Hardware/software codesign of configurable fuzzy control systems

Fuzzy inference techniques are an attractive and well-established approach for solving control problems. This is mainly due to their inherent ability to obtain robust, low-cost controllers from the intuitive (and usually ambiguous or incomplete) linguistic rules used by human operators when describing the control process. This paper focuses on the hardware/software codesign of configurable fuzzy control systems. Two prototype systems implemented on general-purpose development boards are presented. In both of them, hardware components are based on specific and configurable fuzzy inference architecture whereas software tasks are supported by a microcontroller. The first prototype uses an off-the-shelf microcontroller and a low-complexity Xilinx XC4005XL field programmable gate array (FPGA). The second one is implemented as a system on programmable chip (SoPC), integrating the microcontroller together with the fuzzy hardware architecture and its interface circuits into a Xilinx Spartan2E200 FPGA.Comisión Interministerial de Ciencia y Tecnología TIC2001-1726-C02-0

FPGA implementation of embedded fuzzy controllers for robotic applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/soffivare solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle

FPGA Implementation of Embedded Fuzzy Controllers for Robotic Applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/software solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle. © 2007 IEEE.Ministerio de Educación y Ciencia TEC2005-04359/MIC y DPI2005-02293Junta de Andalucía TIC2006-635 y TEP2006-37

A design environment for synthesis of embedded fuzzy controllers on FPGAs

This paper presents a design environment for the synthesis of embedded fuzzy controllers on FPGAs. It provides a novel implementation technique that allows accelerating the exploration of the design space of fuzzy control modules, as well as a codesign flow that eases their integration into complex control systems and the joint development of hardware and software components. The set of CAD tools supporting this environment includes specific fuzzy logic design tools provided by Xfuzzy, FPGA synthesis and implementation tools from Xilinx, and modeling and simulation facilities from Matlab. As demonstrated by the analyzed design examples, the described development strategy takes advantage of flexibility and ease of configuration offered by the different tools to dramatically speed up the stages of description, synthesis, and functional verification of embedded fuzzy control system

Intelligent Embedded Software: New Perspectives and Challenges

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality

Adapting an IP MC6805 core for multiprocessing and multitasking

The availability of high-density field configurable devices provides the opportunity for designing highly integrated solutions (SOPC: System On a Programmable Chip).\nAmong the SOPC solutions, a case is the integration of an embedded single processor equipped with a multitasking operating system. As an alternative to a single processor the embedding of various processors on a chip, even heterogeneous and with multitasking capacity, may be considered.\nA distinctive characteristic of a SOPC device is that the tasks to be performed are well known before the design starts. That feature is opposed to the traditional multiprocessing and multitasking systems in which general purpose applications are adopted during design. The benefit of this knowledge is that hardware as well as software can be adapted to fit the application’s requirements.\nThis paper presents the hardware modifications performed on an microcontroller embedded core, to allow its inclusion as a multitasking device in a “multiprocessor on a chip”, through the addition of a hardware task manager (scheduler) and communication channels among processors.La disponibilidad de dispositivos de Lógica Programable de alta densidad de integración permite buscar soluciones integradas en un dispositivo SOPC (System On a Programmable Chip).\nUn tema de creciente interés son los procesadores empotrados, siendo usual un único procesador y un sistema operativo con capacidad de multitarea.\nSin embargo, debe considerarse como alternativa insertar varios procesadores, no necesariamente idénticos, que pueden a su vez atender varias tareas. En un SOPC, como diferencia fundamental con los casos tradicionales de multiprocesamiento y multitarea, las tareas a realizar son conocidas antes de comenzar el diseño, por lo tanto hardware como software se pueden configurar a medida de la aplicación, combinando la velocidad propia del primero, con la versatilidad del segundo.\nEste artículo describe las modificaciones de hardware realizadas al núcleo IP (Intellectual Property) de un procesador, de modo de permitir la inclusión de un administrador de tareas por hardware y de canales de comunicación interprocesadores

Adapting an IP MC6805 core for multiprocessing and multitasking

La disponibilidad de dispositivos de Lógica Programable de alta densidad de integración permite buscar soluciones integradas en un dispositivo SOPC (System On a Programmable Chip). Un tema de creciente interés son los procesadores empotrados, siendo usual un único procesador y un sistema operativo con capacidad de multitarea. Sin embargo, debe considerarse como alternativa insertar varios procesadores, no necesariamente idénticos, que pueden a su vez atender varias tareas. En un SOPC, como diferencia fundamental con los casos tradicionales de multiprocesamiento y multitarea, las tareas a realizar son conocidas antes de comenzar el diseño, por lo tanto hardware como software se pueden configurar a medida de la aplicación, combinando la velocidad propia del primero, con la versatilidad del segundo. Este artículo describe las modificaciones de hardware realizadas al núcleo IP (Intellectual Property) de un procesador, de modo de permitir la inclusión de un administrador de tareas por hardware y de canales de comunicación interprocesadores.The availability of high-density field configurable devices provides the opportunity for designing highly integrated solutions (SOPC: System On a Programmable Chip). Among the SOPC solutions, a case is the integration of an embedded single processor equipped with a multitasking operating system. As an alternative to a single processor the embedding of various processors on a chip, even heterogeneous and with multitasking capacity, may be considered. A distinctive characteristic of a SOPC device is that the tasks to be performed are well known before the design starts. That feature is opposed to the traditional multiprocessing and multitasking systems in which general purpose applications are adopted during design. The benefit of this knowledge is that hardware as well as software can be adapted to fit the application’s requirements. This paper presents the hardware modifications performed on an microcontroller embedded core, to allow its inclusion as a multitasking device in a “multiprocessor on a chip”, through the addition of a hardware task manager (scheduler) and communication channels among processors.Facultad de Informátic

FPGAs in Industrial Control Applications

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

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

Adapting an IP MC6805 core for multiprocessing and multitasking

La disponibilidad de dispositivos de Lógica Programable de alta densidad de integración permite buscar soluciones integradas en un dispositivo SOPC (System On a Programmable Chip). Un tema de creciente interés son los procesadores empotrados, siendo usual un único procesador y un sistema operativo con capacidad de multitarea. Sin embargo, debe considerarse como alternativa insertar varios procesadores, no necesariamente idénticos, que pueden a su vez atender varias tareas. En un SOPC, como diferencia fundamental con los casos tradicionales de multiprocesamiento y multitarea, las tareas a realizar son conocidas antes de comenzar el diseño, por lo tanto hardware como software se pueden configurar a medida de la aplicación, combinando la velocidad propia del primero, con la versatilidad del segundo. Este artículo describe las modificaciones de hardware realizadas al núcleo IP (Intellectual Property) de un procesador, de modo de permitir la inclusión de un administrador de tareas por hardware y de canales de comunicación interprocesadores.The availability of high-density field configurable devices provides the opportunity for designing highly integrated solutions (SOPC: System On a Programmable Chip). Among the SOPC solutions, a case is the integration of an embedded single processor equipped with a multitasking operating system. As an alternative to a single processor the embedding of various processors on a chip, even heterogeneous and with multitasking capacity, may be considered. A distinctive characteristic of a SOPC device is that the tasks to be performed are well known before the design starts. That feature is opposed to the traditional multiprocessing and multitasking systems in which general purpose applications are adopted during design. The benefit of this knowledge is that hardware as well as software can be adapted to fit the application’s requirements. This paper presents the hardware modifications performed on an microcontroller embedded core, to allow its inclusion as a multitasking device in a “multiprocessor on a chip”, through the addition of a hardware task manager (scheduler) and communication channels among processors.Facultad de Informátic