Laboratoire des systèmes et applications des technologies de l'information et de l'énergie

International audience

This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic.

This note investigates process fault accommodation in a class of nonlinear continuous-time systems. A new fault estimation module, based on an adaptive estimator, is first proposed. The fault tolerant controller is constructed to compensate for the effect of the faults by stabilizing the closed-loop system. A flexible joint robotic example is given to illustrate the efficiency of the proposed approach

The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

Recently, a new adaptive scheme [Conte (1995), Gini (1997)] has been introduced for covariance structure matrix estimation in the context of adaptive radar detection under non-Gaussian noise. This latter has been modeled by compound-Gaussian noise, which is the product <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> of the square root of a positive unknown variable tau (deterministic or random) and an independent Gaussian vector x, c=radictaux. Because of the implicit algebraic structure of the equation to solve, we called the corresponding solution, the fixed point (FP) estimate. When tau is assumed deterministic and unknown, the FP is the exact maximum-likelihood (ML) estimate of the noise covariance structure, while when tau is a positive random variable, the FP is an approximate maximum likelihood (AML). This estimate has been already used for its excellent statistical properties without proofs of its existence and uniqueness. The major contribution of this paper is to fill these gaps. Our derivation is based on some likelihood functions general properties like homogeneity and can be easily adapted to other recursive contexts. Moreover, the corresponding iterative algorithm used for the FP estimate practical determination is also analyzed and we show the convergence of this recursive scheme, ensured whatever the initialization.

In this paper, the suitability of a class of electric machines for vehicle traction applications is discussed. These machines, which are known as hybrid excitation synchronous machines, combine permanent-magnet (PM) excitation with wound field excitation. The goal behind the principle of hybrid excitation is to combine the advantages of PM excited machines and wound field synchronous machines. It is shown that these machines have good flux weakening capability compared with PM machines, and that they constitute an energy-efficient solution for vehicle propulsion.

The aim of this paper is to present the structure of a new flux switching synchronous machine with hybrid excitation. This machine uses the flux switching principle where all the active parts are located on the stator. The rotor is only a salient passive rotor and can be robust and made with a low cost technology. This new machine can be supplied with electricity by means of a traditional three phase voltage converter or can be associated with a diode rectifier. The hybrid excitation is an association of permanent magnets and a wound exciter.

The deployment of distributed energy resources, combined with a more proactive demand side management, is inducing a new paradigm in power system operation and electricity markets. Within a consumer-centric market framework, peer-to-peer (P2P) approaches have gained substantial interest. P2P markets rely on multibilateral negotiation among all agents to match supply and demand. These markets can yield a complete mapping of exchanges onto the grid; hence, allowing to rethink the sharing of costs related to the use of common infrastructure and services. We propose here to attribute such costs through exogenous network charges in several alternative ways, i.e., uniformly, based on the electrical distance between agents and by zones. This variety covers the main grid physical and regulatory configurations. Since attribution mechanisms are defined in an exogenous manner to affect each P2P trade, they eventually shift the market issue to cover the grid exploitation costs. It can even be used to release the stress on the grid when necessary. The interest of our approach is illustrated on a test case using the IEEE 39 bus test system, underlying the impact of attribution mechanisms on trades and grid usage.

International audience

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper addresses the problem of robust fault estimation and fault tolerant control (FTC) for Takagi–Sugeno (T–S) fuzzy systems. A fuzzy-augmented fault estimation observer (AFEO) design is proposed to achieve fault estimation of T–S models with actuator faults. Furthermore, based on the information of online fault estimation, an observer-based dynamic output feedback-fault tolerant controller (DOFFTC) is designed to compensate for the effect of faults by stabilizing the closed-loop system. Sufficient conditions for the existence of both AFEO and DOFFTC are given in terms of linear matrix inequalities. Simulation results of an inverted pendulum system are presented to illustrate the effectiveness of the proposed method. </para>

The aim of this paper is to present the interest of implementing digital controllers using field-programmable gate array (FPGA) components. To this purpose, a variety of current control techniques, which is applied to alternating current machine drives, is designed and implemented. They consist of on-off current controllers, proportional-integral current controller, and predictive current controller. The quality of the regulated current is significantly improved. It is mainly due to a very important reduction of the execution time delay. Indeed, in all described techniques, the execution time of the designed hardware architectures is only a few microseconds. This time reduction derives directly from the possibility offered by FPGAs to design very powerful dedicated architectures. Numerous experimental results are given in order to illustrate the efficiency of FPGA-based solutions to achieve high-performance control of electrical systems.

In this paper we derive alternating least squares algorithms for the computation of the block term decompositions introduced in Part II. We show that degeneracy can also occur for block term decompositions.

BACKGROUND: The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies. METHODS AND RESULTS: We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10(-19)) and identified novel loci on chr10q24 (P=1.6×10(-9)) and chr2p21 (P=4.4×10(-8)). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10(-8)) and chr2p16 (P=1.5×10(-8)). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16). CONCLUSIONS: We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.

This article presents the benefits of using field-programmable gate array (FPGA)-based controllers for power electronics and drive applications. For this purpose, an algorithm perspective is first proposed, where it is stated that, depending on the intrinsic parallelism properties as well as level of complexity, it makes sense to implement each control algorithm on a specific hardware and/or software architecture to get the best performances in terms of execution time or the best ratio performance versus cost. Then, an application perspective is proposed where the constraints specifically linked to the control of power converters are discussed.

This paper deals with covariance matrix estimates in impulsive noise environments. Physical models based on compound noise modeling [spherically invariant random vectors (SIRV), compound Gaussian processes] allow to correctly describe reality (e.g., range power variations or clutter transitions areas in radar problems). However, these models depend on several unknown parameters (covariance matrix, statistical distribution of the texture, disturbance parameters) that have to be estimated. Based on these noise models, this paper presents a complete analysis of the main covariance matrix estimates used in the literature. Four estimates are studied: the well-known sample covariance matrix M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCM</sub> and a normalized version M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sub> , the fixed-point (FP) estimate M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FP</sub> , and a theoretical benchmark M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TFP</sub> . Among these estimates, the only one of practical interest in impulsive noise is the FP. The three others, which could be used in a Gaussian context, are, in this paper, only of academic interest, i.e., for comparison with the FP. A statistical study of these estimates is performed through bias analysis, consistency, and asymptotic distribution. This study allows to compare the performance of the estimates and to establish simple relationships between them. Finally, theoretical results are emphasized by several simulations corresponding to real situations.

Today, electromagnetic compatibility (EMC) seems to be one of the major constraints of power electronic converters, particularly for variable-speed drives. Unfortunately, it is too often regarded as the last phase of the development of a converter since it represents the last step of its marketing. The estimation of conducted and radiated disturbances by simulation offers a considerable gain from the economic point of view. This paper shows how relatively simple models can be used to forecast EMC, taking into account various control strategies. These models are validated on an experimental setup and can be used during the design of a variable-speed inverter motor association. The objective is to approach by “fast” simulations the conducted emission to consider optimization processes. It is then imperative to take into account the environment of the converter, which implies the modeling of cables, motors, and, naturally, the filters.

This paper presents a new control strategy for high voltage direct current (HVDC) transmission based on the synchronverter concept: the sending-end rectifier controls emulate a synchronous motor (SM), and the receiving end inverter emulates a synchronous generator (SG). The two converters connected with a DC line provide what is called a synchronverter HVDC (SHVDC). The structure of the SHVDC is firstly analyzed. It is shown that the droop and voltage regulations included in the SHVDC structure are necessary and sufficient to well define the behavior of SHVDC. The standard parameters of the SG cannot be directly used for this structure. A specific tuning method of these parameters is proposed in order to satisfy the usual HVDC control requirements. The new tuning method is compared with the standard vector control in terms of local performances and fault critical clearing time (CCT) in the neighboring zone of the link. The test network is a 4-machine power system with parallel HVDC/AC transmission. The results indicate the contribution of the proposed controller to enhance the stability margin of the neighbor AC zone of the link.

Grid-connected converters (GcCs) employ LCL filters instead of simple L filters in order to meet new grid codes and their on-going changes in the near future. Active damping methods, without power losses, are preferred to passive ones for solving the resonance problems of LCL-filter-based GcCs ( LCL-GcCs). However, large changes in the grid inductance (typically under weak grid conditions and in rural areas) may compromise the system stability. Moreover, the delay of digital controllers will change system phase-frequency characteristics and consequently will affect the system stability. In view of this, this paper proposes a systematic design procedure for the active damping of voltage-oriented PI control for LCL- GcCs. The procedure considers active damping methods based on capacitor current feedback and is aimed to ensure stable operation under severe grid inductance variations while taking into account the influence of digital control delay and LCL filter parameters changes on the system stability. Simulation and experimental results are presented and discussed in order to validate the proposed design procedure.

Dark field resonant light scattering by gold and silver nanoparticles enables the detection and spectroscopy of such particles with high sensitivity, down to the single-particle level, and can be used to implement miniaturised optical detection schemes for chemical and biological analysis. Here, we present a straightforward optical spectroscopic methodology for the quantitative spectrometric study of resonant light scattering (RLS) by nanoparticles. RLS spectroscopy is complementary to UV-visible absorbance measurements, and we apply it to the characterisation and comparison of different types of gold, silver and gold-silver alloy nanoparticles. The potential of gold and silver particles as alternatives for fluorescent probes in certain applications is discussed. RLS spectroscopy is shown to be useful for studying analyte-induced gold nanoparticle assembly and nanoparticle chemistry, which can induce radical changes in the plasmonic resonances responsible for the strong light scattering. Furthermore, the feasibility of dark field RLS detection and quantitation of metal nanoparticles in microfluidic volumes is demonstrated, opening interesting possibilities for the further development of microfluidic detection schemes.