implementation of a new fpid controller
TRANSCRIPT
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.( [email protected]) .
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.
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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.