Implementation of CMOS fuzzy controllers as mixed-signal integratedcircuits

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 Sugeno's 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     

Self-checking current-mode analogue memory

A long-term analogue memory based on continuous-time, current-mode algorithmic A/D-D/A converters is presented. It achieves an efficient performance in terms of speed, resolution, power, and area, while incorporating a self-checking ability. Its operation is described and illustrated with experimental results of a CMOS 2.4 μm prototype with 7 bit resolution     

Mixed-signal design of a fully parallel fuzzy processor

The authors present a novel architecture for implementing general-purpose fuzzy chips which allows fully-parallel rule processing employing a reduced number of mixed-signal computing blocks and minimum-sized digital memories. The resulting fuzzy processor can interact directly with continuous sensors and actuators and the subsequent digital processing system     

Integrated circuit implementation of fuzzy controllers

This paper presents mixed-signal current-mode CMOS circuits to implement programmable fuzzy controllers that perform the singleton or zero-order Sugeno's method. Design equations to characterize these circuits are provided to explain the precision and speed that they offer. This analysis is illustrated with the experimental results of prototypes integrated in standard CMOS technologies. These tests show that an equivalent precision of 6 b is achieved. The connection of these blocks according to a proposed architecture allows fuzzy chips with low silicon area whose inference speed is in the range of 2 Mega FLIPS (fuzzy logic inferences per second),     

Extending the functionality of a flexible current-mode CMOS circuit

A current-mode cell based on the square-law characteristic of MOS transistors in saturation is revisited to extend its functionality. The original structure was reported to provide a squaring operation. However, by biasing the circuit differently the circuit can perform the inverse operation and operate as a geometric-mean/square-rooter. This is illustrated with experimental results     

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.     

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.