Design and Development of Intelligent Navigation Control Systems for Autonomous Robots that Uses Neural Networks and Fuzzy Logic Techniques and Fpga For Its Implementation

This research compares the behavior of three robot navigation controllers namely: PID, Artificial Neural Networks (ANN), and Fuzzy Logic (FL), that are used to control the same autonomous mobile robot platform navigating a real unknown indoor environment that contains simple geometric-shaped static objects to reach a goal in an unspecified location. In particular, the study presents and compares the design, simulation, hardware implementation, and testing of these controllers. The first controller is a traditional linear PID controller, and the other two are intelligent non-linear controllers, one using Artificial Neural Networks and the other using Fuzzy Logic Techniques. Each controller is simulated first in MATLAB® using the Simulink Toolbox. Later the controllers are implemented using Quartus ll® software and finally the hardware design of each controller is implemented and downloaded to a Field-Programmable Gate Array (FPGA) card which is mounted onto the mobile robot platform. The response of each controller was tested in the same physical testing environment using a maze that the robot should navigate avoiding obstacles and reaching the desired goal. To evaluate the controllers\u27 behavior each trial run is graded with a standardized rubric based on the controllers\u27 ability to react to situations presented within the trial run. The results of both the MATLAB® simulation and FPGA implementation show the two intelligent controllers, ANN and FL, outperformed the PID controller. The ANN controller was marginally superior to the FL controller in overall navigation and intelligence

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

An end-user platform for FPGA-based design and rapid prototyping of feedforward artificial neural networks with on-chip backpropagation learning

The hardware implementation of an artificial neural network (ANN) using field-programmable gate arrays (FPGAs) is a research field that has attracted much interest and attention. With the developments made, the programmer is now forced to face various challenges, such as the need to master various complex hardware-software development platforms, hardware description languages, and advanced ANN knowledge. Moreover, such an implementation is very time consuming. To address these challenges, this paper presents a novel neural design methodology using a holistic modeling approach. Based on the end-user programming concept, the presented solution empowers end users by means of abstracting the low-level hardware functionalities, streamlining the FPGA design process and supporting rapid ANN prototyping. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. The recognition rate versus training data features and data representation was analyzed extensively

Dynamically reconfigurable bio-inspired hardware

During the last several years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bitstream, providing high architectural flexibility, while guaranteeing high performance. These configurability features have received special interest from computer architects: one can find several reconfigurable coprocessor architectures for cryptographic algorithms, image processing, automotive applications, and different general purpose functions. On the other hand we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse topics: evolvable hardware, neural hardware, cellular automata, and fuzzy hardware, among others. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. In general, bio-inspired hardware has been implemented on both custom and commercial hardware platforms. These custom platforms are specifically designed for supporting bio-inspired hardware systems, typically featuring special cellular architectures and enhanced reconfigurability capabilities; an example is their partial and dynamic reconfigurability. These aspects are very well appreciated for providing the performance and the high architectural flexibility required by bio-inspired systems. However, the availability and the very high costs of such custom devices make them only accessible to a very few research groups. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in its early stages and they are not well supported by FPGA vendors, thus making their use difficult to include in existing bio-inspired systems. In this thesis, I present a set of architectures, techniques, and methodologies for benefiting from the configurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures there are neural networks, spiking neuron models, fuzzy systems, cellular automata and random boolean networks. For these architectures, I propose several adaptation techniques for parametric and topological adaptation, such as hebbian learning, evolutionary and co-evolutionary algorithms, and particle swarm optimization. Finally, as case study I consider the implementation of bio-inspired hardware systems in two platforms: YaMoR (Yet another Modular Robot) and ROPES (Reconfigurable Object for Pervasive Systems); the development of both platforms having been co-supervised in the framework of this thesis

Design and Electronic Implementation of Machine Learning-based Advanced Driving Assistance Systems

200 p.Esta tesis tiene como objetivo contribuir al desarrollo y perfeccionamiento de sistemas avanzados a la conducción (ADAS). Para ello, basándose en bases de datos de conducción real, se exploran las posibilidades de personalización de los ADAS existentes mediante técnicas de machine learning, tales como las redes neuronales o los sistemas neuro-borrosos. Así, se obtienen parámetros característicos del estilo cada conductor que ayudan a llevar a cabo una personalización automatizada de los ADAS que equipe el vehículo, como puede ser el control de crucero adaptativo. Por otro lado, basándose en esos mismos parámetros de estilo de conducción, se proponen nuevos ADAS que asesoren a los conductores para modificar su estilo de conducción, con el objetivo de mejorar tanto el consumo de combustible y la emisión de gases de efecto invernadero, como el confort de marcha. Además, dado que esta personalización tiene como objetivo que los sistemas automatizados imiten en cierta manera, y siempre dentro de parámetros seguros, el estilo del conductor humano, se espera que contribuya a incrementar la aceptación de estos sistemas, animando a la utilización y, por tanto, contribuyendo positivamente a la mejora de la seguridad, de la eficiencia energética y del confort de marcha. Además, estos sistemas deben ejecutarse en una plataforma que sea apta para ser embarcada en el automóvil, y, por ello, se exploran las posibilidades de implementación HW/SW en dispositivos reconfigurables tipo FPGA. Así, se desarrollan soluciones HW/SW que implementan los ADAS propuestos en este trabajo con un alto grado de exactitud, rendimiento, y en tiempo real

High Performance Reconfigurable Fuzzy Logic Device for Medical Risk Evaluation

To date cardiovascular diseases (CVD) account for approximately 35% of all deaths worldwide. Many of these deaths are preventable if the risk of developing them can be accurately assessed early. Medical devices in use today cannot determine a patient's risk of developing a CVD condition. If accurate risk assessment was readily available to doctors, they can track rising trends in risk levels and recommend preventative measures for their patients. If patients had this risk assessment information before symptoms developed or life-threatening conditions occurred, they can contact their doctors to inquire about recommendations or seek help in emergency situations.This thesis research proposes the idea of using evolutionary programmed and tuned fuzzy logic controllers to diagnose a patient's risk of developing a CVD condition. The specific aim of this research seeks to advance the flexibility and functionality of fuzzy logic systems without sacrificing high speed and low resource utilization. The proposed system can be broken down into two layers. The bottom layer contains the controller that implements the fuzzy logic model and calculates the patient's risk of developing a CVD. The controller is designed in a context switchable hardware architecture the can be reconfigured to assess the risk of different CVD diseases. The top layer implements the evolutionary genetic algorithm in software, which configures the fuzzy parameters that optimize the behavior of the controller. The current implementation inputs patient's personal data such as electrocardiogram (ECG) wave features, age and body mass index (BMI) and outputs a risk percentage for Sinus Bradycardia (SB), a common cardiac arrhythmia. We validated this system via Matlab and Modelsim simulations and built the first prototype on a Xilinx Virtex-5 FPGA platform. Experimental results show that this 3-input-1-output fuzzy controller with 5 fuzzy sets per variable and 125 rule propositions produces results within an interval of approximately 1us while reducing hardware resource utilization by at least 25% when compared with existing designs

Intelligent tracking control of a DC motor driver using self-organizing TSK type fuzzy neural networks

[[abstract]]In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

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