HARDWARE-SOFTWARE CODESIGN OF FUZZY CONTROL

SYSTEMS USING FPGAS

E. del Toro

Centro de Investigaciones de Microelectrónica (CIME), CUJAE, Ciudad de La Habana, Cuba

S. Sánchez-Solano

Instituto de Microelectrónica de Sevilla (CNM-CSIC), Sevilla, Spain

M. Brox

Dpto. Arquitectura de Computadores, Electrónica y Tecnología Electrónica, Univ. Córdoba, Spain

A. J. Cabrera

Dpto. Automática y Computación, CUJAE, Ciudad de La Habana, Cuba

Keywords: Fuzzy Control, Hardware/Software Codesign, FPGA.

Abstract: This paper describes a hardware/software codesign strategy for fuzzy control systems implementation using

FPGAs. The main contribution of the paper consists of a methodology for joint development of hardware

and software components intended for rapid and verifiable design of a fuzzy control system. The design

flow combines specific tools for fuzzy inference systems included in the XFuzzy environment, simulation

and modelling tools from Matlab and FPGA synthesis, and implementation tools provided by Xilinx. The

advantages of this proposal are described in section 4 as it is used for the control system development of an

autonomous vehicle.

1 INTRODUCTION

Fuzzy logic provides a mathematical framework to

deal with the uncertainty and the imprecision typical

of the human reasoning system. One of its main

characteristics is the capability to describe the

behaviour of a complex system in a linguistic way

(Zadeh, 1973). Unlike classical logic systems, fuzzy

logic aims to model approximated reasoning modes

that play a significant role in the human ability to

make rational decision without using precise

mathematical models. These advantages have led to

an increase in the number of applications using

fuzzy logic controller (Ross, 2004).

A great number of different design proposals,

which range from software implementation to

complete hardware design, have been reported in the

last year (Baturone et al., 2000). The level of

complexity attained by many of the current

applications of control systems requires designing

the fuzzy inference modules (FIM) as components to

be included in a bigger system that, not only will be

able to apply the control responses, but also to

interface to other systems, reconfigure itself to

different states, and perform other tasks not related

to the fuzzy inference process. In these systems,

common tasks and configuration may be realized by

the software part using a general purpose processor,

while time consuming functions must be

implemented by means of specialized hardware

(Cabrera et al., 2004).

The progress in integrated circuits manufacturing

technologies allows the integration of complex

control systems on a single chip. Also, the resources

available in current FPGA families can be used to

implement a system on a programmable chip

(SoPC). However, to benefit from these

213

del Toro E., Sánchez-Solano S., Brox M. and J. Cabrera A. (2010).

HARDWARE-SOFTWARE CODESIGN OF FUZZY CONTROL SYSTEMS USING FPGAS.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 213-216

DOI: 10.5220/0002879602130216

 SciTePress

technological advances, new design methodologies

and powerful CAD tools must be developed to cut

down the development cycle of new products and

make them more competitive in market terms.

A fuzzy control system design methodology is

described in this paper. The codesign strategy and

the basic components of the control systems are

introduced in Section 2. In Section 3, the design

flow and the tools are described. An application of

the proposed methodology is explained in Section 4.

Finally, the main contributions and future goals are

resumed in Section 5.

CONTROL SYSTEMS CODESIGN

MODULES

The proposed HW/SW codesign methodology for

development of a fuzzy control system as a SoPC

combines the use of a general purpose processor,

available as IP-module for FPGA, connected to

specific fuzzy IP-modules that allow fuzzy inference

acceleration.

The processor used is MicroBlaze, which is a 32-

bit RISC processor soft core optimized for

implementation in Xilinx FPGAs. The system

architecture of MicroBlaze consists of several buses

that allows using multiple interfaces to connect

peripherals.

The main characteristics of the fuzzy module

used in this design are the efficient use of resources,

low power and high speed. In order to accomplish

these characteristics it is important to remark the use

of simplified defuzzification methods, the limited

overlap degree of input membership functions and

the implementation of a processing strategy that

evaluates only the active rules (Sánchez-Solano et

al., 2007).

3 DESIGN FLOW TOOLS

The proposed design flow combines the use of

specific tools for development of fuzzy systems

from the XFuzzy environment, modelling and

simulation using Matlab, and Xilinx EDK for

synthesis and implementation in FPGAs. According

to the proposed methodology, the development of a

fuzzy control system will be implemented at

different stages which are described in next sections.

3.1 FIMs Design using Xfuzzy

The Xfuzzy environment has been developed to ease

the design of fuzzy systems by starting from

linguistic and/or numerical knowledge to final

implementing them as hardware and/or software

components. It provides a wide set of new featured

tools which offer Graphical User Interfaces to ease

the design flow at the stages of description,

verification and synthesis. It can be also used for

extracting fuzzy rules from numerical data and

includes tuning and simplification facilities (López

et al., 1998).

The first stage of the aforementioned

methodology aims at functional description and

verification of the fuzzy inference modules. The

FIMs may be described in Xfuzzy using a

hierarchical architecture that combines fuzzy

modules (for implementation of fuzzy rules bases)

and crisp modules (to perform arithmetic and logic

functions).

Knowledge bases may be generated directly via

xfedit or using identification and supervised learning

tools, like xfdm and xfsl, with training data. xfplot

may be used for functional verification. In addition,

a closed loop simulation may be done with xfsim,

using the fuzzy module in connection with a high-

level model of the plant.

Once concluded the specification stage, a tool

named xfsg is used to perform hardware synthesis.

This tool generates the required files for the next

stage.

3.2 Synthesis and Verification using

SysGen

Using the SysGen library (Xilinx, 2008b),

XFuzzyLib is generated as a specific library that

provides basic modules for implementation of fuzzy

controllers. XFuzzyLib library contains basic

building blocks and other module descriptions

including different connectives and defuzzification

methods. See Figure 1 for description of an

inference module.

Automatic translation between the fuzzy

inference description and the Simulink module is

made using the files generated by the above

mentioned xfsg. These files are a Simulink module

describing the fuzzy system and a Matlab file that

contains the definition of size and functionality of

FIM components.

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Figure 1: Description of a fuzzy inference module in Xfuzzy (left) and associated control surface (right).

Figure 2: Left: Hardware/software cosimulation using the FPGA implementation of the fuzzy controller in a closed loop

with a plant model. Top right: Xfuzzy simulation results of parking maneuvers, Bottom right: Simulink simulation.

Design correctness can be verified at this stage

by means of the facilities provided by Matlab. The

use of a System Generator Block allows hardware

synthesis. Also, it is possible to perform functional

verification in closed loop through hardware-

software cosimulation as shown in Figure 2.

3.3 IP Module Construction

and Integration with MicroBlaze

SysGen has options to connect hardware design with

MicroBlaze implementation in a smooth way.

Basically this consists in defining input and output

registers so they can be addressed by MicroBlaze in

various forms, see (Xilinx, 2008a).

The import process in EDK adds interface (glue)

logic according to the selected BUS for connection

as well as basic drivers for software

communication.

3.4 Control System Implementation

MicroBlaze hardware synthesis connected with the

fuzzy controller and with other IP modules is

possible thanks to Xilinx XPS tool. According to the

design needs and constraints, the designer follows

basic steps in order to correctly implement the whole

system. Numerous options can be used, including

that where the system (MicroBlaze processor and

peripherals including the fuzzy controller) can be

taken again to Simulink in order to verify

correctness of implementation and perform a much

more complex simulation.

HARDWARE-SOFTWARE CODESIGN OF FUZZY CONTROL SYSTEMS USING FPGAS

215

4 CONTROL SYSTEM

DEVELOPMENT

OF AN AUTONOMOUS

VEHICLE

Parking of autonomous vehicles in a constrained

space is a typical control problem in robotics (Li et

al., 2003). Starting from any given position and

orientation (x, y, Φ), the autonomous mobile robot

must drive forward and backward (as required) at

speed v and with a wheel curvature γ in order to

always arrive backward at target position (0, 0, 0).

The above methodology has been applied to the

realization of a fuzzy control system for autonomous

parking of an electric vehicle. The used mobile robot

has been an autonomous electric vehicle called

Romeo-4R. Romeo-4R is a four-wheeled car with

standard Ackerman steering, DC traction and

steering electrical motors. A digital signal processor

(DSP) TMS-320LF provides support for motor

control (encoder inputs and PWM outputs), A/D

conversion, and communication links through serial

ports, thus easing the low level control of the

vehicle. The DSP acquires information from sensors

(a gyroscope and encoders) and processes it by using

a kinematical model usually employed for car-like

robots in order to resolve the actual position (x, y)

and orientation (Φ) (Cuesta et al., 2004).

The state of the vehicle is transmitted every 50

ms, thus determining the duration of the control

cycle. The fuzzy high-level controller performs the

parking control strategy and sends back to the DSP

the new required values of speed and wheel

curvature, so that the DSP controls the traction and

direction motors. This hierarchical control structure

allows developing different control strategies in the

high-level controller and frees it from the low-level

control task of Romeo-4R.

Once known the values of the current state (x, y,

Φ, v, γ) of Romeo-4R, the DSP transmits them to the

FPGA containing the fuzzy controller through a RS-

232 serial interface and using a specific

communication protocol (which is also implemented

by the program running in the MicroBlaze

processor).

In order to give physical support to the

development platform, a Xilinx University Program

Virtex2-Pro Development System Board has been

employed. This board allows cosimulation to be

carried out using Matlab.

Figure 2 (right) shows simulation results

illustrating the trajectories of parking maneuvers.

5 CONCLUSIONS

A realization strategy for the development of hybrid

HW/SW embedded fuzzy controllers on FPGA

devices has been described. The design flow

combines specific tools for development, simulation,

synthesis and implementation using FPGAs. The

main contribution of this paper is a methodology for

the joint construction of hardware and software

components in every stage of design. The proposed

methodology is applied to solve a classic robotic

problem.

ACKNOWLEDGEMENTS

Project TEC2008-04920 financed by “Ministerio de

Ciencia e Innovación” and P08-TIC-03674 by

“Junta de Andalucía”. E. del Toro is a MAEC-

AECID scholarship PhD. student.

REFERENCES

Baturone, I., Barriga, A., Sánchez-Solano, S., Jiménez,

C.J., and López, D., 2000. Microelectronic Design of

Fuzzy Logic-Based Systems. CRC Press.

Cabrera, A., Sánchez-Solano, S., Brox, P., Barriga, A., and

Senhadji, R.. 2004. Hardware/software codesign of

configurable fuzzy control systems. Applied Soft

Computing, 4, 271-285.

Cuesta, F., Gómez-Bravo, F., and Ollero, A., 2004.

Parking maneuvers of industrial-like electrical

vehicles with and without trailer, IEEE Trans. on

Industrial Electronics, 51, 2, 257-269.

Li, T. H. S., Shih-Jie, C., and Yi-Xiang, C. 2003.

Implementation of human-like driving skills by

autonomous fuzzy behavior control on an FPGA-

based car-like mobile robot. IEEE Trans. Ind.

Electron., 50, 867– 880.

López, D., Jiménez, C.J., Baturone, I., Barriga, A., and

Sánchez-Solano, S. 1998. Xfuzzy: A Design

Environment for Fuzzy Systems. IEEE International

Conference on Fuzzy Systems, Anchorage.

Ross,, T. J. 2004. Fuzzy Logic with Engineering

Applications, Wiley.

Sánchez-Solano, S., Cabrera, A., Baturone, I., Moreno-

Velo, F.J., and Brox, M., 2007. FPGA Implementation

of Embedded Fuzzy Controllers for Robotic

Applications. IEEE Trans. on Industrial Electronics,

54, 1937-1945.

Xilinx. 2008a. MicroBlaze Reference Guides.

Xilinx. 2008b. Xilinx System Generator v10.1 for

Simulink. User Guide.

Zadeh, L. A., 1973. 1973. Outline of a new approach to

the analysis of complex systems and decision

processes. IEEE Trans. on Systems, Man, and

Cybernetics, 3, 28-44.

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