Step variation studies of arm7 microcontroller based fuzzy logic

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME
405
STEP VARIATION STUDIES OF ARM7 MICROCONTROLLER
BASED FUZZY LOGIC CONTROLLER FOR WATER-IN-TANK
LEVEL CONTROL
L. Shrimanth Sudheer, P. Bhaskar and Parvathi C. S.
Department of Instrumentation Technology, Gulbarga University Post Graduate Centre,
RAICHUR –584133, Karnataka, INDIA,
ABSTRACT
Design and development of ARM7 microcontroller based fuzzy logic controller
(FLC) for water-in-tank level system is presented in this paper. A continuous tank of 100cm
X 20cm X 20cm dimension with one inlet and one outlet is considered. The outlet flow of
water is let open continuously to a reservoir and inlet flow is controlled by a pneumatic
actuated valve. The valve opening is controlled according to the water level in the tank in
order to make the deviation zero. The necessary hardware and software is developed
indigenously. FLC algorithm is written in embedded C in KEIL µV4 integrated development
environment. The main objective of the experimental work is to improve the performance of
the conventional PID controller by replacing it with modern FLC. The proposed controller is
subjected to both step and step-variation (stair-case) inputs. The performance is compared
with PIDC and necessary performance indices are found from the plots and tabulated. It is
observed that FLC outperforms the conventional PID in terms of quicker rise-time and better
tracking of the input.
Keywords: ARM7, Microcontroller, Water-Level, PIDC, FLC.
1. INTRODUCTION
The liquid level control systems play an important role in industries, for example, the
raw materials stock of the chemical works, the mixture raw materials process of the
lithification works, and the output products reaction of biochemical and petrochemical plants,
liquid level control of a steam generator in power plants, and pulp level control in paper and
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ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 2, March – April (2013), pp. 405-415
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406
pulp industry. Typically, many works using liquid tank level systems are using on-off loop
control scheme, which contain only the relay device and the limit switch. For precision
control, performance is limited by using this control; it is difficult to achieve accurate level
control for improving manufacturing quality of the products.
The recent development in the microcontroller, as single chip solution for many of
industrial control applications, has made the author to select and implement the control
algorithm on ARM7 microcontroller for liquid level control. The fuzzy logic control is
emerged over the years and become one of the most active and fruitful areas of research in
the intelligent control applications in industries as well as home appliances. Fuzzy logic
controller is a non linear controller and based on intuition and experience about the plant to
be controlled. Therefore it does not require the precise mathematical model of the plant.
Level of liquid being an important process parameter has to be maintained at the
desired level for smooth running of the process and for better quality products. There have
been many books/papers reported on the subject of controlling and monitoring liquid level in
different industrial processes [1-5]. Miao Wang and Francesco Crusca [6] designed and
implemented a gain scheduling controller for water level control in a tank. It was observed
that the system achieved a better performance over the conventional controller like P, PI, and
PID. Weidong Zhang, et al [7] proposed a new two-degree-of-freedom level control scheme
for processes with dead time. T. Heckenthaler and S. Engell [8] developed level controller for
a nonlinear two-tank system based on fuzzy control. Similarly, application of fuzzy logic for
water level control of small-scale hydro-generating units was reported by T. Niimura and R.
Yokoyama [9], for water level control of steam generator was reported by X. Liu, and T. Chai
[10], and a fuzzy sliding mode controller for two cascaded tanks level control was reported
by N. Waurajitti, et al [11]. The recent work by W. Chatrattanawuth, et al [12] reported a
level control system using a fuzzy I-PD controller. Their simulation results showed that the
proposed fuzzy I-PD controller performed better over the conventional. C. Li and J. Lian [13]
reported the application of genetic algorithm in PID parameter optimization for level control
system. They simulated the proposed strategy on MATLAB and later tested using LabVIEW.
Another LabVIEW based water level control is also reported by L. Gao and J. Lin [14]. A
DCS based water level control of boiler drum is reported by Y. Qiliang, et al [15]. A similar
work is also reported by H-M Chen, et al [16]. They designed a sliding mode controller for a
water tank liquid level control system.
In most of the research work reported earlier and recently, the studies have been either
simulation, based on MATLAB, or implementation using LabVIEW with PC as the platform.
The use of a single chip, advanced microcontroller with fuzzy logic control algorithms for
liquid level control is rather scarce and nowhere found in the papers reported. So this is a
novel approach and motivated us to employ an advanced, industry standard, ARM7
microcontroller based control system for liquid level control. The proposed work is not only
advanced, but achieves low cost and high efficiency in terms of size, speed, performance, and
development time.
2. WATER-IN-TANK SYSTEM
A continuous linear tank made of stain less steel with external dimensions of 100cm
X 20cm X 20cm is considered. The process tank has one inlet and one outlet. The outlet flow
of water is let open to a reservoir and inlet flow is controlled by a pneumatic actuated valve
(PCV). A differential pressure transducer, placed at the bottom of the process tank, senses the

407
liquid level in terms of pressure and converts into proportional voltage. The valve opening is
controlled according the water level in the process tank in order to maintain the measured
value as close to set point as possible i.e., to make the deviation zero. The water from
reservoir is pumped to process tank by means of a pump through a PCV which in turn
controlled by the fuzzy logic algorithm run by the microcontroller to maintain the water level
of tank at the set-point. The set-point of the tank can be changed by user through host
computer connected to ARM7 microcontroller. The water-in-tank system is shown in Fig. 1.
3. METHODOLOGY
The principle and block diagram of ARM7 microcontroller based water-in-tank level
control system is shown in Fig. 2. Level of water in process tank is measured in terms of
pressure-head developed in the capillary attached to the tank at the bottom. The other end of
the capillary is attached to a level sensor which is basically a differential pressure transducer
(DPT). As the water level increases in the tank, the pressure due to air trapped inside the
capillary also increases. Hence, the pressure, directly proportional to the water level, is
sensed and converted into equivalent voltage by the sensor. The microcontroller measures the
voltage proportional to level through the transducer, instrumentation amplifier (IN-AMP),
and on-chip analog to digital converter (ADC), converts into actual level in cm and displays it
on LCD. ARM7 based microcontroller LPC2129 from Philips is used. The inlet flow of water
Fig. 1. Water-in-tank system
DPT
Process Tank
Reservoir
Level
Water under
Process
Pump
HV2
HV1
PCV
Excitation
Actuator
1 mtr
0
LevelIndicator

408
from reservoir to the process tank through a pump is controlled by a pneumatic valve which
in turn controlled by the microcontroller through on-chip PWM unit, PWM to voltage
converter, voltage to current and a current to air (I/P) converter connected to PCV. The I/P
converter is supplied with a pressurized air (through air regulator) with the help of air
compressor.
The LPC2129 from Philips Semiconductor [17] consists of an ARM7TDMI-S CPU
with real-time emulation and 256KB of embedded high speed flash memory available in
compact 64 pin package. The ARM7TDMI-S is a general purpose 32-bit microprocessor,
which offers high performance and low power consumption. Its architecture is based on RISC
principle. It includes the following components: 16KB on-chip SRAM, 256KB Flash, 2-
channel CAN interface, 4-channel 10-bit ADC, 32-bit timers with PWM units and RTC, 46
GPIO ports, I2C bus interface, and on-chip crystal oscillator. The technical specifications of
equipment used in the experimental setup are given in Table 1.
Table 1: Technical specifications of experimental setup
Part Name Specifications
Process Tank
(Rectangular/cylindrical)
Material: Stainless Steel
Dimension: 100x20x20 cm
Reservoir Material: Stainless Steel
Volume: 100 Liters
Pump Centrifugal (Single-Phase, ¼ HP)
Control Valve Size ¼”, Pneumatic Actuated
Type: Air to open
Input: 3-15 psi
Air Regulator Input: 400 psi (max)
Output: 2-150 psi
I/P Converter Input: 4-20 mA
Output: 3-15 psi
Pressure Gauge Range: 0-30 psi, 0-100 psi
Compressor Output: 200 psi (max)
Differential Pressure
Transducer
Type: Diaphragm
Range:0-5 psi
Output: 3mV/V/psid
Fig. 2. Block diagram of proposed water-in-tank level control system
ARM7 Microcontroller
UART1
FLC
Algorith
On-chip
PWM1
Pump
Process
Tank
IN-AMP
On-chip
ADC
Reservoir
PCV
DPT
PWM to V
Converter
V to I
Converter
I to P
Converter
LCD
Liquid
Level

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3.1 Design of Fuzzy Logic Controller
A fuzzy logic controller (FLC) incorporates fuzzy logic for decision making, or rather
to produce control action, as required by the process or plant. FLCs are knowledge based
controllers consisting of linguistic “IF-THEN” rules that can be constructed using the
knowledge of experts in the given field of interest [18-19]. The fuzzy inference engine is the
heart of the FLC comprises both the knowledge base and decision-making logic. The
knowledge base consists of data base with necessary linguistic variables (rule set) and
decision-making logic used to decide what control action to be taken. The outputs of
inference engine, which are fuzzy linguistic terms, are converted into real (crisp) numbers in
the defuzzification stage [20-21].
A two input and one output fuzzy logic controller is designed as shown in the Fig. 3.
The error (e) and change-in-error (ce) are the two inputs, and control action (ca) is the
corresponding output of the FLC. A triangular membership function with nine members
(linguistic variables) termed as negative large (NL), negative medium (NM), negative small
(NS), negative zero (NZ), zero error (ZE), positive zero (PZ), positive small (PS), positive
medium (PM), and positive large (PL) are used to map the crisp input to universe of
discourse (-1 to +1). The universe of discourse is the range over which the fuzzy variables are
defined. The nine-member triangular functions of error, change-in error, and control action
are shown in Fig. 4. The control rules are constructed to achieve the best performance of
FLC. With nine members, we obtain 81 rules. The max-min method is used for inference
engine. The defuzzification is done using centre of gravity (COG) method.
The e input to the FLC is obtained by subtracting measured value/process variable (y)
from the reference (r), and the ce which is the difference between present and previous errors.
The output of the controller i.e., change in control action (ca) is applied to the process. The
reference input r, which is also the desired value, is entered by the operator in the beginning.
This is a closed loop control where the process variable is being continuously monitored to
maintain the error zero.
Fig. 3 Fuzzy logic control system
FLC
Fuzzifier
Inference
Engine
Rule Base
Defuzzifier
z-1
r +
+
-
-
e=r-y y
ce
ca
Level
Process

410
3.2 Software Details
The complete software for data acquisition, display, processing, and controlling the
liquid level is developed in embedded C language using KEIL’s µV4 Integrated
Development Environment. After compiling and debugging the code in PC, the output .hex
file is downloaded to LPC2129 microcontroller through serial port (COM1). The stored data
in on-chip flash of microcontroller is sent back to PC for storing, plotting and further
analysis. The flowchart of the complete program is shown in Fig. 5. The program consists of
five major subroutines of data acquisition (A/D conversion), LCD update, FLC algorithm,
PWM generation, and serial communication.
Fig. 4. Nine-member triangular functions of error (e),
change-in-error (ce), and control action (ca)
MembershipGradeµ(e)
NL
0.0
1.0
0.5
0.0 +1.0-1.0
error (e)
0.5-0.5
NM NS NZ ZE PZ PS PM PL
MembershipGradeµ(ce)
NL
0.0
1.0
0.5
0.0 +1.0-1.0
change-in-error (ce)
0.5-0.5
MembershipGradeµ(ca)
NL
0.0
1.0
0.5
0.0 +1.0-1.0
Control action (ca)
0.5-0.5

411
Fig. 5. The complete flowchart of level control program
Initialize hardware
(LCD, on-chip ADC, PWM1, and UART1)
Start
Declare & Initialize LCD, ADC, PWM, & FLC
variables, members, & functions
Send valve-open & motor-on commands
Call ADC and LCD subroutine to display
present level in cm
Read reference command (step/
staircase) from the user
Compute the error, change-in error and
apply to FLC algorithm
Scale FLC o/p & load in PWM1 register to
generate control action
Store and send the control action to PC
through UART1
Update FLC variables
Call ADC & LCD subroutine to measure
and display the initial level on LCD

412
4. RESULTS
The liquid level control system is subjected to step and step variation inputs. Before
subjecting the system to various test commands, the system initial conditions are met. Under
initial conditions, the process tank outlet is let open at a fixed (constant) flow rate. The supply
air to I/P converter is provided by an air regulator at a pressure of 30psi from a compressor.
For a step input, a desired level command of 15cm is applied. The corresponding
responses of the controllers are plotted as shown in Fig. 6. The rise time and settling time of
FLC are found to be respectively 52.77 sec and 65.56 sec as shown in Table 2. The steady
state error is found to be zero. FLC achieves superior performance when compare to the
traditional PIDC (best tuned with KP=390.0, KI=0.45, KD=0.1, & T=1).
In case of step variation studies, a stair-case command is applied to the controller. The
input command is varied for three values, in a step of 15cm from 0 to 15cm, 15 to 30cm, and
30 to 45cm. The corresponding output responses of both PIDC and FLC are plotted as shown
in Fig. 7. It is evident from the plot that FLC performs better than PIDC in tracking the step
variation of the input. PIDC performs sluggishly to the input. The standard performance
indices of PIDC and FLC at different liquid levels are tabulated in Table 3.
In both the cases fuzzy logic controller outperformed the PID controller. Hence, we
say that FLC is a robust controller when compare to PIDC and can be easily implemented
replacing the existing PIDC.
Table 2. Performance comparison for a step input of 15 cm
Performance Indices→
Controller Type↓
Rise
time (tr)
Settling
time (ts)
Steady state
error (ess)
Overshoo
t (MP)
PIDC 67.39 sec 120.50 sec 0.4 cm 4%
FLC 52.77 65.56 sec 0 0.6%
Table 3. Performance comparison for stair-case input
Liquid Level→
Performance Indices↓
15 cm 30 cm 45 cm
Rise time (sec) PIDC 67.39 67.60 68.00
FLC 52.77 53.10 51.80
Settling time (sec) PIDC 120.5 134.6 111.5
FLC 65.56 65.80 64.40
Overshoot (%) PIDC 4 0.6 0.6
FLC 0.6 0.6 0.6

413
Fig. 6. Step response of PIDC and FLC (from 0 to 15 cm)
0 50 100 150 200 250 300
1
3
6
9
12
15
Time in Sec
LevelinCm
FLC
PIDC
Fig. 7. Staircase response of PIDC and FLC (from 0 to 45cm
in a step of 15 cm)
0 100 200 300 400 500 600 700 800 900
0
5
10
15
20
25
30
35
40
45
50
Time in sec
Levelincm
REF FLC
PIDC

414
5. CONCLUSIONS
The real time implementation of fuzzy logic controller on an advanced ARM7 based
LPC2129 microcontroller for liquid level control is discussed. The performance comparison
between conventional PIDC and proposed FLC is made for a standard step, and step variation
inputs. It is observed that FLC performed better than the conventional PIDC. FLC was found
to be quick in reaching the set point, and settling, besides robust for step variations. So, it can
be concluded with the present work that FLC is superior over PIDC. Also, incorporation of
ARM7 microcontroller has increased the performance and greatly reduced the cost and space
in terms of few interfacing circuits.
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