for large-scale graphs. We have focused on graph families which can describe sensor networks, and hence have geometric constraints, namely nodes can be connected only with nearby nodes in the sense of Euclidean distance .

The problem of source localization consists in finding, with one or several agents possibly cooperating with each other, the point or the spatial region from which a quantity of interest is being emitted. Source-seeking agents can be fixed sensors, that collect and exchange some information about the signal field and try to identify the position of the source (or the smallest region in which it is included), or moving devices equipped with one or more sensors, that physically reach the source in an individual or cooperative way. This research area is attracting a rapidly increasing interest, in particular in applications where the agents have limited or no position information and GPS navigation is not available, as in underwater navigation or in cave exploration: for instance, source localization is relevant to many applications of vapor emitting sources such as explosive detection, drug detection, sensing leakage or hazardous chemicals, pollution sensing and environmental studies. Other fields of interest are sound source localization, heat source localization and vent sources in underwater field. Techniques present in literature either are based on a specific knowledge of the solution of the diffusion process, or make use of an extremum-seeking approach, exciting the system with a periodic signal so as to explore the field and collect enough information to reconstruct the gradient of the quantity of interest. Our approach lies in the computation of derivatives (potentially of any order) from Poisson integrals that, for isotropic diffusive source in steady-state, whose solution satisfies the Laplace equation, allows for a gradient search with a small computation load (derivatives are computed by integrals) and without requiring any knowledge of the closed-form solution, avoiding in the same time extremum-seeking oscillations; this has the additional advantage of an intrinsic high-frequency filtering, that makes the method low sensitive to measurement noise. This work is the topic of the Ph.D. of Ruggero Fabbiano, and is described in papers under review.

Moreover, a hardware implementation of the source-seeking algorithm has been done during the internship of Yvan Gaudfrin, at Gipsa-Lab with the support of Jonathan Dumon. A description of the setup and videos of the source-seeking robot are available online:
http://

We are interested to motion capture (or attitude) by fusing Inertial and Magnetic Sensors. In , we present a viable quaternion-based Complementary Observer (CO) which is designed for rigid body attitude estimation. The CO processes data from a small inertial/magnetic sensor module containing tri-axial angular rate sensors, accelerometers, and magnetometers, without resorting to GPS data. The proposed algorithm incorporates a motion kinematic model and adopts a two-layer filter architecture. In the latter, the Levenberg Marquardt Algorithm (LMA) pre-processes acceleration and local magnetic field measurements, to produce what will be called the system's output. The system's output together with the angular rate measurements will become measurement signals for the CO. In this way, the overall CO design is greatly simplified. The efficiency of the CO is experimentally investigated through an industrial robot and a commercial IMU during human segment motion exercises. In a recent work , a viable quaternion-based Adaptive Kalman Filter (q-AKF) that is designed for rigid body attitude estimation. This approach is an alternative to overcome the limitations of the classical Kalman filter. The q-AKF processes data from a small inertial/magnetic sensor module containing triaxial gyroscopes, accelerometers, and magnetometers. The proposed approach addresses two challenges. The first one concerns attitude estimation during various dynamic conditions, in which external acceleration occurs. Although external acceleration is one of the main source of loss of performance in attitude estimation methods, this problem has not been sufficiently addressed in the literature. An adaptive algorithm compensating external acceleration from the residual in the accelerometer is proposed. At each step, the covariance matrix associated with the external acceleration is estimated to adaptively tune the filter gain. The second challenge is focused on the energy consumption issue of gyroscopes for long-term battery life of Inertial Measurement Units. We study the way to reduce the gyro measurement acquisition while maintaining acceptable attitude estimation. Through numerical simulations, under external acceleration and parsimonious gyroscope’s use, the efficiency of the proposed q-AKF is illustrated.

We proposes a foot-mounted Zero Velocity Update (ZVU) aided Inertial Measurement Unit (IMU) filtering algorithm for pedestrian tracking in indoor environment . The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and gait phase. The foot motion filtering algorithm incorporates methods for orientation estimation, gait detection, and position estimation. A novel Complementary Filter (CF) is introduced to better pre-process the sensor data from a foot-mounted IMU containing tri-axial angular rate sensors, accelerometers, and magnetometers and to estimate the foot orientation without resorting to GPS data. A gait detection is accomplished using a simple states detector that transitions between states based on acceleration measurements . Once foot orientation is computed, position estimates are obtained by using integrating acceleration and velocity data, which has been corrected at step stance phase for drift using an implemented ZVU algorithm, leading to a position accuracy improvement. We illustrate our findings experimentally by using of a commercial IMU during regular human walking trial in a typical public building. Experiment results show that the positioning approach achieves approximately a position accuracy less than 1 m and improves the performance regarding a previous work of literature .

We have considered an event-based approach to energy-efficient management of the radio chip in the sensor node of a wireless networked control system. Indeed the radio is the main energy consumer, and intermittent data transmission allows one to reduce the use of the radio. While the existing literature in the control community on event-based control only addresses policies using two radio modes (transmitting/sleep), our work follows some considerations on the radio chip modes well-known in the communication networks literature, and introduces various radio-modes: different ‘idle’ non-transmitting modes, where only part of the radio chip is switched off (thus consuming more energy than ‘sleep’, but allowing for faster transition to transmission), and various transmitting modes, with different power levels. We propose an event-based radio-mode switching policy, which allows to perform a trade-off between energy saving and performance of the control application; to this end, a switched model describes the system, taking into account control and communication. The optimal switching policy is computed using dynamic programming, considering a cost either over an infinite time-horizon (see ) or over a finite receding horizon (joint work with D. Quevedo, Univ. Newcastle, Australia, described in a paper in preparation).

In aerospace field, the economic realization of a spacecraft is one of the main objectives which should be accomplished by conceiving the optimal propulsion system and the best control algorithms. Our work focuses on the development of a viable Adaptive Control Approach (ACA) for Spacecraft Motion Trajectory (SMT), see .The proposed strategy involves the nonlinear mathematical model of SMT expressed in the central field, which is linearized by the Taylor expansion, and the second Lyapunov method to offer a high rate and unfailing performance in the functioning. The adaptive control system is composed of the cascade of adaptation loop and feedback control loop. When the spacecraft deviates from its reference trajectory model, the ACA acts on the control system to correct this deviation and follow the optimal reference trajectory. Therefore, when the states of the adjustable model are different from its reference values, then the error signal is provided as an input to the adaptation law, which contains the adaptation algorithm. The output will be the state variable feedback control matrix which will be used to calculate the new control law vector. The efficiencies of the linearization procedure and the control approach are theoretically investigated through some realistic simulations and tests under MATLAB. The steady state errors of control between the reference model and the adjustable model of SMT converge to zero. This work is described in .

Reconstructing densities in portions of the road links
not equipped with sensors constitutes an important task
in traffic estimation, forecasting, and control problems.
Among many other approaches, model-based observers is
one popular technique to build this information. They can
also be understood as *virtual sensors* deployed inside of
the cells not equipped with *true sensors*. They are used
to better track, in real-time, density variations with a fine
degree of granularity in the space, as the *virtual cells* can
be selected as small as desired.

In , a graph constrained-CTM observer was introduced. It allows reconstructing rather accurately the internal states (densities) of a road portion not equipped with sensors. This strategy for real-time density estimation was applied on Grenoble South Ring. In , this observer has been associated with an adaptive Kalman filtering approach for traffic prediction in terms of travel time. The adaptive Kalman filtering approach was also been used for predicting input flows in .

This work has been conducted in two parallel directions, combining steady state analysis and design of an optimal ramp metering controller.

The first direction was to extend the preliminary results presented in the papers and . The goal was to implement the idea of optimal steady state balancing. A relevant software was built up and tested on the model representing the south ring of Grenoble. The results were published in . A comprehensive study of steady state balancing was submitted as a journal paper, under review.

The second direction was to develop a distributed optimal ramp metering controller. This study is motivated by two following facts. The first one is to decentralize and parallelize computation for optimal freeway traffic control problem, and thus to reduce computational complexity. The second one is to reduce the lengths of the communication channels, in order to eliminate the probability of information delay or packet loss. The proposed new control objective provides a uniformly distributed (or balanced) vehicle density such that the usage of freeway (measured by the Total Travel Distance and Total Input Volume) is maximized. This optimal balancing objective is reached by taking a proper state feedback control structure and optimizing the set of gains. Here we imposed distributed condition for both, the feedback structure and the optimization process. We have focused the efforts to design the controller network architecture that is based on the common elements (ramp meter controllers), executing the same computational procedures and applying the control signals based on the same state-feedback structure. This meets a spirit of `plug and play' (PnP), and is beneficial for both, architecture assembling and component replacement (in case of failure). In order to define the functionality for each of this PnP controller, the analysis on both system controllability and conditions for optimality were carried on. The preliminary work let us to determine the what type of information and upon which communication topology it is required to be sent in order to solve the posed optimization problem. Firstly, the feedback controls for each of the controllers require state information of the section that is controllable for it. In general, each of the controllers demands the state for its closest surrounding sections (downstream and upstream). Secondly, each of the controllers communicates with its closest active neighbors to exchange the information of optimal solution, namely optimal boundary flow or optimal control. We also observed that in any system mode there might be only one inactive controller (the controller that does not have any controllable section) surrounded by two active ones, and thus the maximum required information comes from the two closest neighbors for each of the directions. We noticed also that inactive controllers may serve to convey the information for the active ones, so the communication can be based on a path (or linear) graph. Part of this research was realized in UC Berkeley during the visit of Dominik Pisarski in PATH laboratory.

Traffic control is based on models of traffic, usually the so-called CTM – Cell transmission Model. Some work in the team aims at developing different models, more suitable for control. One such model is based on cells of variable length, as an alternative way to describe the congestion position. This model, proposed in , has been refined in the master thesis of Giulio Bontadini, taking into account mass conservation laws.

Disabled people face the effort to turn the steering wheel while driving their vehicle. This study, funded by the VolHand project, focuses on the aspect of the assistance during driving maneuver at low speeds (for instance, parking). On common vehicles for healthy people, the system that improves the driver's steering feel in these situations is the power-steering stage, which is mounted at the basis of the steering column and is based on hydraulic technology; the new generation uses an electric motor instead of the hydraulic pump, with more advantages in terms of fuel consumption, better road-feel feedback to the driver and better return-to-center performances of the steering wheel. This work has developed a general methodology to adapt the current technology for disabled people, by introducing additional blocks that can be implemented via software without altering the hardware of the vehicle. In this way, it can be easily exported without additional costs in terms of design and technology for the industrial partner. The methodology has been studied theoretically, joining control aspects with bio-mechanical ones. Moreover, the theoretical study has been tested in laboratory on the hardware-in-the loop setup, using the experimental platform NeCSCar (see Section ). First, a real steering wheel has been linked to a real-time PC-unit and to an electrical motor. A graphical user interface has been implemented to facilitate the access to the software. Then, the last part of the study has been the experimental validation with a tele-operated real vehicle. The vehicle provided the measure of the friction torque to the PC-unit, simulating a real driving situation.

This work is described in and in the Ph.D. thesis .

For a given vehicle there are different ways to travel on a given distance in a given time, associated to different levels of energy consumption; therefore, it is always possible to find an energy-optimal trajectory. Advising the driver via a suitable interface can reduce the energy consumed during the travel, and thus improve the energy efficiency: this is the principle of eco-driving. In urban areas, the optimal trajectory of the vehicle depends on interactions with other vehicles, also on passive signs (panels, priorities, etc.) and active signs (traffic lights); in each case, constraints are imposed on the command (vehicle speed). From the infrastructure perspective, traffic control in urban areas consists in determining the state of traffic signals in order to solve an optimization problem, for example minimizing average travel time of vehicles in the road network. If all vehicles could communicate with one another and with the active infrastructure (traffic lights), we could imagine benefits for each of the two problems which can be considered as a whole: on the one hand, from the vehicles' point of view, more information is available that can be integrated into the online optimization problem; on the other hand, there are new measures and new commands available to control traffic. Indeed, the estimation of the traffic is no longer necessary, as the position and speed of approaching vehicles is known and shared. More importantly, the traffic manager can send instructions to the vehicles. The aim of the research is to evaluate the potential in terms of energy saving and traffic improvement made possible by communicating vehicles. This work is the topic of the Ph.D. thesis of Giovanni De Nunzio, a CIFRE thesis with IFPEN. The paper considers the scenario where vehicle and infrastructure (traffic lights) can communicate, and describes a suitable optimization algorithm that can be run on-board the vehicle so to optimize its energy consumption by avoiding stops and abrupt changes of speed at traffic lights, thanks to the information on upcoming traffic lights on the same road.

Accompanying PhD contract with IFPEN (IFP Energies Nouvelles), in the framework of the PhD grant of A. Ben Khaled. The thesis explores new architectures and flexible scheduling methods to enhance the trade-off between the integration accuracy and the simulation speed of distributed real-time (hardware-in-the-loop) simulators, in particular in the framework of automotive power-trains.

Accompanying PhD contract with IFPEN (IFP Energies Nouvelles), in the framework of the PhD grant of Giovanni de Nunzio. The thesis explores eco-driving for comunicating vehicles in urban environment.

VOLHAND (**VOL**ant pour personne âgée et/ou **HAND**icapée) is a project funded by the ANR (National Research agency). This project, started in October 2009, is a result of collaboration between C. Canudas de Wit and Franck Quaine/Violaine Cahouët (from the biomechanical team of Gipsa-Lab). The project has concerned the development of a new generation of Electrical power-assisted steering specifically designed for disabled and aged people. Our contribution has been to design new assisted laws, taking into account the specific mechanical characteristics of this particular population of drivers. The consortium was composed by: LAMIH, CHRU, Fondation Hopale, Gipsa-Lab, INRETS and JTEKT.
More information can be found on-line: http://

The MOCoPo project (Measuring and mOdelling traffic COngestion and
POllution) is funded by the French Ministry in charge of Transport
(MEDDTL), through the PREDIT (Research and Innovation in Land
Transport Program). The project began in January 2011 and will end up
in December 2013. Various research institutes and universities, some
teams of the MEDDTL and pollution measurements associations are
involved in the project: LICIT (Transport and Traffic Engineering
Laboratory, joint unit of IFSTTAR and ENTPE), LTE (Transports and
Environment Laboratory, IFSTTAR), LEPSIS (Laboratory for Road
Operations, Perception, Simulators and Simulations, IFSTTAR), IM
(Infrastructures and Mobility Department, IFSTTAR), MACS (Monitoring,
Assessment, Computational Sciences, IFSTTAR), Inria-NECS, Atmo Rhône
Alpes, DIR-CE (Center-East Direction of Roads), LRPC Angers (Regional
Laboratory of Angers), CERTU (Center for Cities and Urban
Transportation), and CEREA (Center of Teaching and Research in
Atmospheric Environment, laboratory Ecole des Ponts ParisTech / EDF
Research and Development). NeCS is particularly involved in tasks
devoted to travel-time estimation and prediction. For this purpose one
post-doc (Fabio Morbidi) has been hired. More information can
be found on-line: http://

META-TRAM is a PEPS-CNRS project funded for two years (2013-2015). It aims at studying tensor methods for analyzing traffic data. Indeed, for a better management of mobility in modern cities (avoid or better control episodes of congestion, accurately predict traffic trends, finely analyze urban and suburban trips via multimodal networks), it is necessary to develop appropriate analytic tools that integrate multimodality and heterogeneity of networks from inherently multidimensional measures. Three areas are studied: tensor modeling for estimating origin-destination matrices, dynamic clustering flow and synthesis of distributed algorithms adapted to large volume of data, diversity of sensors, and their spatial dispersion. This project involves also I3S Lab (Sophia Antipolis) and CRAN (Nancy).

**Inertial and magnetic data integration for human movements analysis**

The goal of this consortium is to work together on how to deal with inertial data in different or complementary fields. Orange Grenoble lab works on the analysis of inertial data. Orange sells some smart-phones equipped with inertial unit. The goal of Orange is to develop from these data some analysis bricks. The bricks are identified by: a) Monitoring of activity by identifying postures and deduce the activity by a correlation table, b) Prevention of falls by an analysis of walking monitoring, c) Monitoring of indoor and outdoor trajectory, d) Position of the sensor, and e) Identification of the dynamic parts of the signal. Orange offers to provide laboratories participating in the consortium: a) The database created through a 2012 IGS experiment where 7 peoples wore smart-phones for 3 months and the report of the experiment, b) The ability to store the data recorded by the consortium on a server in the capacity limit of the predefined server, c) The loan of smart-phones, and d) A schedule of specifications of a service activity monitoring of remote person. A consortium agreement has been signed by eight laboratories: INSA-INL, UJF-AGIM, UJF-GIPSA, CNRS-LAAS, CNRS-IRIT, Amines- école des mines de Douai, ISFTTAR, UTT et Orange Labs.

Type: COOPERATION

Objective: Engineering of Networked Monitoring and Control Systems

Instrument: Network of Excellence

Objective: Engineering of Networked Monitoring and Control systems

Duration: September 2010 - August 2014

Coordinator: CNRS (France)

Partners: Inria (France), ETH Zurich (Switzerland), TU Berlin (Germany), TU Delft (Netherlands) and many others

Inria contact: C. Canudas de Wit

Abstract: Hycon 2 aims at stimulating and establishing a long-term integration in the strategic field of control of complex, large-scale, and networked dynamical systems. It focuses in particular on the domains of ground and aerospace transportation, electrical power networks, process industries, and biological and medical systems.

See also: http://

Type: STREP

Objective: ICT-2013.4.2a – Scalable data analytics – Scalable Algorithms, software frameworks and viualisation

Duration: Feb. 2014 to Jan. 2017.

Coordinator: National Centre of Scientific Research `Demokritos' (Greece)

Partners: IBM Israel, ETH Zurich (CH), Technion (Israel), Univ. of Birmingham (UK), NeCS CNRS (France), FeedZai (Portugal)

Inria contact: C. Canudas de Wit

Abstract: SPEEDD will develop a prototype for robust forecasting and proactive event-driven decision-making, with on-the-fly processing of Big Data, and resilient to the inherent data uncertainties. NeCS will lead the intelligent traffic-management use and show case.

See also: http://

Carlos Canudas de Wit participates to the working group WG1 “Systems of Systems in transportation and logistics” of the support action CPSoS “Towards a European Roadmap on Research and Innovation in Engineering and Management of Cyber-physical Systems of Systems”, led by TU Dortmund (Germany).

NeCS has submitted a proposal for the construction of a new associate team: COMFORT, with partner UC Berkeley/PATH. The proposal has been accepted, and the associate team will be funded for the period 2014-2016.

H. Fourati has a collaboration with the Kazakhstan National Technical University (KazNTU). He currently co-advises (with Pr. Olga Shiryayeva in KazNTU) Zarina Samigulina, a PhD student in KazNTU.

TeMP (Tensor-based information Modelling and Processing) is a project funded in the framework of the French-Brazilian bilateral collaboration program (FUNCAP-Inria). It started from August 2011 and ended in December 2013. It was coordinated for the French part by A. Kibangou and aimed to study, analyze, propose and evaluate new models and techniques for digital communication systems using tensors and multilinear algebra tools, through in-depth theoretical analysis of mathematical models, optimization algorithms, and computational simulations. Distributed and collaborative algorithms have been devised for processing tensors involved in cooperative communications. In addition, new methods for processing very large tensors (big data issue) have been obtained. A special session has been organized in CAMSAP 2013 by A. de Almeida, the coordinator of the Brazilian side of the project.

Zarina Samigulina, PhD student, Kazakhstan National Technical University (KazNTU), one-month visit (mid-May to mid-July).

The following professors from UFC Brazil visited NeCS within the framework of the TeMP project: André L. F. de Almeida (Associate Professor) in February and November for one week each stay; Carlos Alexandre Rolim Fernandes (Associate Professor) in May for one week; Carlos Estevao Rolim Fernandes (Associate Professor) in May for three days.

prof. Antonella Ferrara, from Università di Pavia (Italy), has been visiting NeCS regularly, with multiple visit of a few days, for an active collaboration on the traffic application, within Hycon2 project.

Giulio Bontadini, Master student, Università di Pavia (Italy), from March to August, co-advised by C. Canudas de Wit and A. Ferrara, master thesis:
*Modeling and control of traffic systems*

Yvan Gaudfrin, Master student, University of Bristol (UK), from June to September, co-advised by F. Garin, R. Fabbiano and J. Dumon, master thesis: *Source seeking via Poisson integrals – Practical implementation of a source-localization set-up.*

D. Pisarski has been a visiting scholar at UC Berkeley, Mechanical Engineering Dept., for three months (Oct.-Dec.). His stay was supported by Inria `Programme Explorateur' and CMIRA `Explora Doc'.

A. Kibangou spent two weeks in UFC, Brazil, in October, within the framework of the TeMP project.

C. Canudas de Wit is President Elect of the European Control Association (EUCA) 2013-2015, and member of the Board of Governors of IEEE Control Systems Society.
He is General Chair of the forthcoming European Control Conference, Strasbourg, July 2014
(http://

H. Fourati has been a peer-reviewer for international journals (IEEE Sensors Journal, IEEE/ASME Transactions on Mechatronics, Journal of Mechanics Engineering and Automation).

F. Garin has been a peer-reviewer for international journals (IEEE Trans. Automatic Control, Automatica) and conferences (CDC 2013, ACC 2014, ECC 2014, ICCPS 2014) and for a chapter in a book in Springer LNCS. She is chair for student activities of the forthcoming European Control Conference ECC 2014.

A. Kibangou has been a peer-reviewer for international journals (Automatica, IEEE Trans. on Control Systems and Technology, IEEE Trans. on Control of Network Systems, Elsevier Signal Processing, IEEE Trans. on Signal Processing, IET Communications, Int. J. of Adaptive Control and Signal Processing, and System & Control Letters) and conferences (CDC 2013, ACC 2014, ECC 2014). Locally, he is the organizer of seminars for the Control Department of Gipsa-Lab.

Licence: H. Fourati, Informatique Industrielle, 100h, L1, IUT 1 (GEII2), University Joseph Fourier, France;

Licence: H. Fourati, Automatique, 24h, L2, IUT 1 (GEII2), University Joseph Fourier, France;

Licence: H. Fourati, Automatismes industriels et réseaux, 90h, L1 et L2, IUT1 (GEII2), University Joseph Fourier, France.

Licence: H. Fourati, Automatique, 45h, L3, UFR physique, University Joseph Fourier, France

Licence: F. Garin, Automatique, 26h, L2, IUT1 (GEII1), University Joseph Fourier, France.

Licence: A. Kibangou, Automatique,62h, L2, IUT1(GEII1), University Joseph Fourier, France.

Licence: A.Kibangou, Mathématiques, 14h, L2, IUT1 (GEII1) , University Joseph Fourier, France.

PhD: Valentina Ciarla, Commande d'un système de puissance électrique pour personne âgée et/ou handicapée, Grenoble University, Oct. 10th, 2013, co-advised by C. Canudas de Wit, F. Quaine, and V. Cahouet.

PhD in progress: Abir Ben Khaled, Distributed real time simulation of numerical models: application to powertrain, Grenoble INP, Jan. 2011 - Apr. 2014, co-advised by D. Simon and M. Ben Gaid (IFPEN).

PhD in progress: Luis R. Leon Ojeda, Modélisation macroscopique, estimation de la demande et prédiction du flux pour les systèmes de transport intelligents, Grenoble University, April 2011-March 2014, Co-advised by C. Canudas de Wit and A. Kibangou.

PhD in progress: Dominik Pisarski, Contrôle d'accès collaboratif : application à la Rocade Sud de Grenoble, Grenoble University, June 2011 - May 2014, Advised by C. Canudas de Wit.

PhD in progress: Ruggero Fabbiano, Distributed source seeking control, Grenoble University, Dec. 2011 - Nov. 2014, co-advised by C. Canudas de Wit and F. Garin.

PhD in progress: Thi-Minh Dung Tran, Consensus en temps fini et ses applications en estimation distribuée pour les systèmes de transport intelligents, Grenoble University, co-advised by A. Kibangou and C. Canudas de Wit, Jan. 2012 - Apr. 2015.

PhD in progress: Giovanni de Nunzio, Control of communicating vehicles in urban environnement, Grenoble University, co-advised by C. Canudas de Wit and P. Moulin (IFPEN), Sep. 2012 - Aug. 2015

PhD in progress: Aida Makni, Estimation multi-capteurs et commande temps-réel tolérante aux fautes d'un drone aérien, Grenoble INP, Oct. 2012 - Sep. 2015, co-advised by H. Fourati, A. Kibangou and C. Canudas de Wit.

C. Canudas de Wit was member of the AERES Evaluation committee for the RTRA STAE, May 2013.

F. Garin was a member of the Ph.D. defense committee of Weiguo Xia, Univ. of Groningen (The Netherlands), June 28th, 2013. Thesis: Distributed algorithms for interacting autonomous agents.

Carlos Canudas has given a plenary talk on `Forecasting and control of traffic systems: a network perspective' at the
International Workshop on Smart City, organized by IEEE Control Systems Society in Hangzhou, China, in August 2013,
http://