1.5V fully programmable CMOS Membership Function Generator Circuit with proportional DC-voltage control

A Membership Function Generator Circuit (MFGC) with bias supply of 1.5 Volts and independent DC-voltage programmable functionalities is presented. The realization is based on a programmable differential current mirror and three compact voltage-to-current converters, allowing continuous and quasi-linear adjustment of the center position, height, width and slopes of the triangular/trapezoidal output waveforms. HSPICE simulation results of the proposed circuit using the parameters of a double-poly, three metal layers, 0.5 μm CMOS technology validate the functionality of the proposed architecture, which exhibits a maximum deviation of the linearity in the programmability of 7 %

Modeling and design of memristor-based fuzzy systems

The incessant down scaling of CMOS technology has been the main driving force for the semiconductor industry over the past decades. Yet, as process variations and leakage current continue to exhibit more pronounced effect with every technology node, this down scaling paradigm is expected to saturate in the few coming years. This prospect has led the research community to seek new technologies to surpass those challenges. Amongst the promising candidates is the memristor technology recently characterized by HP Labs. The miniaturized features and the peculiar behavior exhibited by the memsitor make it very well suited in some applications. For instance, memrsitors are used as memory cells in state-of-the-art memories known as Resistive RAMs in which the non-volatility of the memristor is exploited. The programmable nature of the memristor has made it a powerful candidate in neuromorphic and fuzzy systems that, in essence, go beyond the classical Von Neumann computing paradigm. In such systems, ideas from Artificial Intelligence, that for so long have been implemented on the software level, are implemented as electronic circuitry which renders benefits such as compact area and reduced power consumption. This work focuses on memrsitor-based Fuzzy applications. First, memristor-based Min-Max circuit used in the Fuzzy Inference engine is analyzed. It is proven that memrsitor-based Min-Max circuits can be extended to an arbitrary number of inputs ‘N’ under the proper design constraints. In addition, the effect of the memristor threshold is analyzed and a closed form expression is derived. It is shown that, for a given memristor with a specific OFF resistance and threshold current, there is a trade-off between the size and the resolution of the circuit. Then, a memrsitor-based Defuzzifier circuit is proposed. A major challenge in Defuzzifiers is their area occupancy due to the use of Multiplier and Divider circuits. In this design, the memrsitor analog programmability is leveraged to reduce the multiplication operation into simple Ohm’s Law which alleviates the need for dedicated hardware for multiplier circuit and, accordingly, reduces the area occupancy

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

A Novel Programmable CMOS Fuzzifiers Using Voltage-to-Current Converter Circuit

This paper presents a new voltage-input, current-output programmable membership function generator circuit (MFC) using CMOS technology. It employs a voltage-to-current converter to provide the required current bias for the membership function circuit. The proposed MFC has several advantageous features. This MFC can be reconfigured to perform triangular, trapezoidal, S-shape, Z-Shape, and Gaussian membership forms. This membership function can be programmed in terms of its width, slope, and its center locations in its universe of discourses. The easily adjustable characteristics of the proposed circuit and its accuracy make it suitable for embedded system and industrial control applications. The proposed MFC is designed using the spice software, and simulation results are obtained

FPGA design methodology for industrial control systems—a review

This paper reviews the state of the art of fieldprogrammable 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

On Design and Implementation of Generic Fuzzy Logic Controllers

Soft computing techniques, unlike traditional deterministic logic based computing techniques, sometimes also called as hard computing, are tolerant of imprecision, uncertainty, and approximation. The primary inspiration for soft computing is the human mind and its ability to address day-to-day problems. The primary constituents of soft computing techniques are Artificial Neural Network, Fuzzy Logic Systems, and Evolutionary Computing. This thesis presents design and implementation of a generic hardware architecture based Type-IMamdani fuzzy logic controller (FLC) implemented on a programmable device, which can be remotely configured in real-time over Ethernet. This reconfigurability is added as a feature to existing FLCs in literature. It enables users to change parameters (those drive the FLC systems) in real-time and eliminate repeated hardware programming whenever there is a need. Realization of these systems in real-time is difficult as the computational complexity increases exponentially with an increase in the number of inputs. Hence challenge lies in reducing the Rulebase significantly such that the inference time and the throughput time is perceivable for real-time applications. To achieve these objectives, a modified thresholded fired rules hypercube (MT-FRHC) algorithm for Rulebase reduction is proposed and implemented. MT-FRHC reduces the useful rules without compromising system accuracy and improves the cycle time in terms of fuzzy logic operations per second (FzLOPS). It is imperative to understand that there are over sixty reconfigurable parameters, and it becomes an arduous task for a user to manage them. Therefore, a genetic algorithm based parameter extraction technique is proposed. This will help to develop a course tuning and provide default parameters that can be later fine-tuned by the users remotely through the Web-based User Interface. A hardware software codesign architecture for FLC is developed on TI C6748 DSP hardware with Sys/BIOS RTOS and seamlessly integrated with a webbased user interface (WebUI) for reconfigurability. Fuzzy systems employ defuzzifier to convert the fuzzy output into the real world crisp output. Centroid of Area (CoA) method is most widely used defuzzification method for control applications. However, the prevalent method of CoA computation is based on the principle of Riemann sum which is computationally complex. A vertices based CoA (VBCoA) defuzzification method is introduced. It has been observed that the proposed VBCoA method for COA computation is faster than the Riemann sum based CoA computation. A code optimization technique, exclusive to TI DSPs, is implemented to achieve memory and machine cycle optimization. The WebUI is developed in accordance to a client–server model using ASP.NET. It acquires fuzzy parameters from users, and a server application is dedicated to handling data communication between the hardware and the server. Testing and analysis of this hardware G-FLCS has been carried out by using hardware-in-loop test to control various system models in Simulink environment which includes water level control in a two tank system, intelligent cruise control system, speed control of an armature controlled DC motor and anti-windup control. The performance of the proposed G-FLCS is compared to Fuzzy Inference System of Matlab Fuzzy Logic Toolbox and PID controller in terms of settling time, transient time and steady state error. This proposed MT-FRHC based G-FLCS with VBCoA defuzzification implemented on C6748 DSP was finally deployed to control the radial position of plasma in Aditya Tokamak fusion reactor. The proposed G-FLCS is observed to deliver a smooth and fast system response

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

Autonomous navigation using image processing

In the recent years, the pursuit intelligent and self-operated machines have increased. The human user is to be completely eliminated or minimized as much as possible. It is also popular now to observe machines that are able to do variety of tasks, instead of just one. A division of intelligent robotics is autonomous navigation. Google, Tesla, Honda, and many other large corporations are trying to master this field, since the search for a self-driving vehicle is profitable as much as it is difficult. The research presented in this thesis is autonomous navigation for mobile robots in an indoor environment, but not limited to. Some explored algorithms focus on navigating in environment that has been already explored and mapped. Algorithms such as modified A-Star and goal-based navigational vector field were tested for how effectively a path is planned from one point to another. The algorithms were compared and analyzed for how well the robot avoided obstacles and the length of the path taken. Other algorithms were also developed and tested for navigation without a map. The navigational algorithms are simulated on different artificial environments as well as on real environments. Machine learning is used to learn and adapt to the robot’s motion behaviours, which enables the robot to perform movements as intended by the implemented navigational algorithms. Referred to in this thesis as the intelligence engine, a feed-forward artificial neural network was created to predict power delivery to the motors. Back-propagation algorithm is used alongside the neural network to enable supervised learning. Similar to human vision, the algorithm relies mainly on image processing to obtain data about the surrounding environment. The data human eyes provide helps one perceive and understand the surroundings. Similarly, a Kinect sensor is used in this thesis to get 2-dimensional colour data as well as depth data. A program was implemented to process and understand this arbitrary sequential array of numbers in terms of quantifiable values. The robot in return is capable of understanding target, obstructions, and is capable of navigation. All external data are gathered from one optical sensor. Many different algorithms were implemented and tested to efficiently detect and track a target. The idea is to make an artificial robot perceive its’ surrounding using 3-Dimentional image data and intelligently navigate the local surroundings

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

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners