ADAPTIVE CRUISE CONTROL MECHATRONICS MirzaAbdel.ppt

ADAPTIVE CRUISE CONTROL MECHATRONICS MirzaAbdel.ppt

1. Adaptive Cruise Control (ACC)Adaptive Cruise Control (ACC)
ELG 4152 ProjectELG 4152 Project
Professor Riadh HabashProfessor Riadh Habash
TA: Fouad KhalilTA: Fouad Khalil
Group Memebers:Group Memebers:
Mirza Abdel Jabbar Baig (3256498)Mirza Abdel Jabbar Baig (3256498)
Mohammad Ali Akbari (3299852)Mohammad Ali Akbari (3299852)
Navid Moazzami (3413826)Navid Moazzami (3413826)
Hasan Ashrafuzzaman (3384661)Hasan Ashrafuzzaman (3384661)

2. ReferenceReference
[1] [1] A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go ScenariosA Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios
Martinez, J.-J.; Canudas-de-Wit, C.; Volume 15,  Issue 2,  March 2007 Page(s):246 – 258Martinez, J.-J.; Canudas-de-Wit, C.; Volume 15,  Issue 2,  March 2007 Page(s):246 – 258
[2] [2] Modeling a Cruise ControlModeling a Cruise Control
http://www.library.cmu.edu/ctms/ctms/examples/cruise/cc.htmhttp://www.library.cmu.edu/ctms/ctms/examples/cruise/cc.htm
[3] [3] Highway Speed ControllerHighway Speed Controller
http://www.site.uottawa.ca/~misbah/elg4392/HC12CodeWarriorC/HighwaySpeedController/http://www.site.uottawa.ca/~misbah/elg4392/HC12CodeWarriorC/HighwaySpeedController/
project.cproject.c
[4] W. Jones, “Keeping cars from crashing,” IEEE Spectrum, vol. 38, no.
9, pp. 40–45, Sep. 2001.
[5] M. A. Goodrich and E. R. Boer, “Designing human-centered automation:
Tradeoffs in collision avoidance system design,” IEEE Trans. Intell.
Transp. Syst., vol. 1, no. 1, pp. 40–54, Mar. 2000.

3. Problem StatementProblem Statement

The main problem regarding the normal Cruise The main problem regarding the normal Cruise
Control technology is that it is not aware of Control technology is that it is not aware of
other vehicles’s movementother vehicles’s movement

The driver must be always aware. Hence, The driver must be always aware. Hence,
possibility of mistakespossibility of mistakes

Possibility of collision with the leading car if not Possibility of collision with the leading car if not
manually slowed downmanually slowed down

4. Proposed SolutionProposed Solution

Introduce Adaptive Cruise Control for Introduce Adaptive Cruise Control for
longitudinal control of the vehiclelongitudinal control of the vehicle

Speed would be automatically adjusted for safe Speed would be automatically adjusted for safe
inter-distanceinter-distance

Once safe inter-distance is reached, the speed Once safe inter-distance is reached, the speed
would return to the desired speed set by the would return to the desired speed set by the
driverdriver

5. Technical ObjectivesTechnical Objectives

To design a control system for ACC.To design a control system for ACC.

No overshootNo overshoot

Settling Time of about 4-7 seconds.Settling Time of about 4-7 seconds.

No oscillation (because no overshoot)No oscillation (because no overshoot)

A steady-state error of 0A steady-state error of 0

6. Vehicle CharacteristicsVehicle Characteristics

If the inertia of the wheels is neglected, and it is If the inertia of the wheels is neglected, and it is
assumed that friction (which is proportional to assumed that friction (which is proportional to
the car's speed) is what is opposing the motion the car's speed) is what is opposing the motion
of the car, then the problem is reduced to the of the car, then the problem is reduced to the
simple mass and damper system shown in the simple mass and damper system shown in the
next slide.next slide.

7. Vehicle CharacteristicsVehicle Characteristics

8. System Block Diagram [2]System Block Diagram [2]

9. Controller SelectionController Selection

Which kind of Controller is the best?Which kind of Controller is the best?

No controller.No controller.

P controller.P controller.

PI controller.PI controller.

PID controller.PID controller.

PD controller.PD controller.

10. Controller SelectionController Selection
P ControllerP ControllerNo ControllerNo Controller
Step Response
Time (sec)
A
m
p
lit
u
d
e
0 20 40 60 80 100 120
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
System: untitled1
Settling Time (sec): 76.7
Settling time = 76.7 s
Steady state error > 98%
Kp = 10000
Settling Time = 0.389s
Steady state error = 2%
Step Response
Time (sec)
A
m
p
lit
u
d
e
0 0.1 0.2 0.3 0.4 0.5 0.6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
System: untitled1
Settling Time (sec): 0.389

11. Controller SelectionController Selection
Kp=800, Ki=40
Settling time = 4.89 s
Steady state error = 0
Step Response
Time (sec)
A
m
p
lit
u
d
e
0 1 2 3 4 5 6 7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
System: untitled1
Settling Time (sec): 4.89
PI ControllerPI Controller
*Final
choice is PI
Controller*

12. Distance Checking [1]Distance Checking [1]
Three scenarios:Three scenarios:
dd
r > r >
dd
00, cruises at desired speed, ACC inactive, cruises at desired speed, ACC inactive
dd
rr
< d < d
cc, danger zone, ACC enables to slow down, danger zone, ACC enables to slow down
dd
00 < d < d
rr
< d < d
00, ACC is enable to reach safe inter-distance , ACC is enable to reach safe inter-distance

13. Implementation of Distance Implementation of Distance
Checking [3]Checking [3]

The distance checking algorithm only requires a minimum distance and a The distance checking algorithm only requires a minimum distance and a
range.range.

The algorithm calculates the actual minimum distance (> provided The algorithm calculates the actual minimum distance (> provided
distance) and maximum distance and then outputs the new speed of the distance) and maximum distance and then outputs the new speed of the
vehicle. vehicle.

The user can also provide a maximum and minimum speed for the The user can also provide a maximum and minimum speed for the
vehicle.vehicle.

14. Implementation of Distance Implementation of Distance
CheckingChecking
temp=(300*(speedmax-speedmin))/(12*range)
minimum_Distance=(minimum_Distance*32)/10
max_Distance = minimum_Distance + (3*range)

if (distance > (max_Distance))
speed = speedmax;

if (distance < minimum_Distance)
speed = 0;

if ((distance < max_Distance) and (distance>minimum_Distance))
if leader_speed > 0
speed = ((100*speedmin-(kvit*(minimum_distance))) + temp * distance)/100;
else
speed = ((100*speedmin+(kvit*(max_Distance))) + temp * distance)/100;

15. SimulationSimulation

Maximum follower vehicle speed = 100 m/sMaximum follower vehicle speed = 100 m/s

Minimum follower vehicle speed = 0 m/sMinimum follower vehicle speed = 0 m/s

Minimum distance = 40 mMinimum distance = 40 m

Range = 20 mRange = 20 m

Initial distance = 80 mInitial distance = 80 m

Kp = 800Kp = 800

Ki = 40Ki = 40

b = 50b = 50

m = 1000m = 1000
The following parameters were used for the simulation:The following parameters were used for the simulation:

16. Final Model (simplified)Final Model (simplified)

17. SimulationSimulation
Yellow: Distance between two vehicles
Blue: Speed of the leader vehicle
Purple: Speed of the follower vehicle

18. Limitations/ConclusionLimitations/Conclusion

Not a complete transfer function of the vehicle Not a complete transfer function of the vehicle
and environment.and environment.

Linear distance-checking model.Linear distance-checking model.

No limitations on the acceleration and jerk.No limitations on the acceleration and jerk.

Our model is simplified compared to real-time Our model is simplified compared to real-time
models, but can be used to implement a practical models, but can be used to implement a practical
ACC.ACC.