Abstract— In this paper, the implementation of a new FPID as a

single input single output (SISO) was presented. It was demonstrated

that the FPID design methodology resulted in better performance

than currently utilized conventional control methodology. The

simulation results also confirm that the Fuzzy Logic control can

control a real life system that contains perturbations from the

mathematical model. The controller of one input, three rules, and one

output simulated with MATLAB systematically, and the total

controller circuit simulated with HSPICE and the Layouts were

extracted with Magic.

Keywords— Fuzzy Logic Controller, PID, Soft Controlling,

SISO System.

I. INTRODUCTION

ONVENTIONAL FL control may result in steady state

errors if the system does not have an inherent integrating

property. To improve conventional FL control, some

algorithms have been proposed in the literature such as Fuzzy

PID (FPID) control. The PI type of FLC, which uses the same

inputs as the conventional FLC, is known to be more practical

and generates incremental control output via integral action at

the output. As the structural difference, the rule base of the PI-

FLC is different from that of the conventional FLC in order to

reduce overshoot and settling time. The PI type of FL control

is capable of reducing steady state errors; however, it is known

to give poor performance in transient responses. PID-FLC has

been developed to improve the transient response [5]

The conventional FL control is easy to perform in industry

due to its simple control structure, ease of design and

inexpensive cost [7]. However, FL control with fixed scaling

factors and fuzzy rules may not provide perfect control

performance if the controlled plant is highly nonlinear and

uncertain [1]. Adaptive FL control gives better performance

than FL control in many cases and applications [2]. There are

several adaptive fuzzy techniques for the controller: (i)

membership function tuning, (ii) input or output scaling factors

tuning and (iii) linguistic rule tuning [3]. Tuning of the scaling

factors has been recommended since gain coefficient tuning

appears to be more effective and simpler for implementation of

a control policy [4]. This technique leads to the development

of an adaptive fuzzy controller whose control action changes

Kabiraddin Asadian is with the Department of Physics, Mahabad Branch,

Islamic Azad University, Mahabad, Iran.

Muhammadamin Daneshwar, Sadeq Aminifar, and Ghader Yosefi is with

the Department of Electrical and Electronic Engineering, Mahabad Branch,

Islamic Azad University, Mahabad, Iran.( saminifar@yahoo.com) .

with respect to the plant operation.

Because of that fuzzy logic gives accuracy up in order to

achieve higher intelligence, they are not essentially very high

accurate systems. Typical fuzzy logic applications employ

universes of discourse with 32 points, and the resolution of the

interval signals is 3 or 4 bits (a resolution of 10 is said to be

enough). The current-mode circuits that we have used provide

5-bit resolution, input and output signals of system are also 5-

bit digital signals [5]. In this paper, we present a new FPID

with digital input and output signals with analog circuitry. Fig.

1 shows the Stepped Membership function diagram, which

used in our controller.

Fig. 1 Stepped Membership Function

There exist numerous examples of successful applications of

fuzzy logic in control, pattern recognition, and expert systems.

Fuzzy logic has been applied in more and more scientific

areas. The design of fuzzy systems is very much dependent on

the domain knowledge of experts. As fuzzy logic plays an ever

larger role in engineering, hardware implementations of fuzzy

systems become increasingly more important for developing

real time applications.

II. MAIN IDEA OF SYSTEM

Fig. 2 shows all blocks of the system with 2-input (each

input has 3 language terms) and one output with 3 singletons

(S, M, and L) using CGA for defuzzifying, Max circuit for

combining antecedents of each rule, and product-sum

inference method for performing system deduction. The

presented controller circuit is the result of a new method for

implementing digital input and digital output fuzzy logic-

proportional-integration-derivation controller (FPID), which

les. Adds all analog design advantages to digital input-output

(I/O) system. A digital system supports other digital

environments and digital signals are much robust against noise

and distortion. It is also easily practical to implement an

intelligence adaptive fuzzy system because of capability of

using RAM and ROMs [6].

Implementation of a new FPID controller

Kabiraddin Asadian, Sadeq Aminifar, Muhammadamin Daneshwar, Ghader Yosefi

C

Fig. 2 The Main Blocks of the System

As a drawback of digital realization, we can cite a great

circuit complexity, especially, when we are obliged to use

inference and defuzzification methods that are much more

accurate such as product–sum and center of area (COA) [7].

For having all digital advantages with least drawbacks, we

have taken inputs and outputs of the proposed FPID as digital

signals, while the internal blocks (fuzzifier, inference, and

defuzzifier) are realized by current-mode analog circuits [8].

Realizing internal blocks with analog circuits caused high

speed and low area and power consumption, especially, when

we are designing fully parallel and multi-valued systems like

fuzzy systems [9].

III. FUZIFFIER

The main idea of the fuzzifier circuit has been shown in the

circuit illustrated in Fig. 3. Crisp input signals and the chosen

parameters for performing favorable membership function, are

applied to 5-bit sub tractors and the output of this blocks

control some switches in multiplier blocks. Membership

functions generated by this membership function generator are

trapezoidal and triangular capable of changing their ascending

and descending slops arbitrarily, by controlling input digital

parameters.

IV. DIVIDER AND MULTIPLIER

It was specified the structure used for controllable multiplier

in Fig. 3. In this structure, the multiplication rate will be

performed by choosing appropriate current branches (with

controlling appropriate switches). For more precision, the

branch with dimension 8 is implemented as eight parallel

minimum size branches, respectively. The circuit multiplies

the input current by one up to fifteen. The structure used for

controllable Divider is specified in Fig. 2. Controlling Sdl to

Sd3 switches yields the current which is quotient of dividing

reference current over the number chosen by these switches.

Fig. 3 Fuzzifier Circuit

Fig. 4 Used Membership Functions

V. PID CIRCUIT

The circuit representing the controller based on two current

mirrors CDA is shown in Fig. 5. Compared to Fig. 1, the CDA

from Fig. 2 has to be considered as non-ideal, due to the

nonzero input impedances 1/gm and 1/gm of the current

inputs.

In this idealized schematic, we distinguish two mentioned

current mirrors, reference current source resistance RREF +

gm impedance Z1 containing R1, C1, gm, and impedance Z2

composed of R2, C2, and C3. The current sources are

designed in order to provide the zero output current IOUT in

the steady state (3).

Fig. 5 PID controller applied to FPID controller

VI. SIMULATIONS

The key issue in such control problems is to hold a variable

to a constant set point. As the design objective, the overshoot

in speed step response is desired to be not bigger than 5% for a

speed control problem.

The fuzzy control rule base inside the present controller

nominal value of the speed. The set of fuzzy rules has been

based on fast attaining of the desired speed and avoiding its

overshoots. Performance improvements for such a problem are

usually demonstrated by reductions in the amplitude of

undesirable oscillations in the controlled variable around the

set point.

Several simulations were performed to evaluate the

performance of the FPID controller. The Membership

functions applied the FPID controller are shown in Fig. 6 for a

step set point speed change (0–800 rpm), and the related

control signals (armature voltage) are illustrated in Fig. 7. A

smooth response was obtained without overshoot in the FPID

control. The conventional controllers (PD, PI, PID) did not

meet the needs of precise control, since the PD controller

resulted in a large steady state error and the PI and PID

controllers resulted in large magnitudes of overshoots and

settling times.

The FPID control signal is also smaller in magnitude at the

instant of speed increase and settles down in shorter time

compared with the PID, PI and PD controllers.

Fig. 6 Input Membership Functions

Fig7. Output Signal of FPID controller

.

VII. CONCLUSION

In this paper, the controller use digital circuitry in order to

increase analog fuzzy controller flexibility, while the proposed

method uses analog circuit realization in order to increase

inference speed and capability of employing more accurate

inference and defuzzifying methods with the least circuit

complexity. There is a comparison between proposed digital

FPID and previous recent works. Proposed controller because

of analog realization has provided low die area, low power

consumption and much higher inference speed. Moreover,

proposed controller is much more accurate than [10] because

of using COA and product-sum methods.

Finally, it should be said that evolutionary algorithms are

not the only way to adapt a fuzzy controller according to a

predefined merit figure. Nor even has there been a

demonstration to state they are the best approach for this

problem. In this work, it is shown that the FPID control

method could be an alternative method to conventional control

methods, since the computational task is not a problem

anymore because of high speed computers and application

tools to use in industrial applications.

ACKNOWLEDGMENT

The authors would like to thank Mahabad branch, Islamic

Azad University; for funding this research.

REFERENCES

[1] Huertas JL, Sanchez-Solano S, Barriga A, "hardware implementation

using AID VLSI techniques" In: Proceedings of the 8th Int. Confer. On

Fuzzy Logic and Neural Networks, Iizuka, Japan, July 17-22, 2004. p.

535-8.

[2] Walter G.Jung, IC Op-Amp Cookbook, third edition, SAMS

publications, pp. 7 – 17, (1999).

[3] Microchip Technology Incorporation reference books, PICmicro™ Mid-

Range MCU Family, Reference Manual, pp. (9-1)-(9-14). (2007)

[4] Microchip Technology Incorporation reference books, PIC12F683 Data

Sheet with Nano-Watt Technology), (2004)

[5] Christophe P. Basso, Switch Mode Power Supply Spice Cookbook, Mc

Graw Hill, pp. 118 –132. (2001)

[6] Bart Kosko, Fuzzy Thinking: The new Science of Fuzzy Logic,

University of Southern California, (1996)

[7] Alereza Naimi, FPIDts For mixed Analog – Digital closed loop circuitry,

master thesis, (2008)

[8] Temaitre L, Patyra MJ, Mlynek D. Analysis and design of CMOS fuzzy

logic controller in current-mode. IEEE J Solid State Circuits

1999;29(3):317-22.

[9] Yamakawa T. A fuzzy inference engine in nonlinear analog model and

its application to a fuzzy logic control. IEEE Trans Neural Networks

2003;4(3):496-522.

[10] Kawa T,Iki T, Uno F. The design and fabrication of the fuzzy logic

semi-custom IC in the standard CMOS IC technology. In: Proceedings

of the 15th IEEE International Symposium on Multiple- Valued Logic,

May 2006. p. 76-82.

BIOGRAPHIES

Kabiraddin Asadian – He received his M.S. in 2002 in Mathematics, Iran.

Sadeq Aminifar – (Oshnavieh, Iran, Born 1975) received his B.S. in 1999 in

Electrical Electronics Engineering from the Shahid Beheshti University,

Tehran, Iran and his M.S. in 2002 in Electronics Engineering from the Urmia

University, Urmia, Iran.

Muhammadamin Daneshwar – (Urmia, Iran, Born 1975) received his B.S.

in 1999 in Electrical Electronics Engineering from the Urmia University,

Urmia, Iran and his M.S. in 2006 in Electronics Engineering from the Islamic

Azad University, branch of sciences and researchs, Tehran, Iran.

Ghader Yosefi – (Piranshahr, Iran, Born 1979) received his B.S. in 2002 in

Electrical Electronics Engineering from the Urmia University, Urmia, Iran

and his M.S. in 2005 in Electronics Engineering from Urmia University,

Urmia, Iran.