Santiago Sánchez-Solano

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 and application of digital fuzzy controllers

This paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capability of these systems to be applied as function approximators.

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.

Hardware/software codesign of configurable fuzzy control systems

Abstract 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.

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.

XFVHDL: a tool for the synthesis of fuzzy logic controllers

A tool for the synthesis of fuzzy controllers is presented in this paper. This tool takes as input the behavioral specification of a controller and generates its VHDL description according to a target architecture. The VHDL code can be synthesized by means of two implementation methodologies, ASIC and FPGA. The main advantages of using this approach are rapid prototyping, and the use of well-known commercial design environments like Synopsys, Mentor Graphics, or Cadence.

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.

CAD Tools for Hardware Implementation of Embedded Fuzzy Systems on FPGAs

This paper describes two computer-aided design (CAD) tools for automatic synthesis of fuzzy logic-based inference systems. The tools share a common architecture for efficient hardware implementation of fuzzy modules, but are based on two different design strategies. One of them is focused on the generation of standard VHDL code, which can be later implemented on a reconfigurable device [field-programmable gate array (FPGA)] or as an application-specific integrated circuit (ASIC). The other one uses the Matlab/Simulink environment and tools for development of digital signal processing (DSP) systems on Xilinxs FPGAs. Both tools are included in the last version of Xfuzzy, which is a specific environment for designing complex fuzzy systems, and they provide interfaces to commercial VHDL synthesis and verification tools, as well as to conventional FPGA development environments. As demonstrated by the included design example, the proposed development strategies speed up the stages of description, synthesis, and functional verification of embedded fuzzy inference systems.