PID CONTROL FOR A WEARABLE E-BRAILLE USING A FORCE SENSING RESISTOR

PROPOSAL

Alejandro Martinez AAdvisor: Dr. Mohammad Saadeh

Dr. Cris KoutsougerasET 493 Senior Design Project I

Spring 2015

ABSTRACT

The objective of this research is to design a control system for a Braille-reading device with an

electronic tactile display. The device is wearable on the dorsal side of the index finger. A

miniature DC motor maintains the contact between the fingertip and the electronic tactile

display. A force sensing resistor (FSR) is used to provide feedback sensory of the tactile force

at the user’s finger pad. The signal of the FSR is transmitted to a microcontroller. Because of

the FSR nonlinearity, nonlinear identification techniques are used to obtain the relation between

the measured voltage and actual force applied at the finger pad. The DC motor, through rack

and pinion mechanism, allows the motion of the tactile display assembly to control the force on

the finger pad.

INTRODUCTION

Control systems are components that are added to other components, to increase functionality,

or to meet a set of design criteria.

This paper addresses a force feedback control system for a device that can help the visually

impaired and blind persons identify the Braille characters using an FSR as the sensing element.

An FSR is a sensor that exhibits a decrease in resistance at its terminals due to an increase in

the force applied at its surface. The FSR technology provided convenient substitutes for the

bulky, complex, and expensive conventional sensing elements. Some of the advantages that

the FSR has over conventional force sensors include no need for maintenance, lower cost,

smaller size, more robust structure, no external circuitry to drive, lesser power consumption, and

most importantly the very small space occupied by the FSR, making it a suitable sensor to many

space-limited applications. In addition, FSR’s can be customized in different shapes and force

ranges to suit many applications. This advanced the use of FSR in automotive, medical,

consumer electronics, and industrial applications.

One of the challenges that designers of devices for personal use face are that there are no

preset values can be suitable for everyone. In the case of the E-Braille reader, the user has to

occasionally increase or decrease the contact force between the electro tactile board and the

finger pad. Therefore, a feedback controller is needed to allow the user to regulate the force

level of the E-Braille device. The controller design should accommodate the nonlinearity of the

FSR.

PID controller types will be tested and compared with varying gains. Response time, steady

state error, and stability are going to be evaluated with each combination of gain values to

determine control viability.

OBJECTIVES

Identify different FSRs using a load cell to calibrate them.

Compare their mechanical properties such as, hysteresis, creep, linearity, and

repeatability.

Use only FSR to measure accurate forces.

Choose the best FSR candidate to use in a Braille-reading device with an electronic

tactile display.

Design a proportional-integral-derivative (PID) control system to drive a DC-motor.

The DC motor regulates the force on the finger pad.

FSR provides feedback sensory of the tactile force and the finger pad.

BACKGROUND DAQ OMEGA 2408

Data acquisition (DAQ) is the process of measuring an electrical or physical phenomenon such

as voltage, current, temperature, pressure, or sound with a computer A DAQ system consists of

sensors, DAQ measurement hardware, and a computer with programmable software.

Compared to traditional measurement systems, PC-based DAQ systems exploit the processing

power, productivity, display, and connectivity capabilities of industry-standard computers

providing a more powerful, flexible, and cost-effective measurement solution.

Data Acquisition (Figure 1)

ARDUINO UNO

The Arduino Uno is a microcontroller board based on the ATmega328 (Figure 2). It has 14

digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz

ceramic resonator, a USB connection, a power jack, an ICSP header, and a reset button. It

contains everything needed to support the microcontroller; simply connect it to a computer with

a USB cable or power it with an AC-to-DC adapter or battery to get started.

Arduino UNO (Figure 2)

MegaMoto Motor Control Shield

The MegaMoto is a dual half-bridge circuit that can be configured either has a full H-bridge or as

two independent half-bridge circuits. This allows a single MegaMoto to drive one motor with full

variable-speed control both forward and reverse (Figure 3).

MegaMotor (Figure 3)

VOLTAGE DIVIDER

A voltage divider is a simple circuit which reduces voltage. Using just two series resistors and

an input voltage, we can create an output voltage that is a fraction of the input (Figure 4).

Voltage Divider (Figure 4)

Force Sensing Resistor (FSR)

FSR's are basically a resistor that changes its resistive value (in ohms Ω) depending on how

much it’s pressed. The harder the force the lower the resistance. These sensors are fairly low

cost, and easy to use but they're rarely accurate (Figure 5).

FSR (Figure 5)

POLOLU MOTOR

With a cross section measuring only 10×12 mm (0.39″×0.47″), these small brushed DC gear motors have a gear ratios—from 5:1 up to 1000:1—and offer a choice between three different motors: high-power (HP), medium-power (MP), and standard. (Figure 6).

Pololu Motor (Figure 6)

DCM 4059C

The DCM 4059G accepts a strain gauge, bridge, load cell, or a summed input from up to four

sensors, and provides a proportional, isolated DC voltage or current output. It includes filtering

and processing to allow effective use of low-level transducers in the noisy environments found

in industrial applications. The full 3-way (input, output, power) isolation makes this module

useful for ground loop elimination, common mode signal rejection or noise pickup reduction.

(Figure 7).

Strain gauge (Figure 7)

Analysis

Stability:

The greatest challenge encountered during testing of the prototype was the instability

problem. The most main indication of instability in the system was found in the output of the

Load Cell. Even though three different DAQ were testing the signal was instable, a DCM 4059

bridge was incorporated to the system in order to remove it. A power supply was using to send

19 volts to the DCM Bridge. The configuration selected was using the excitation of the load cell

0.18 mV, with a desired signal between 1 to 5 Volts. Therefore, the combination 303 V will give

the best performance for the load cell and the bridge. In addition, different software was using to

test such as LabView, DAQcentral, and DAQami. DAQami generated the most stable signal

(Figure 8).

DAQami Signal and Scalar Values for a Load Cell and FSR (Figure 8)

Testing:

After have all the devices setting up, six different FSRs were tested (a bump was placed in the

middle of each FSR in order to distribute the load) Figure 9:

A Pololu #1696 0.5" circle

B C Cp 0151

C 402 FSR

D FlexiForce A201-1 F146

E FlexiForce A201-25 AB124

F Seninstrocics 1/2" shunt mode

FSRs (Figure 9)

The data information was collected using the Load Cell in channel 1 and different FSR’s in

Channel 2. The Slope and the offset was setting up in 1 and zero in the first instance. These

values were modified only in the load cell. The load cell was calibrated each time using a set of

weight of 50 gr, 100 gr, and 150 gr, using 25 samples per channel. An insulated plastic box was

using to reduce noise and filtration values (Figure 10).

Calibration Set Up (Figure 10)

The values were plugged in Matlab, which will generate the slope and offset for each calibration

using Polynomial evaluation (Figure 11). This two values were using again in the DAQami,

which provided calibrate values for the load cell. After have the load cell calibrated, a weight of

400 gr was place on the top of both, FSR and load cell, in order to select the best resistor for the

FSR. The ideal value was to be 3 volts.

Load Cell Calibration using MatLab (Figure 11)

The values for the resistor was for A 8.45 KΩ, B 19.2 KΩ , C 91 KΩ, D 27.4 KΩ, E 1 MΩ, and

F 27.4 KΩ. The next step was to send signal in 1 second (1Hz), 2 seconds (0.5 HZ), and 5

seconds (0.2Hz), in order to obtain values for the hysteresis of each FSR (Figure 12).

Square signals 1 Hz, ½ Hz, and 1/5 Hz (Figure 12)

Data information was also taking for creep using 400 gr for ten minutes. (Figure 13)

Creep set up using 400 grams (Figure 13)

The values were exported to an excel file and then to Matlab, which organized and obtain the

values in mass and voltage.

To Do For Summer

Create disks that can generate triangular and sinusoidal excitation signals that

can be applied at the FSRs. (Figure 14)

Use system identification toolbox in MATLAB to model each FSR.

Solid Work Disk (Figure 14)

Matlab Disk Sinuidonal signal (Figure 14)