I. Baturone

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.

Implementation of CMOS fuzzy controllers as mixed-signal integrated circuits

This paper discusses architectural and circuit-level aspects related to hardware realizations of fuzzy controllers. A brief overview on fuzzy inference methods is given focusing on chip implementation. The singleton or zero-order Sugenos method is chosen since it offers a good tradeoff between hardware simplicity and control efficiency. The CMOS microcontroller described herein processes information in the current-domain, but input-output signals are represented as voltage to ease communications with conventional control circuitry. Programming functionalities are added by combining analog and digital techniques, giving rise to a versatile microcontroller, capable of solving different control problems. After identifying the basic component blocks, the circuits used for their implementation are discussed and compared with other alternatives. This study is illustrated with the experimental results of prototypes integrated in different CMOS technologies.

Automatic design of fuzzy controllers for car-like autonomous robots

This paper describes the design and implementation of a fuzzy control system for a car-like autonomous vehicle. The problem addressed is the diagonal parking in a constrained space, a typical problem in motion control of nonholonomic robots. The architecture proposed for the fuzzy controller is a hierarchical scheme which combines seven modules working in series and in parallel. The rules of each module employ the adequate fuzzy operators for its task (making a decision or generating a smoothly varying control output), and they have been obtained from heuristic knowledge and numerical data (with geometric information) depending on the module requirements (some of them are constrained to provide paths of near-minimal lengths). The computer-aided design tools of the environment Xfuzzy 3.0 (developed by some of the authors) have been employed to automate the different design stages: 1) translation of heuristic knowledge into fuzzy rules; 2) extraction of fuzzy rules from numerical data and their tuning to give paths of near-minimal lengths; 3) offline verification of the control system behavior; and 4) its synthesis to be implemented in a true robot and be verified on line. Real experiments with the autonomous vehicle ROMEO 4R (designed and built at the Escuela Superior de Ingenieros, University of Seville, Seville, Spain) demonstrate the efficiency of the described controller and of the methodology followed in its design.

Design of Embedded DSP-Based Fuzzy Controllers for Autonomous Mobile Robots

Fuzzy controllers are used in many applications because of their rapid design by translating heuristic knowledge, robustness against perturbations, and smoothness in the control action. However, they require parallel processing and special operators (such as fuzzification or defuzzification) which are not available in standard digital signal processors (DSPs), thus complicating their direct implementation. This paper describes an efficient design methodology that allows starting with any kind of fuzzy controller and subsequently transforming it until a system suitable for easy DSP implementation is obtained. Such methodology is greatly aided by the design environment Xfuzzy 3. The parking problem of an autonomous robot is described to illustrate the steps of this methodology. Real experiments with the autonomous robot ROMEO 4R demonstrate efficiency of the designed fuzzy controller embedded into a stand-alone card based on a fixed-point DSP from Texas Instruments.

Integrated circuit implementation of fuzzy controllers

This paper describes a system architecture for implementing fuzzy systems. Programming functionalities are added by combining analog and digital techniques, giving rise to a versatile microcontroller. Novel circuits to implement the required building blocks are provided and illustrated with experimental results.

Xfuzzy: a design environment for fuzzy systems

Xfuzzy is a CAD tool that eases the development of fuzzy systems from their conception to their final implementation. It is composed of a set of modules and programs that share a common specification language and cover the different stages of the design process. Modules for describing, verifying and tuning the behavior of the system are integrated within the environment. In addition to these features, common to other fuzzy design tools, a relevant characteristic of Xfuzzy is that it includes several synthesis facilities for implementing the system on either software or hardware.

Using Xfuzzy Environment for the Whole Design of Fuzzy Systems

Since 1992, Xfuzzy environment has been improving to ease the design of fuzzy systems. The current version, Xfuzzy 3, which is entirely programmed in Java, includes a wide set of new featured tools that allow automating the whole design process of a fuzzy logic based system: from its description (in the XFL3 language) to its synthesis in C, C++ or Java (to be included in software projects) or in VHDL (for hardware projects). The new features of the current version have been exploited in different application areas such as autonomous robot navigation and image processing.

Model-Based Design Methodology for Rapid Development of Fuzzy Controllers on FPGAs

The complexity reached by current applications of industrial control systems has motivated the development of new computational paradigms, as well as the employment of hybrid implementation techniques that combine hardware and software components to fulfill system requirements. On the other hand, continuous improvements in field-programmable devices today make possible the implementation of complex control systems on reconfigurable hardware, although they are limited by the lack of specific design tools and methodologies to facilitate the development of new products. This paper describes a model-based design approach for the synthesis of embedded fuzzy controllers on field-programmable gate arrays (FPGAs). Its main contributions are the proposal of a novel implementation technique, which allows accelerating the exploration of the design space of fuzzy inference modules, and the use of a design flow that eases their integration into complex control systems and the joint development of hardware and software components. This design flow is supported by specific tools for fuzzy systems development and standard FPGA synthesis and implementation tools, which use the modeling and simulation facilities provided by the Matlab environment. The development of a complex control system for parking an autonomous vehicle demonstrates the capabilities of the proposed procedure to dramatically speed up the stages of description, synthesis, and functional verification of embedded fuzzy controllers for industrial applications.

A fuzzy controller using switched-capacitor techniques

The use of switched-capacitor (SC) techniques to build a fuzzy controller is discussed. This is carried out at two levels: architecture, and cell design. Using a sequential architecture, the required building blocks are introduced and its realization is described. An advantage of this approach is the compatibility with sound analog techniques that can help in the design of the defuzzifier. The proposed system can be considered as a starting point for exploring the capabilities offered by SC networks for the hardware implementation of future fuzzy systems.<<ETX>>

Tuning complex fuzzy systems by supervised learning algorithms

Tuning a fuzzy system to meet a given set of input/output patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0.