Development of FPGA based Standalone Tunable Fuzzy Logic Controllers

Soft computing techniques differ from conventional (hard) computing, in that unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind and its ability to address day-to-day problems. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Artificial Neural Networks (ANNs). This thesis presents a generic hardware architecture for type-I and type-II standalone tunable Fuzzy Logic Controllers (FLCs) in Field Programmable Gate Array (FPGA). The designed FLC system can be remotely configured or tuned according to expert operated knowledge and deployed in different applications to replace traditional Proportional Integral Derivative (PID) controllers. This re-configurability is added as a feature to existing FLCs in literature. The FLC parameters which are needed for tuning purpose are mainly input range, output range, number of inputs, number of outputs, the parameters of the membership functions like slope and center points, and an If-Else rule base for the fuzzy inference process. Online tuning enables users to change these FLC parameters in real-time and eliminate repeated hardware programming whenever there is a need to change. Realization of these systems in real-time is difficult as the computational complexity increases exponentially with an increase in the number of inputs. Hence, the challenge lies in reducing the rule base significantly such that the inference time and the throughput time is perceivable for real-time applications. To achieve these objectives, Modified Rule Active 2 Overlap Membership Function (MRA2-OMF), Modified Rule Active 3 Overlap Membership Function (MRA3-OMF), Modified Rule Active 4 Overlap Membership Function (MRA4-OMF), and Genetic Algorithm (GA) base rule optimization methods are proposed and implemented. These methods reduce the effective rules without compromising system accuracy and improve the cycle time in terms of Fuzzy Logic Inferences Per Second (FLIPS). In the proposed system architecture, the FLC is segmented into three independent modules, fuzzifier, inference engine with rule base, and defuzzifier. Fuzzy systems employ fuzzifier to convert the real world crisp input into the fuzzy output. In type 2 fuzzy systems there are two fuzzifications happen simultaneously from upper and lower membership functions (UMF and LMF) with subtractions and divisions. Non-restoring, very high radix, and newton raphson approximation are most widely used division algorithms in hardware implementations. However, these prevalent methods have a cost of more latency. In order to overcome this problem, a successive approximation division algorithm based type 2 fuzzifier is introduced. It has been observed that successive approximation based fuzzifier computation is faster than the other type 2 fuzzifier. A hardware-software co-design is established on Virtex 5 LX110T FPGA board. The MATLAB Graphical User Interface (GUI) acquires the fuzzy (type 1 or type 2) parameters from users and a Universal Asynchronous Receiver/Transmitter (UART) is dedicated to data communication between the hardware and the fuzzy toolbox. This GUI is provided to initiate control, input, rule transfer, and then to observe the crisp output on the computer. A proposed method which can support canonical fuzzy IF-THEN rules, which includes special cases of the fuzzy rule base is included in Digital Fuzzy Logic Controller (DFLC) architecture. For this purpose, a mealy state machine is incorporated into the design. The proposed FLCs are implemented on Xilinx Virtex-5 LX110T. DFLC peripheral integration with Micro-Blaze (MB) processor through Processor Logic Bus (PLB) is established for Intellectual Property (IP) core validation. The performance of the proposed systems are compared to Fuzzy Toolbox of MATLAB. Analysis of these designs is carried out by using Hardware-In-Loop (HIL) test to control various plant models in MATLAB/Simulink environments

Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance

Advanced control system for stand-alone diesel engine driven-permanent magnetic generator sets

The main focus is on the development of an advanced control system for variable speed standalone diesel engine driven generator systems. An extensive literature survey reviews the historical development and previous relevant research work in the fields of diesel engines, electrical machines, power electronic converters, power and electronic systems. Models are developed for each subsystem from mathematical derivations with necessary simplifications made to reduce complexity while retaining the required accuracy. Initially system performance is investigated using simulation models in Matlab/Simulink. The AC/DC/AC power electronic conversion system used employs a voltage controlled dc link. The ac voltage is maintained at constant magnitude and frequency by using a dc-dc converter and a fixed modulation ratio VSI PWM inverter. The DC chopper provides fast control of the output voltage by dealing efficiently with transient conditions. A Variable Speed Fuzzy Logic Core (VSFLC) controller is combined with a classical control method to produce a novel hybrid controller. This provides an innovative variable speed control that responds to both load and speed changes. A new power balance based control strategy is proposed and implemented in the speed controller. Subsequently a novel overall control strategy is proposed to co-ordinate the hybrid variable speed controller and chopper controller to provide overall control for both fast and slow variations of system operating conditions. The control system is developed and implemented in hardware using Xilinx Foundation Express. The VHDL code for the complete control system design is developed and the designs are synthesised and analysed within the Xilinx environment. The controllers are implemented with XC95108-PC84 and XC4010-PC84 to provide a compact and cheap control system. A prototype experimental system is described and test results are obtained that show the combined control strategy to be very effective. The research work makes contributions in the areas of automatic control systems for diesel engine generator sets and CPLD/FPGA application that will benefit manufacturers and consumers.EPSR

Development of controllers using FPGA for fuel cells in standalone and utility applications

In the recent years, increase in consumption of energy, instability of crude oil price and global climate change has forced researchers to focus more on renewable energy sources.Though there are different renewable energy sources available (such as photovoltaic and wind energy), they have some major limitations. The potential techniques which can provide renewable energy are fuel cell technology which is better than other renewable sources of energy. Solid oxide fuel cell (SOFC) is more efficient, environmental friendly renewable energy source. This dissertation focuses on load/grid connected fuel cell power system (FCPS) which can be used as a backup power source for household and commercial units. This backup power source will be efficient and will provide energy at an affordable per unit cost. Load/grid connected fuel cell power system mainly comprises of a fuel cell module, DCDC converter and DC-AC inverter. This thesis primarily focuses on solid oxide fuel cell (SOFC) modelling, digital control of DC-DC converter and DC-AC inverter. Extensive simulation results are validated by experimental results. Dynamic mathematical model of SOFC is developed to find out output voltage, efficiency, over potential loss and power density of fuel cell stack. The output voltage of fuel cell is fed to a DC-DC converter to step up the output voltage. Conventional Proportional-Integral (PI) controller and FPGA based PI controller is implemented and experimentally validated. The output voltage of DC-DC converter is fed to DC-AC inverter. Different pulse width modulation-voltage source inverter (PWM-VSI) control strategy (such as Hysteresis Current Controller (HCC), Adaptive-HCC, Fuzzy-HCC, Adaptive Fuzzy-HCC, Triangular Carrier Current Controller (TCCC) and Triangular Periodical Current Controller (TPCC)) for DC-AC inverter are investigated and validated through extensive simulations using MATLAB/SIMULINK. This work also focuses on number of fuel cells required for application in real time and remedy strategies when one or few fuel cells are malfunctioning. When the required numbers of fuel cells are not available, DC-DC converter is used to step up the output voltage of fuel cell. When there is a malfunction in fuel cell or shortage of hydrogen then a battery is used to provide backup power

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Methoden und Beschreibungssprachen zur Modellierung und Verifikation vonSchaltungen und Systemen: MBMV 2015 - Tagungsband, Chemnitz, 03. - 04. März 2015

Der Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2015) findet nun schon zum 18. mal statt. Ausrichter sind in diesem Jahr die Professur Schaltkreis- und Systementwurf der Technischen Universität Chemnitz und das Steinbeis-Forschungszentrum Systementwurf und Test. Der Workshop hat es sich zum Ziel gesetzt, neueste Trends, Ergebnisse und aktuelle Probleme auf dem Gebiet der Methoden zur Modellierung und Verifikation sowie der Beschreibungssprachen digitaler, analoger und Mixed-Signal-Schaltungen zu diskutieren. Er soll somit ein Forum zum Ideenaustausch sein. Weiterhin bietet der Workshop eine Plattform für den Austausch zwischen Forschung und Industrie sowie zur Pflege bestehender und zur Knüpfung neuer Kontakte. Jungen Wissenschaftlern erlaubt er, ihre Ideen und Ansätze einem breiten Publikum aus Wissenschaft und Wirtschaft zu präsentieren und im Rahmen der Veranstaltung auch fundiert zu diskutieren. Sein langjähriges Bestehen hat ihn zu einer festen Größe in vielen Veranstaltungskalendern gemacht. Traditionell sind auch die Treffen der ITGFachgruppen an den Workshop angegliedert. In diesem Jahr nutzen zwei im Rahmen der InnoProfile-Transfer-Initiative durch das Bundesministerium für Bildung und Forschung geförderte Projekte den Workshop, um in zwei eigenen Tracks ihre Forschungsergebnisse einem breiten Publikum zu präsentieren. Vertreter der Projekte Generische Plattform für Systemzuverlässigkeit und Verifikation (GPZV) und GINKO - Generische Infrastruktur zur nahtlosen energetischen Kopplung von Elektrofahrzeugen stellen Teile ihrer gegenwärtigen Arbeiten vor. Dies bereichert denWorkshop durch zusätzliche Themenschwerpunkte und bietet eine wertvolle Ergänzung zu den Beiträgen der Autoren. [... aus dem Vorwort

Rapidly-implementable optimizely-sizable fuzzy controller architectures: A performance analysis for semiconductor packaging two axes table

The tendency of miniaturizing semiconductor products towards nano-size transistor in integrated chips has motivated this work on the semiconductor package. Consequently, Four Fuzzy PID controller architectures based on type 2 FLC are developed; the Interval Type-2 Fuzzy Logic PID, IT2FLC PID MOALO-based, IT2FLC PI-PD, and IT2FLC PI-PD MOALO controllers. These architectures are improved to overcome the inherent nonlinearity in X-Y table models and capacitate the uncertainties of the parameters and the disturbances. Both controllers are designed to improve the desired position specification at minimum settling time (Ts), rise time (Tr), overshoot through minimization of oscillation and friction rejection during tracking the desired position trajectory. The ant lion optimization (ALO) algorithm has been efficiently solved optimization problems with minimum parameters and execution time. Hence, Multi-Objective Ant Lion Optimizer (MOALO) has been implemented to size the gains of the proposed controllers to get the desired position trajectory according to the required specification. A comparison with a related existing work shows minimal numerical values of improved transient specification response of Tr, Mp% and Ts for the MOALO- Based developed IT2 FLC PID and IT2 FLC PI-PD architectures. Observation of a higher Maximum Percentage of Enhancement settling time is noticed in both axes within the IT2FLC PI-PD architecture. Accordingly, transient performances of the four architectures have been significantly improved. The improvement is noticeable within the response of IT2FLC PI-PD architecture. The Maximum Percentage of Enhancement in the X-axis and Y-axis has been improved more than eight-fold and six-fold respectively using IT2FLC PI-PD architecture

Acta Universitatis Sapientiae - Electrical and Mechanical Engineering

Series Electrical and Mechanical Engineering publishes original papers and surveys in various fields of Electrical and Mechanical Engineering

Performance Analysis of Type-1 and Type-2 FLC based Shunt Active Filter Control Strategies for Power Quality Improvement

In recent years electrical power quality has been an important and growing problem because of the proliferation of nonlinear loads such as power electronic converters in typical power distribution systems. Particularly, voltage harmonics and power distribution equipment problems result from current harmonics produced by nonlinear loads. The electronic equipments like; computers, battery chargers, electronic ballasts, variable frequency drives, and switch mode power supplies, generate perilous harmonics and cause enormous economic loss every year. Problems caused by power quality have great adverse economic impact on the utilities and customers. Due to that both power suppliers and power consumers are concerned about the power quality problems and compensation techniques. Issue of harmonics are of a greater concern to engineers and building designers because harmonics can do more than distort voltage waveforms, they can overheat the building wiring, causes nuisance tripping, overheat transformer units, and cause random end-user equipment failures. Thus power quality (PQ) has become more and more serious with each passing day. As a result active power filter (APF) gains much more attention due to excellent harmonic and reactive power compensation in two-wire (single phase), three-wire (threephase without neutral), and four-wire (three-phase with neutral) ac power networks with nonlinear loads. The APF technology has been under research and development for providing compensation for harmonics, reactive power, and/or neutral current in ac networks. Current harmonics are one of the most common power quality problems and are usually resolved by the use of shunt active filters. In this research work, the performance of the shunt active filter control strategies has been evaluated in terms of harmonic mitigation and DC link voltage regulation. Three-phase reference current waveforms generated by proposed scheme are tracked by the three-phase voltage source converter in a hysteresis band control scheme. This research presents different topologies and controllers with enhanced performance of shunt active filter for power quality improvement by mitigating the harmonics and maintaining dc link voltage constant. For extracting the three-phase reference currents for shunt active power filters, we have developed shunt active filter Instantaneous active and reactive power “p-q” and Instantaneous active and reactive current “Id-Iq” control strategies. For regulating and maintaining the DC link capacitor voltage constant, the active power flowing into the active filter needs to be controlled. In order to maintain DC link voltage constant and to generate the compensating reference currents, we have developed PI Controller, Type-1 and Type-2 Fuzzy logic controller with different Fuzzy MFs (Trapezoidal, Triangular and Gaussian). The proposed APF is verified through MATLAB/SIMULINK. The detailed real-time results using Real-time digital simulator are presented to support the feasibility of proposed control strategy

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