Arm7 microcontroller based fuzzy logic controller for liquid level control system

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN

0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME

217

ARM7 MICROCONTROLLER BASED FUZZY LOGIC CONTROLLER

FOR LIQUID LEVEL CONTROL SYSTEM

L. Shrimanth Sudheer, Immanuel J., P. Bhaskar, and Parvathi C. S.

Department of Instrumentation Technology,

Gulbarga University Post Graduate Centre,

RAICHUR –584133, Karnataka, INDIA,

ABSTRACT

Design and construction of a microcontroller based liquid level control system is

presented in this paper. ARM7 (Philips LPC2129) microcontroller based system for the real

time liquid level control is developed using the fuzzy logic controller (FLC). This controller

has been applied to the water-in-tank level control of a continuous process. The controller is

implemented in embedded C language to control the liquid level to the desired value. The

performance of the proposed controller is compared with conventional PID controller. An

accuracy of ±.1% is achieved in the control of liquid level over the range of 0 to 100cm. It is

observed that the proposed scheme controls the tank level effectively not only in the steady

state but also in the transient state.

Keywords: ARM7, FLC, Liquid Level, Microcontroller.

1. INTRODUCTION

The nonlinear systems are frequently encountered in the process industries. 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 papers

reported on the subject of controlling and monitoring liquid level in different industrial

processes. M. Wang and F. Crusca [1] 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 controllers like P, PI, and PID. W. Zhang et al [2]

proposed a new two-degree-of-freedom level control scheme for processes with dead time T.

Heckenthaler and S. Engell [3] developed level controller for a nonlinear two-tank system

based on fuzzy control. Similarly, application of fuzzy logic for water level control of small-

INTERNATIONAL JOURNAL OF ELECTRONICS AND

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ISSN 0976 – 6464(Print)

ISSN 0976 – 6472(Online)

Volume 4, Issue 2, March – April, 2013, pp. 217-224

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scale hydro-generating units was reported by T. Niimura and R. Yokoyama [4]. The recent work

by W. Chatrattanawuth et al [5] reported a level control system using a fuzzy I-PD controller.

Their simulation results shown that fuzzy I-PD controller performed better over conventional

controller. C. Li and J. Lian [6] 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 [7]. The DCS based water level control of boiler drum is reported by Y. Qiliang et

al [8]. A similar work is also reported by H-M Chen et al [9]. They designed a sliding mode

controller for a water tank liquid level control system.

Few authors reported various schemes and their implementation on different platforms

such as PC/uP/DSP. Some of the reports were also based on simulation. But an attempt is made

here to implement a fuzzy logic control algorithm on a microcontroller for real time level control

of a water-in-tank system. This approach will reduce the cost and space of the system. We will

address this issue by employing an advanced ARM7TDMI (PHILIPS LPC2129) processor.

2. DESIGN OF FUZZY LOGIC CONTROLLER

As the name itself suggests, a fuzzy logic controller incorporates fuzzy logic for decision

making or rather to produce control action as required by the plant or process [10]. 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. A two input and one output fuzzy

logic controller is designed as shown in the Fig. 1. 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 seven members (linguistic variables) termed as negative large (NL),

negative medium (NM), negative small (NS), zero error (ZE), 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

control rules are constructed to achieve the best performance of the FLC. With seven members,

we obtain 49 rules. Mamdani inference engine is used [11].

The e input to the controller is obtained by subtracting measured value/process variable

(y) from the reference (r), and the ce is difference between present and previous errors. The

output of the controller i.e., change in control action (ca) is applied to the process. The 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 continuously monitored to maintain the error to zero.

Fig 1: Fuzzy logic control system

FLC

Fuzz

ifie

r

Inference

Engine

Rule Base

Def

uzz

ifie

r

z-1

r +

+

-

-

e=r-y y

ce

ca Process/

Plant



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3. ARM7 MICROCONTROLLER BASED LEVEL CONTROL SYSTEM

The block diagram of proposed fuzzy logic controller scheme for liquid level control

system is illustrated in Fig. 2. A cylindrical tank of 100cmX20cmX20cm dimension is

considered. Level of the liquid (water) contained in a tank is measured and controlled at the

desired value. The level is measured in terms of pressure developed in the capillary attached

to the tank at the bottom. As the liquid level in tank increases the pressure developed inside

the capillary also increases. Hence, the pressure, directly proportional to the liquid level, is

sensed and converted into equivalent voltage by the integrated circuit differential pressure

transducer (DPT) placed on the top of the tank. The microcontroller measures the liquid level

through this sensor, signal conditioner, and on-chip analog to digital converter (ADC) and

displays it on LCD in terms of cm. The inlet flow of water from a pump (motor) to the tank is

controlled by a pneumatic control valve (PCV) which in turn controlled by the

microcontroller through on-chip PWM unit, PWM to voltage converter, V/I converter, and

current to pressure converter (IPC). The PWM technique is employed to precisely move the

pneumatic valve.

4. HARDWARE DETAILS

The actual hardware used to study the proposed control system is discussed here. The

hardware consists of process-tank, reservoir tank, pump, level sensor, pneumatic actuator,

compressor, input and output signal conditioning circuits, ARM7 microcontroller, and LCD.

The photograph of complete hardware is shown in Fig. 3.

4.1 ARM7 Microcontroller

The LPC2129 from Philips Semiconductor [12] 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; 16KB on-chip SRAM, 256KB Flash, 4-channel 10-bit ADC, 32-bit

timers with PWM units and RTC, 46 GPIO ports, I2C bus interface, and on-chip crystal

oscillator. This microcontroller is best suited for designing single-chip instruments.

Fig 2: Block diagram of microcontroller based FLC for liquid level control system

Fuzzy

Logic

Controller

PWM

Unit Output Signal

Conditioner PCV

Pump

Process

Tank

DPT A/D

Converter

Measured

Value

e Controlled

Value

Desired

Value

ARM7 Microcontroller

Input Signal

Conditioner

Reservoir

+ -



220

4.2 Level Sensor

The level sensor SX05DN from SenSym [13] is used. It is basically an integrated

circuit differential pressure transducer (DPT) consists of four strain gauges connected in

Wheatstone bridge and are pasted on a diaphragm. The bridge is excited with a stable +5V

DC. The sensor is provided with two input ports for applying either single ended or

differential pressure. In this application, one port is closed, and another is connected to the

bottom of the tank for single ended measurement. The input change in pressure, exerted on

the diaphragm, is converted into corresponding change in resistance which is further

converted to change in voltage.

4.3 Excitation Source In order to convert the change in resistance of the sensor to the corresponding change

in voltage, a precise and constant excitation voltage of +5V is generated using LM329,

LM308, and 2N2222 as shown in Fig. 4. LM329, a precision voltage source, produces 6.9V

which is dropped down to +5V and connected to non-inverting terminal of op-amp LM308.

An op-amp with npn-transistor 2N2222 at the output provides the enough current to the

bridge. With a +5V excitation voltage, the sensor will produce an output of 1.5mV/cm. An

offset-nullify circuit, using a potentiometer, is connected to bridge output to nullify the offset

and make zero adjust in initial condition.

4.4 Instrumentation Amplifier Sensor produces a small differential output voltage of 1.5mV/cm liquid height. So an

instrumentation amplifier, AD620 [14] from Analog Devices, is used to pick, amplify, and

convert it to single ended voltage compatible to be sampled by the on-chip ADC of LPC2129

microcontroller. A gain of 10 is set for the instrumentation amplifier to get 15mV/cm which

is more than the resolution of ADC.

Fig 3: Photograph of level process

Process Tank

Reservoir

PCV

Level Sensor

Regulator

IPC

Pump



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4.5 Analog to Digital Converter

The output of instrumentation amplifier is acquired by on-chip ADC and converted

into 10-bit binary word under program control. The resolution of ADC is 2.5mV at Vref

=2.56V and conversion time is 2.44 µSec.

4.6 LCD LCD provides better readability, reduced power consumption, and backlight during

low light vision. A 16x2 line LCD [15] is used to display the measured level. It is interfaced

to the microcontroller in nibble-mode with upper 4-bits (D3-D7) on the LCD to transfer the

data with MSB first and LSB next mode. The data lines, D3 to D7, are connected to P0.4 to

P0.7, and control lines RS (register select), and E (enable) are connected to P0.2 and P0.10

ports of microcontroller respectively as shown in Fig. 4.

5. SOFTWARE DETAILS

The complete algorithm for data acquisition, measurement, display, and control of

liquid level is developed in embedded C under KEIL’s integrated development environment

(µVision 4.0). The flowchart of the complete routine is shown in Fig. 5. All the variables of

controller and on-chip peripherals are initialized in the beginning. A serial program is also

developed to transfer the data to PC through UART1 for further analysis of the data.

Fig 4: Circuit schematic of the complete system



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6. RESULTS

The real time implementation of FLC for liquid level control is tested for standard

step input of 15 cm. A step input from initial value of 0 cm is applied to the controller. The

performance of FLC is compared with the conventional PID controller for the same step of

15 cm. The plots in Fig. 6 show step input response of FLC and PIDC. It is evident from the

plot that FLC performs superior over PIDC in terms of sharp rise time, and quick settling

time. The comparison of both the controllers is made and the corresponding performance

indices are tabulated in Table 1.

Fig 5: Flowchart of level control system

Initialize hardware

(LCD, on-chip ADC, PWM, and

Start

Declare & Initialize LCD, ADC, PWM, & FLC subroutines, local variables &

Send valve-open & motor-on

commands and display the initial level on LCD

Call ADC and LCD subroutine

to display current level in cm

Read set point level and display

it on LCD

Find the error and substitute it

in FLC algorithm

Scale FLC output & load in

PWM register to generate control

Store and send the control action to PC through UART1

Update FLC variables

Fig 6: Step input response for 15

0 50 100 150 200 250 300

1

3

6

9

12

15

Time in Sec

Le

ve

l in

Cm

FL

PID



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7. CONCLUSION

In this paper we have successfully designed and implemented a fuzzy logic controller

on ARM7 microcontroller for a real-time liquid level control. The real time experimental

results show that the proposed control scheme provides better transient as well as steady state

response. More remarkably, the error response seems to be excellent in contrast to most

available linear PID controller. Besides, the incorporation of ARM7 microcontroller made the

system very compact and low cost.

REFERENCES

[1] M. Wang and F. Crusca, “Design and implementation of a gain scheduling controller for

a water level control system,” ISA Transactions, vol.41, no.3, pp.323-331 2002.

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[12] Philips LPC2129 user manual, 2004 at http://www.semiconductors.philips.com

[13] SenSym SX05DN -ICPT Datasheet at http://www.sensortechnics.com

Table 1: Performance comparison of controllers for a step of 15 cm

Performance Indices→

Controller Type↓ tr (Sec) ts (Sec) ess (cm) MP (cm)

PIDC 65.37 108.4 0.2 0

FLC 59.55 101.91 0 0



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