robotic car traction control

7/27/2019 Robotic Car Traction Control

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Robotic Car Traction Control

Andrew Gifft atg23

Rob Miceli rjm73

Contents Introduction High Level Design

o Rationalo Background Matho Logical Structureo Hardware/Software Tradeoffso Standardso Existing Patents and Copyrights

Hardware and Software Designo Software Designo Hardware Design

Results Conclusions

o Analysiso Standards and Intellectual Propertyo Ethics

Appendix Acknowledgements

References

Introduction

For our ECE 4760 Final project we have developed a traction control system that detects wheel

slip and adjusts the velocity of the wheels accordingly.

Robotic vehicles are becoming increasingly complex and often need high levels of movement

control. Specifically, when the wheels of a vehicle begin to slip, it is optimal to adjust their speed so

that the vehicle moves towards its intended direction.Applications include vehicles traveling over

rough terrain, exploratory robots, and remote controlled cars. The purpose of our project is to designand implement a four wheel drive robot that monitors the rotational velocity of each wheel and limits

the amount of slip when the vehicle is accelerating.
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Figure 1: Front Two Traction Control Wheels

High Level Design

An electronic limited slip differential system controls the rotational velocity of the output shafts

of a vehicle using speed sensors, anti-lock brakes, and microcontrollers. By electronically monitoring

slipping, the microcontroller can activate the anti-lock brakes to slow down the wheel that is moving too

quickly. An electronic system has the ability to be adjusted for different applications or conditions,

such as on and off-road terrain, slippery weather, or driving at different speeds. This makes it much

more attractive than a mechanical system.While the dynamics of modern day traction controlsystems are very complex, the basic idea motivated our project. The applications for this design are

very practical and universal. Any vehicle with two or more wheels will benefit from greater stability

and movement control with our traction control system.

The main component of our traction control system is a feedback loop that adjusts the velocity

of each individual wheel to the velocity of the slowest wheel on the vehicle. It contains both positive


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and negative feedback by slowing down the fastest wheel motor and speeding up the slowest. A basic

schematic of our system is shown in Figure 1. This block diagram is implemented four times in

software for each wheel.

Figure 2: Basic Feedback Loop Structure

Background Math

Velocity and Acceleration

To calculate the velocity and acceleration from the encoders, we need to record the time

between phase changes.We also need two velocity readings to estimate acceleration and will assume

a constant acceleration between adjacent phases.

To calculate velocity we use the standard formula:

Notice we are sampling and using since our changes are finite. To find we start a

timer at each edge and record it at the following edge. This gives us the elapsed time between known

positions. Therefore our velocities are:


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We can normalize distance out of our calculations since the distance traveled between each

reading is constant at 1/8th a rotation.

These velocities are the average velocities over the recorded time interval and therefore if we

are going to use them to calculate acceleration we must use the velocities at the midpoints of the time

intervals. The acceleration is:

We found it easier to calculate acceleration directly from the time differences using the formula:

Figure 3: Sample graph of velocity calculations

Pulse Width Modulation


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Pulse width modulation of a square wave of changes its duty cycle

to control the amount of time a signal is high during a single period. The average value of a square

wave is defined as,

Where T is the period of the square wave and f(t) is the square wave function. This function can be

described as some maximum value ymax between 0 < t < DT and some minimum value ymin for the rest

of the period, DT < t < T. Here D (duty cycle) is the fraction of the period that the square wave is at

its maximum value. Substituting this into the integral above and then solving for :

All signals generated for this project have a minimum value of zero. Therefore, the average value of

each PWM signal is directly proportional to its duty cycle.

Logical Structure


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Figure 4: Basic Schematic of the Traction Control System

This is a basic block diagram of our traction control system. The

Mega644 microcontroller was used to generate four PWM signals to be sent to the H-Bridges and to

read the rotary encoder signals. Electrical connections are shown in blue and mechanical connectionsare shown in red. Inputs to the microcontroller are labeled as PINxn and outputs are labeled as

PORTxn. OCRnx are the PWM output registers that control the motor speed. This diagram is

explained in detail in the software and hardware design sections of the report.

Hardware/Software Tradeoffs

There were many trade-offs that had to be made in our design, many of which we discovered

while debugging and testing. One major trade-off was the rotary encoders we used to determine

wheel velocity. There are two major classes of encoders, optical and mechanical.Optical encoders

offer significantly higher accuracy, can offer much faster maximum rotational speeds and most

importantly for this project, must higher pulses per revolution (easily exceeding 128 ppr). The greater

the pulses per revolution, the quicker the response, and/or the more accurate the velocity

reading. Unfortunately these encoders are priced outside of our budget and we were forced to use a

mechanical encoder with 16 pulses per rotation. This means that we would require two full rotations

of the wheel to gather as much data as 1/4 revolution of a fairly common optical encoder. The

additional information could help the effectiveness of a traction control system on two accounts. First

we could detect a slip quicker, apply a duty cycle change, and check the effectiveness of that change in a

much shorter time frame and more importantly, a much smaller wheel rotation. Or, if there are

inaccuracies in our time readings (as we see with our current encoders) we could average 16 readings in

1/8 revolution with the optical and a maximum of two readings with the mechanical encoder.We can

easily see the disadvantage of so few pulses in our prototype as it takes multiple revolutions to reach

the desired speed.

We also encountered tradeoffs that had to be made within the design of our software.We

started out designing an edge triggered interrupt to accurately detect a phase change on the encoder

signal and get an accurate time reading. Since the Mega644 only has one comparator we had to

combine the pulses from the four wheels onto 1 signal wire.We found a hardware solution to

implement this (by XORing the previously read data points and ORing the XORs outputs). But this

required us to allow our edge-triggered interrupt to be interrupted. Another difficulty with our edge

triggered interrupt was the significant switch bounce seen on the encoders during a phase

change. This problem is described in greater detail in the software section. The combination of our


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re-entrant interrupt and significant bounce noise required us to switch to polling the encoders. This

gives us much less accurate timing, but is significantly easier to implement.

Figure 5: Design of edge triggered interrupt external hardware

(not used in final design)

Finally, we identified a tradeoff with our threshold settings. The more we allowed wheel

velocity to vary, the faster the wheels would reach the target speed. This is truly a tradeoff as both

results are desired.We simultaneously want fast response as well as matched wheel speeds. Ofcourse the solution to this problem is to implement a sophisticated algorithm and feedback

control. Unfortunately we did not have the time, nor the expertise required to properly characterize

the system and develop the stable and efficient algorithms needed to fully utilize the system and perfect

the design.

Standards

Our project was not required to follow any standards when it is

under operation. It consists of a self contained system that does not transmit or receive any external

data.Additionally, our robotic vehicle obeyed all speed limits and local traffic laws.When

programming and debugging our software, we followed the RS-232 standards for serial data.

Existing Patents and Copyrights

The ideas used in our project were considered to be in the public

domain and we did not copy any patented designs to accomplish our goals. This project was used for


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educational and demonstrational purposes only. At the conclusion of our project, we are not

considering to pursue any copyrights or patents on our design.

Figure 6: Complete Traction Control Vehicle

Software Design

The software can be broken up into three major sections, timers, PWM signal edge detection,

and wheel torque adjustments. In combination these parts are able to accurately read each wheels


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velocity and acceleration and allocate the proper amount of torque to reach and maintain a desired

speed while improving traction by reducing wheel slip.

Interrupts and Timers

Due to the relatively slow servo motor maximum speed, encoder signal noise, and difficulties

with edge triggered interrupts, we decided it would be sufficient to poll the rotary

encoders. Therefore we maintained a counter for each wheel that was incremented in our interrupt

every 0.2ms. Placing the counters in the interrupt ensures that the few hundred to roughly two

thousands increments were done consistently, and any timing accuracy lost was only during the

processing of these counts. For example, during our testing we underestimated the length of time to

send data to Hyper Term. Before placing the counters in the interrupt we saw a very large margin of

error with our elapsed time measurements as the hyperterm task influenced each increment. After

moving the counters to the interrupt we only had one delay per encoder phase instead of a delay foreach additional timer increment. This gave much more consistent speed measurements (although we

decided to remove Hyper Term functionality all together for the final car).

Detecting Encoder Edges and Recording Data

Due to budget constraints we were forced to use the less expensive and reliably mechanical

type encoder instead of the more advanced optical encoders. During our debugging process we

noticed that the edges during phase transitions were very inconsistent as the switches would make and

break a connection multiple times at each transition. This variability would often continue for around

1ms (see diagram).

Figure 7: Rotary Encoder Edge Noise

In order to accurately detect a single edge we implemented the debouncing technique used in

the DTMF dialer lab.We found reliable edges could be detected with ten consistent readings (at 5kHz)

This limits the maximum phase of the PWM signal to roughly 3ms limiting wheel speed to about 41Hz,

which is still far beyond the capability of the servo motors used. After an accurate phase reading is

taken, we compare the phase with the last recorded value. Only when a change occurs is an edge


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detected. Once an edge was detected the elapsed time counter was stored and reset. The previous

datapoint was moved but not deleted. Remember that two velocities are needed to calculate

acceleration (see background math section).

Deciding torque adjustments

The main purpose of a traction control system is to maintain wheel grip. In order to do this we

must detect when a wheel is moving faster than the car.We use two references to control the duty

cycle of the PWMs sent to the wheels. First, the car has a desired speed. In order to make

calculations as simple as possible we refer everything to the inverse of velocity, which once distance is

normalized out, are simply our timed pulse widths. Our desired speed is therefore recorded as a pulse

width and all wheels accelerate or decelerate in order to stay within a defined margin of the desired

speed. If a wheel is moving significantly slower than the desired speed, then we ensure that no wheel

is moving faster than a different pre-defined margin of this slowest wheel. This process allows the carto quickly reach its desired speed without slipping.

Below is a timing diagram for our PWM signal.We programmed the TCNT1 and TCNT2

registers to count from 0 to 256.When we wanted to increase the speed of the wheel and thus the

increase the duty cycle on the PWM signal, we would increment the OCRnx register by a fixed

value. This was true for the inverse.When we decremented the OCRnx register, the duty cycle of

the PWM would decrease.Also note that the frequency of the pulses is identical no matter the duty

cycle.We used a frequency of about 31 kHz to optimize the H-bridge functionality.

Figure 8: Mega644 PWM Output Timing Diagram


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Hardware Design

The hardware was designed to create a robotic car with four

individually controlled servo motors using PWM signals and H-bridge circuits. The rotation of each

motor was measured with a 2-bit (gray code) rotary encoder. Additionally, the acceleration of the

vehicle was measured with a z-axis 1.5g acceleration sensor. A basic schematic of the hardware setup

is shown below.

H-Bridge Motor Driver

The H-Bridge circuit allows the microcontroller to run a two-terminal motor without any large

noise spikes feeding back into the microcontroller circuitry.When the motor is switched on and off,

there is a large change in voltage in a short period of time. Since the motor can be modeled as an

inductor, the big change in voltage will cause a huge spike in current, which can destroy the input

terminal of the microcontroller. This is prevented by wiring diodes across four MOSFET in the

configuration shown below to stop current from flowing when the MOSFET is turned off. These

devices have similar properties of a switch because they can limit or amplify the flow of current

depending on the voltage across the gate to body terminals.

Figure 9: H-Bridge Motor Driver Schematic

The H-Bridge came in a 8-pin SOP package with inputs for Vcc,

ground, forward pulse width modulation (PWM), and reverse PWM. There were two outputs to wire

across the terminals of the motor. Since there were two separate inputs for forward and reverse

PWM signals, our design was utilized only the forward PWM signal to control how the vehicle moved in

the forward direction.


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Figure 10: Two H-Bridges on an SOIC Header

Rotary Encoder

The rotary encoders are used to resolve wheel velocity and acceleration. The encoders output

a two bit binary quadrature signal, meaning they output two pulse width signals that are 90 degrees out

of phase. The pulses are generated by opening and closing switches to a common

ground. Therefore, in order to generate a readable signal from the encoders, we used pull-up resistors

on the signal lines. Also, since the two phases were not sufficiently accurate relative to eachother, we

only use 1 signal from each encoder. This cuts the number of pulses per rotation in half, but gives us a

more consistent signal for accurate timing.

Figure 11: Rotary Encoder Schematic

The specific encoders we used had 16 pulses per rotation. This means that there were four

periods per rotation. The repetition would be insufficient for us to determine the position of the

wheel (it is ambiguous which quadrant the wheel is in) but satisfactory for determining velocity. An

example signal and rotary position is shown below. Velocity is determined by timing the difference

between the rising and falling edges. Similarly, acceleration can be determined by calculating the


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difference in velocity between two adjacent pulses. Note the acceleration between the orange phase

and green phase.

Figure 12: Pulse signal from encoder with encoder position

Servo Motors

To rotate the wheels of our vehicle, we used four continuous

rotation servo motors. They run on a maximum of 6 volts DC and rotate a full 0 to 180 degrees. An

image of the motor is shown below. To attach the rotary encoder, we modified each motor by

removing the back plate and attaching a metal rod to the back side of the rotating gear.We then used

flexible clear plastic tubing to form a connection between the metal rod and the rotary encoder. A

picture of the modification is shown below.

Figure 13: Servo Motor Layout


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Figure 14: Servo Motor Layout

ATmega644

The Atmel Mega644 was used to control and monitor the

acceleration and velocity of our vehicle. The major features of the Mega644 that were used for our

project included one 8-bit timer, 4 PWM output pins, one Analog to Digital converter, and

four standard I/O pins. The Mega644 chip was built on a prototype board provided by Bruce

Land. It features an easy way to power, attach a crystal clock, connect to a RS232 interface, and

program the Atmel chip. A schematic of the board can be foundhere.

We chose to attach the optional interface for serial communication because it provided critical

debugging features and we had extra money to budget. An image of a completed prototype board is

displayed below courtesy of the ECE 4760 website.
http://www.nbb.cornell.edu/neurobio/land/PROJECTS/Protoboard476/v8for2009/V8schematic.pnghttp://www.nbb.cornell.edu/neurobio/land/PROJECTS/Protoboard476/v8for2009/V8schematic.pnghttp://www.nbb.cornell.edu/neurobio/land/PROJECTS/Protoboard476/v8for2009/V8schematic.pnghttp://www.nbb.cornell.edu/neurobio/land/PROJECTS/Protoboard476/v8for2009/V8schematic.png


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Figure 15: Sample proto-board from 476 website

Results

In general, the end result of our traction control was a

success. The vehicle was able to ramp up its velocity uniformly without any major slipping.When

the vehicle reached its desired speed, the wheels maintained their velocity and generally stayed in

balance with each other. In order to test that out system was working, we performed two

tests. First, we placed tightly fitting rubber bands around the outside rims of the left two wheels and

kept the plastic wheels on the right side unmodified.We then started up the vehicle in the hallway on

hard tile flooring. As the car started up, the velocity of each wheel was maintained a constant rate

and the car moved in a relatively straight path for about 2 meters.

The results of the start-up can be seen in the plot below.When the car first starts, wheel

number 4, shown in turquoise, is established as the fastest wheel and wheel number 3, shown in red is

the slowest wheel. Almost immediately, wheel 4 jumps up to 14 rpm and is promptly slowed to run at

the same speed as the other wheels. The wheels continue to pick up speed, until they reach a

programmed desired speed. It is held at this speed for a few seconds. Looking at the region

between 3 and 7 seconds, wheel number 2 naturally tries to run at a speed faster than desired. This is

recognized by the microcontroller and the PWM is adjusted to make the wheel run slower. The

reverse is true for wheel 3 and the microcontroller sends more power to the motor to account for this.

The second test involved pushing down on a single wheel and slowing it down to a very lowvelocity. The system would pass this test if the other wheels recognized that they were rotating too

quickly and slowed down appropriately. The results of this test are also shown in the graph

below. At about 7 seconds, the second wheel is held down and its velocity drops to about 6

rpm. The response of the other wheels 1 and 3 is pretty fast and responds to the velocity change

within a couple of measurement cycles.Wheel 4 was the slowest to respond to the change, taking

less than 1 second. Although there is a fast response time to the velocity change, the time it takes to

actually slow the other wheels to the slowest level takes a very long time. As shown in the graph, it

takes well above 2 seconds for the other wheels to drop down to 10 rpm.


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Figure 16: RPM vs Time for Traction Control Start-up


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Figure 17: Worst Case RPM vs Time Scenario

The results of our second traction control test are shown

above. At about 1 second, the 4th wheel is held down and it slows to 20 rpm. The other wheels

respond in about the same manner as the first test.Overall, we can see that it takes around 3 seconds

to the wheels to completely adjust to a large change in velocity. Then, at 12 seconds the wheel is

released. Since wheel 4 naturally ran the fastest of all the wheels, there was a large

acceleration. The system tries to respond to the situation, but it takes over 8 seconds just for the

wheels to recover about of the velocity difference.This was a worst case scenario for our traction

control system.

ConclusionsAnalysis


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Our prototype traction control system demonstrates how a high speed microcontroller can be

used to accurately control a varying and possibly unstable system. Our controller effectively throttled

wheel speed when a slip was detected as well as actively controlled wheel rotation to maintain the

desired speed. Although our results demonstrated the functioning of our code, we originally hoped

for a faster response ideally being able to correct wheel slip within one rotation. The slow response

was mainly caused by adjusting the PWM pulses by 1% each time a measurement was recorded.We

sacrificed a fast responding system for a more stable one. This is always a careful balance to choose

for many engineering problems. The addition of higher accuracy encoders as well as implementing a

more sophisticated control algorithm would help us achieve more desirable results. Due to our

implementation of a four wheel, independent drive system, our project would require some significant

overhead to be integrated with other commercial vehicle stability systems such as ABS (anti-lock

breaking system) and ESC (electronic stability control) systems. This again was a trade-off as

integrating with the various automotive communication standards and devices would in itself be asubstantial design project.

Standards and Intellectual Property

Our project did not require the use of any public or private domain software or any proprietary

hardware. Although our specific implementation of a traction control system itself would not be

sufficient for a patent, it does serve as a strong baseline for further development on traction

control. Traction control itself is a fairly new technology and is just now becoming main-stream in

consumer automobiles. There is certainly substantial opportunity for improvement to the currentalgorithms and systems used today, especially if the focus is moved to include performance (most

traction control systems are for safety only and can negatively affect performance).

Ethics

During the design of our traction control system we had to keep in mind the use of such systems

and the ethical responsibilities of design.Most traction control systems are used on consumer motor

vehicles as a safety system. Our system, as well as commercial grade systems, has the ability to over-

ride driver control. A malfunction of this system could significantly compromise the safety of the

passengers as well as others on the road. Even if the system was not used on a passenger vehicle, it is

still partially or wholly responsible for the movement of a potentially dangerous object. Therefore

during our design we constantly had to keep safety in mind and try to design such that failures are

minimized and will not result in an out of control vehicle. This was reflected in our decision to keep

the less aggressive method of throttling up wheel speed and using small accepted margins of

error. This means that our model cars acceleration was significantly less than the motors where


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capable of doing, but the slower accelerating vehicle would have better slip protection favoring safety

over performance.

Appendix

Budget

Item Unit Price Quantity Price

Electric Servo Motor Sampled 4 0.00

Rotary encoder 2.58 4 10.32

IC H-Bridge 1.35 4 5.40

Atmel Mega 644 Sampled 1 0.00

MAX233ACPP Sampled 1 0.00

Custom PC Board 4.00 1 4.00Max233CPP 7.00 1 7.00

RS232 Connector 1.00 1 1.00

SOIC Carriers 1.00 2 2.00

Accelerometer Sampled 1 0.00

Car chassis Borrowed from MAE 1 0.00

Scrap Metal Free 1 0.00

Wheels Borrowed from MAE 4 0.00

9 Volt Battery 2.00 1 2.00

AA 1.5 Volt Battery 0.50 4 2.00

TOTAL $33.72

Source Code

Task Distribution

Andrew Gifft

Motor Control Software Encoder Hardware and Measurement Software
http://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2009/atg23_rjm73/atg23_rjm73/tractionPoll.chttp://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2009/atg23_rjm73/atg23_rjm73/tractionPoll.chttp://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2009/atg23_rjm73/atg23_rjm73/tractionPoll.c


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Hyperterm/ Debugging Code Compiling Hardware Components Together Debugging Final Report

Rob Miceli

Robot Chassis Frame Extension H-bridge Hardware PC Board Fabrication PWM Control Code Debugging Final Report

Acknowledgements

We would like to thank Bruce Land for his help and dedication to

ECE 4760 Designing with Microcontrollers.We would also like to thank our TA Yuan Ning and the

other 4760 TAs for their help and many hours in the 4760 lab. Additionally, we would like to thank

Atmel and Maxim-IC for their sampled parts. Lastly, we would like to thank the Mechanical

Engineering Department for allowing us to borrow the robotic vehicle parts.

References

AtMega644 Datasheet

Encoder Datasheet

H-Bridge Datasheet

Servo Motor Datasheet

Accelerometer Datasheet

ECE 4760 Webpage
http://instruct1.cit.cornell.edu/courses/ee476/AtmelStuff/mega644full.pdfhttp://instruct1.cit.cornell.edu/courses/ee476/AtmelStuff/mega644full.pdfhttp://www.ctscorp.com/components/datasheets/288.pdfhttp://www.ctscorp.com/components/datasheets/288.pdfhttp://www.rohm.com/products/databook/general/pdf/bd621x_series-e.pdfhttp://www.rohm.com/products/databook/general/pdf/bd621x_series-e.pdfhttp://www.parallax.com/dl/docs/prod/motors/stdservo.pdfhttp://www.parallax.com/dl/docs/prod/motors/stdservo.pdfhttp://pdfserv.datasheetpro.net/FreescaleSemiconductor/MMA1260D.pdfhttp://pdfserv.datasheetpro.net/FreescaleSemiconductor/MMA1260D.pdfhttp://instruct1.cit.cornell.edu/courses/ee476/http://instruct1.cit.cornell.edu/courses/ee476/http://instruct1.cit.cornell.edu/courses/ee476/http://pdfserv.datasheetpro.net/FreescaleSemiconductor/MMA1260D.pdfhttp://www.parallax.com/dl/docs/prod/motors/stdservo.pdfhttp://www.rohm.com/products/databook/general/pdf/bd621x_series-e.pdfhttp://www.ctscorp.com/components/datasheets/288.pdfhttp://instruct1.cit.cornell.edu/courses/ee476/AtmelStuff/mega644full.pdf

robotic car traction control

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