Field Programmable Gate Arrays and Reconfigurable Computing in Automatic Control

New combustion engine principles increase the demands on feedback combustion control, at the same time economical considerations currently enforce the usage of low-end control hardware limiting implementation possibilities. Significant development is simultaneously and continuously carried out within the field of Field Programmable Gate Arrays (FPGAs). In recent years FPGAs have developed, from being a device mainly used to implement grids of 'glue-logic' to something of a flexible 'dream device' in cost and performance sensitive applications. It is not solely the development of FPGA devices which has made the FPGA the promising implementation platform it is, development of software tool sets and design methodologies is as important as the device as such. This thesis describes the nature of FPGAs, how they work, which programming environments that are available and which design methodologies that can be used on different levels. Focus is set on implementing control and feedback control on FPGAs in general terms. There are a lot of practical considerations differing between the FPGA environment and the well-known micro-controller environment and those are discussed from the view of the literature available in the different areas. The potential application of FPGAs is described and illustrated with application examples found in the literature, both general applications and control applications are discussed. The intended application is control of internal combustion engines and one FPGA implementation of a modeling algorithm commonly used within automotive control is described and discussed. The intention is to illustrate the usefulness in automotive control applications. Finally a suggestion of a suitable FPGA based automotive-control development environment is treat

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Employment of Real-Time/FPGA Architectures for Test and Control of Automotive Engines

Nowadays the production of increasingly complex and electrified vehicles requires the implementation of new control and monitoring systems. This reason, together with the tendency of moving rapidly from the test bench to the vehicle, leads to a landscape that requires the development of embedded hardware and software to face the application effectively and efficiently. The development of application-based software on real-time/FPGA hardware could be a good answer for these challenges: FPGA grants parallel low-level and high-speed calculation/timing, while the Real-Time processor can handle high-level calculation layers, logging and communication functions with determinism. Thanks to the software flexibility and small dimensions, these architectures can find a perfect collocation as engine RCP (Rapid Control Prototyping) units and as smart data logger/analyser, both for test bench and on vehicle application. Efforts have been done for building a base architecture with common functionalities capable of easily hosting application-specific control code. Several case studies originating in this scenario will be shown; dedicated solutions for protype applications have been developed exploiting a real-time/FPGA architecture as ECU (Engine Control Unit) and custom RCP functionalities, such as water injection and testing hydraulic brake control

Hardware-in-the-Loop Validation of an FPGA-Based Real-Time Simulator for Power Electronics Applications

This paper presents the hardware-in-the-loop (HIL) validation of a proposed FPGA-based real-time simulator for power electronics applications. The proposed FPGA-based real-time simulation platform integrates the Modified Nodal Analysis (MNA) method, Fixed Admittance Matrix Nodal Method (FAMNM) and an optimization technique to assess the optimal value of the switches conductance in order to minimize the relevant errors. Moreover, the proposed platform includes an automatic procedure to translate the netlist user-defined circuit schemes to the relevant equations to be solved in the FPGA. The proposed simulator is validated first by comparing the FPGA- based simulation results with offline ones performed by EMTP- RV. Then, further validation is presented by means of a dedicated HIL experimental setup composed of a controller connected to an actual two-level, three-phase inverter and its corresponding FPGA real-time model

Optimization of DSSS Receivers Using Hardware-in-the-Loop Simulations

Over the years, there has been significant interest in defining a hardware abstraction layer to facilitate code reuse in software defined radio (SDR) applications. Designers are looking for a way to enable application software to specify a waveform, configure the platform, and control digital signal processing (DSP) functions in a hardware platform in a way that insulates it from the details of realization. This thesis presents a tool-based methodolgy for developing and optimizing a Direct Sequence Spread Spectrum (DSSS) transceiver deployed in custom hardware like Field Programmble Gate Arrays (FPGAs). The system model consists of a tranmitter which employs a quadrature phase shift keying (QPSK) modulation scheme, an additive white Gaussian noise (AWGN) channel, and a receiver whose main parts consist of an analog-to-digital converter (ADC), digital down converter (DDC), image rejection low-pass filter (LPF), carrier phase locked loop (PLL), tracking locked loop, down-sampler, spread spectrum correlators, and rectangular-to-polar converter. The design methodology is based on a new programming model for FPGAs developed in the industry by Xilinx Inc. The Xilinx System Generator for DSP software tool provides design portability and streamlines system development by enabling engineers to create and validate a system model in Xilinx FPGAs. By providing hierarchical modeling and automatic HDL code generation for programmable devices, designs can be easily verified through hardware-in-the-loop (HIL) simulations. HIL provides a significant increase in simulation speed which allows optimization of the receiver design with respect to the datapath size for different functional parts of the receiver. The parameterized datapath points used in the simulation are ADC resolution, DDC datapath size, LPF datapath size, correlator height, correlator datapath size, and rectangular-to-polar datapath size. These parameters are changed in the software enviornment and tested for bit error rate (BER) performance through real-time hardware simualtions. The final result presents a system design with minimum harware area occupancy relative to an acceptable BER degradation

Opérateurs et engins de calcul en virgule flottante et leur application à la simulation en temps réel sur FPGA

RÉSUMÉ La simulation en temps réel des réseaux électriques connaît un vif intérêt industriel, motivé par la réduction substantielle des coûts de développement qu'offre une telle approche de prototypage. Ainsi, la simulation en temps réel permet d'intégrer dans la boucle de la simulation du matériel au fur et à mesure sa conception, permettant du même coup d'en vérifier le bon fonctionnement dans des conditions réalistes. Néanmoins, la simulation en temps réel au moyen de CPU, telle qu'elle a été pensée depuis une quinzaine d'années, souffre de certaines limitations, notamment dans l'atteinte de pas de calcul de l'ordre de quelques micro-secondes, un requis important pour la simulation fidèle des transitoires rapides qu'exigent les convertisseurs de puissance modernes. Pour tenter d'apporter une réponse à ces difficultés, les industriels ont adopté les circuits FPGA pour la réalisation d'engins de calcul dédiés à la simulation rapide des réseaux électriques, ce qui a permis de franchir la barrière de la fréquence de commutation de 5 kHz qui était caractéristique de la simulation sur CPU. La simulation sur FPGA offre à ce titre différents avantages telle que la réduction de la latence de la boucle de simulation du matériel sous test, particulièrement du fait que le FPGA donne un accès direct aux senseurs et aux actuateurs du dispositif en cours de prototypage. Les paradigmes usuels du traitement de signal sur FPGA font qu'il est d'usage d'y opérer une arithmétique à virgule fixe. Ce format des nombres pénalise le temps de développement puisqu'il requiert du concepteur une évaluation complexe de la précision nécessaire pour représenter l'ensemble des variables du modèle mathématique. C'est pourquoi l'arithmétique à virgule flottante suscite un certain intérêt dans la simulation des réseaux sur FPGA. Cependant, les opérateurs en virgule flottante imposent de longues latences, particulièrement handicapantes dans la réalisation de lois d'intégration (trapézoïdale, Euler-arrière, etc.) pour lesquelles l'utilisation d'un accumulateur à un cycle est cruciale. En cela, la problématique de l'addition et de l'accumulation en virgule flottante forme le cœur de notre travail de recherche. Ce travail a permis l'élaboration des architectures d'accumulateurs, de multiplieurs accumulateurs (MAC) et d'opérateurs de produit scalaire (OPS) en virgule flottante, qui joueront un rôle déterminant dans la mise en œuvre de nos engins de calcul pour la simulation des réseaux électriques. Ainsi, le travail présenté dans cette thèse propose différentes contributions scientifiques au domaine de la simulation en temps réel sur FPGA. D'une part, il contribue à la formulation d'un algorithme de sommation qui est une généralisation de la technique d'auto-alignement, nantie ici d'une formulation et d'une réalisation matérielle simplifiées. Le travail établit les critères permettant de garantir la bonne exactitude des résultats, critères que nous avons établis par des démonstrations théoriques et empiriques. La thèse propose également une analyse exhaustive de l'utilisation du format redondant high radix carry-save (HRCS) dans l'addition de mantisses larges, format pour lequel deux nouveaux opérateurs arithmétiques sont proposés: un additionneur endomorphique ainsi qu'un convertisseur HRCS à conventionnel. Une fois l'addition en virgule flottante à un cycle réalisée, la thèse propose de concevoir sur FPGA des engins de calcul exploitant une architecture SIMD (single instruction, multiple data) et disposant de plusieurs MAC ou opérateurs de produit scalaire (OPS) en virgule flottante. Ces opérateurs présentent une latence très courte, permettant l'atteinte de pas de calcul de quelques centaines de nanosecondes dans la simulation de convertisseurs de puissance de moyenne complexité.----------ABSTRACT The real-time simulation of electrical networks gained a vivid industrial interest during recent years, motivated by the substantial development cost reduction that such a prototyping approach can offer. Real-time simulation allows the progressive inclusion of real hardware during its development, allowing its testing under realistic conditions. However, CPU-based simulations suffer from certain limitations such as the difficulty to reach time-steps of a few microsecond, an important challenge brought by modern power converters. Hence, industrial practitioners adopted the FPGA as a platform of choice for the implementation of calculation engines dedicated to the rapid real-time simulation of electrical networks. The reconfigurable technology broke the 5~kHz switching frequency barrier that is characteristic of CPU-based simulations. Moreover, FPGA-based real-time simulation offers many advantages, including the reduced latency of the simulation loop that is obtained thanks to a direct access to sensors and actuators. The fixed-point format is paradigmatic to FPGA-based digital signal processing. However, the format imposes a time penalty in the development process since the designer has to asses the required precision for all model variables. This fact brought an import research effort on the use of the floating-point format for the simulation of electrical networks. One of the main challenges in the use of the floating-point format are the long latencies required by the elementary arithmetic operators, particularly when an adder is used as an accumulator, an important building block for the implementation of integration rules such as the trapezoidal method. Hence, single-cycle floating-point accumulation forms the core of this research work. Our results help building such operators as accumulators, multiply-accumulators (MACs), and dot-product (DP) operators. These operators play a key role in the implementation of the proposed calculation engines. Therefore, this thesis contributes to the realm of FPGA-based real-time simulation in many ways. The research work proposes a new summation algorithm, which is a generalization of the so-called self-alignment technique. The new formulation is broader, simpler in its expression and hardware implementation. Our research helps formulating criteria to guarantee good accuracy, the criteria being established on a theoretical, as well as empirical basis. Moreover, the thesis offers a comprehensive analysis on the use of the redundant high radix carry-save (HRCS) format. The HRCS format is used to perform rapid additions of large mantissas. Two new HRCS operators are also proposed, namely an endomorphic adder and a HRCS to conventional converter. Once the mean to single-cycle accumulation is defined as a combination of the self-alignment technique and the HRCS format, the research focuses on the FPGA implementation of SIMD calculation engines using parallel floating-point MACs or DPs. The proposed operators are characterized by low latencies, allowing the engines to reach very low time-steps. The document finally discusses power electronic circuits modelling, and concludes with the presentation of a versatile calculation engine capable of simulating power converter with arbitrary topologies and up to 24 switches, while achieving time steps below 1 μs and allowing switching frequencies in the range of tens kilohertz

Overview of Real-Time Simulation as a Supporting Effort to Smart-Grid Attainment

abstract: The smart-grid approach undergoes many difficulties regarding the strategy that will enable its actual implementation. In this paper, an overview of real-time simulation technologies and their applicability to the smart-grid approach are presented as enabling steps toward the smart-grid’s actual implementation. The objective of this work is to contribute with an introductory text for interested readers of real-time systems in the context of modern electric needs and trends. In addition, a comprehensive review of current applications of real-time simulation in electric systems is provided, together with the basis to understand real-time simulation and the topologies and hardware used to implement it. Furthermore, an overview of the evolution of real-time simulators in the industrial and academic background and its current challenges are introduced.The final version of this article, as published in Energies, can be viewed online at: http://www.mdpi.com/1996-1073/10/6/81

Software and Hardware-In-The-Loop Modeling of an Audio Watermarking Algorithm

Due to the accelerated growth in digital music distribution, it becomes easy to modify, intercept, and distribute material illegally. To overcome the urgent need for copyright protection against piracy, several audio watermarking schemes have been proposed and implemented. These digital audio watermarking schemes have the purpose of embedding inaudible information within the host file to cover copyright and authentication issues. This thesis proposes an audio watermarking model using MATLAB® and Simulink® software for 1K and 2K fast Fourier transform (FFT) lengths. The watermark insertion process is performed in the frequency domain to guarantee the imperceptibility of the watermark to the human auditory system. Additionally, the proposed audio watermarking model was implemented in a Cyclone® II FPGA device from Altera® using the Altera® DSP Builder tool and MATLAB/Simulink® software. To evaluate the performance of the proposed audio watermarking scheme, effectiveness and fidelity performance tests were conducted for the proposed software and hardware-in-the-loop based audio watermarking model