john t. cameron pennsylvania state university dr. sean brennan pennsylvania state university
DESCRIPTION
A Comparative, Experimental Study of Model Suitability to Describe Vehicle Rollover Dynamics for Control Design. John T. Cameron Pennsylvania State University Dr. Sean Brennan Pennsylvania State University. Outline. Goals Analytical Vehicle Models Experimental Model Validation Conclusions. - PowerPoint PPT PresentationTRANSCRIPT
Dept. Of Mechanical and Nuclear Engineering, Penn State University
Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
A Comparative, Experimental Study of Model Suitability to Describe Vehicle Rollover Dynamics for Control Design
John T. CameronPennsylvania State University
Dr. Sean BrennanPennsylvania State University
Dept. Of Mechanical and Nuclear Engineering, Penn State University
2/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Outline
1. Goals2. Analytical Vehicle Models3. Experimental Model Validation4. Conclusions
Dept. Of Mechanical and Nuclear Engineering, Penn State University
3/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Goals
Examine various vehicle models to determine the effect that different assumptions have on: Model order Model complexity Number and type of parameters required
Experimentally validate the models to: Determine model accuracy Relate modeling accuracy to assumptions made Determine the simplest model that accurately
represents a vehicles planar and roll dynamics
Dept. Of Mechanical and Nuclear Engineering, Penn State University
4/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models
Standard SAE sign convention
Dept. Of Mechanical and Nuclear Engineering, Penn State University
5/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models Basic Assumptions Common to All Models
All models are linear Result:
• Small angles are assumed making cos(θ)≈1, sin(θ)≈0• Constant longitudinal velocity (along the x-axis)• The lateral force acting on a tire is directly proportional
to slip angle
• Longitudinal forces ignored• Tire forces symmetric right-to-left
sin1cos
sin1cos
tiretire CF
Dept. Of Mechanical and Nuclear Engineering, Penn State University
6/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models
Model 1 – 2DOF Bicycle Model
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Dept. Of Mechanical and Nuclear Engineering, Penn State University
7/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models Model 2 – 3DOF Roll Model
Assumes the existence of a sprung mass No x-z planar symmetry Originally presented by Mammar et. al., National Institute of
Research on the Transportations and their Security (INRETS), Versailles, France in 1999
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Dept. Of Mechanical and Nuclear Engineering, Penn State University
8/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models Model 3 – 3DOF Roll Model
Assumes the existence of a sprung mass x-z planar symmetry Roll-steer influence Originally presented by Kim and Park, Samchok University,
South Korea, 2003
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Dept. Of Mechanical and Nuclear Engineering, Penn State University
9/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models Model 3 (continued)
As a result of the assumption of roll steer, the external forces acting on the vehicle change accordingly
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Dept. Of Mechanical and Nuclear Engineering, Penn State University
10/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models Model 4 – 3DOF Roll Model
Assumes a sprung mass suspended upon a massless frame x-z planar symmetry No roll steer influence Originally presented by Carlson and Gerdes, Stanford
University, 2003
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Dept. Of Mechanical and Nuclear Engineering, Penn State University
11/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle Models Effect of assuming force equivalence
Slightly changes plant description (i.e. eigenvalues) Additionally, causes a higher gain in roll response from the
massless frame assumption
Dept. Of Mechanical and Nuclear Engineering, Penn State University
12/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Model Fitting Procedures1. Experimentally determine the understeer gradient to find
the relationship between front and rear cornering stiffness values.
Considering both frequency and time domains*:
2. Determine estimates on cornering stiffness values by fitting of the 2DOF Bicycle Model (Model 1).
3. Determine estimates on roll stiffness and damping by fitting of Models 2 – 4.
* - Time domain maneuvers were a lane change and a pseudo-step
Dept. Of Mechanical and Nuclear Engineering, Penn State University
13/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Time Domain Fit Results
101
0
5
10
15
Frequency Response, Steering Input to Yaw Rate, Mercury Tracer U =16.5 Cf =-22750 Cr =-19958.4561 K =38000 D =5000
w (rad/s)
Mag
(dB
)
101
-100
-50
0
w (rad/s)
Pha
se (d
eg)
MeasuredModel 1Model 2Model 3Model 4
101
15
20
25
30
35
40
Frequency Response, Steering Input to Lateral Acceleration, Mercury Tracer U =16.5 Cf =-22750 Cr =-19958.4561 K =38000 D =5000
w (rad/s)
Mag
(dB
)
101
0
50
100
150
w (rad/s)
Pha
se (d
eg)
MeasuredModel 1Model 2Model 3Model 4
Dept. Of Mechanical and Nuclear Engineering, Penn State University
14/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Model Fitting Results Results for Steering Input to Lateral Acceleration
101
15
20
25
30
35
40
Frequency Response, Steering Input to Lateral Acceleration, Mercury Tracer U =16.5 Cf =-45500 Cr =-75562.5 K =53000 D =6000
w (rad/s)
Mag
(dB
)
101
0
50
100
150
w (rad/s)
Pha
se (d
eg)
MeasuredModel 1Model 2Model 3Model 4
Freq. Domain Fit
Dept. Of Mechanical and Nuclear Engineering, Penn State University
15/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Model Fitting Results Results for Steering Input to Yaw Rate
101
0
5
10
15
Frequency Response, Steering Input to Yaw Rate, Mercury Tracer U =16.5 Cf =-45500 Cr =-75562.5 K =53000 D =6000
w (rad/s)
Mag
(dB
)
101
-100
-50
0
w (rad/s)
Pha
se (d
eg)
MeasuredModel 1Model 2Model 3Model 4
Freq. Domain Fit
Dept. Of Mechanical and Nuclear Engineering, Penn State University
16/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Model Fitting Results Results for Steering Input to Roll Rate
101
-5
0
5
10
15
Frequency Response, Steering Input to Roll Rate, Mercury Tracer U =16.5 Cf =-45500 Cr =-75562.5 K =53000 D =6000
w (rad/s)
Mag
(dB
)
101
-100
-50
0
50
100
w (rad/s)
Pha
se (d
eg)
MeasuredModel 2Model 3Model 4
Freq. Domain Fit
Dept. Of Mechanical and Nuclear Engineering, Penn State University
17/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Model Fitting Results Inconsistency in roll rate measured response does not
appear at lower speeds
Better sensors are required to clarify inconsistencies in data – especially lateral acceleration and roll rate
100
101
-10
0
10
Frequency Response, Steering Input to Roll Rate U =8.9 Cf =-45500 Cr =-75560 K =53000 D =6000
w (rad/s)
Mag
(dB
)
100
101
-500
-450
-400
-350
-300
-250
w (rad/s)
Pha
se (d
eg)
measuredModel 2Model 3Model 4
Dept. Of Mechanical and Nuclear Engineering, Penn State University
18/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Remarks on Model Validation
As a result of overall accuracy and simplicity, Model 3 was chosen for further investigation. This entails: The development of model-based predictive
algorithms for rollover propensity The development of control algorithms for rollover
mitigation
Dept. Of Mechanical and Nuclear Engineering, Penn State University
19/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Conclusions
A relatively simple dynamic model is capable of modeling both the planar and roll dynamics of a vehicle well under constant speed conditions.
Relatively accurate measurements may be taken with inexpensive sensors The dynamics are seen even with commercial grade
sensors Important for industry because such sensors are typically
found in production vehicles
Extra care should be taken when model fitting in the time domain
Dept. Of Mechanical and Nuclear Engineering, Penn State University
20/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Time Response Tests Pseudo-Step Response, 8.9 m/s, 0.09 rad amplitude, FR Params
1 1.5 2 2.5
0.02
0.04
0.06
0.08
0.1Step, Steering vs. Time
Time (s)
Ste
erin
g A
ngle
(rad
)
1 1.5 2 2.50
0.05
0.1
0.15
0.2
0.25
0.3Yaw Rate vs. Time
Time(s)
Yaw
Rat
e (ra
d/s)
MeasuredModel 1Model 2Model 3Model 4
1 1.5 2 2.5
0
0.2
0.4
0.6
0.8
1
1.2
Lat. Accel. vs. Time
Time (s)
Lat.
Acc
el. (
m/s
2 )2.5 3 3.5 4
0
0.02
0.04
0.06
0.08
0.1
Steering vs. Time
Time (s)
Ang
le (r
ad)
2.5 3 3.5 4
0
0.05
0.1Roll Rate vs. Time
Time (s)
Rol
l Rat
e (ra
d/s)
Dept. Of Mechanical and Nuclear Engineering, Penn State University
21/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Time Response Tests Pseudo-Step Response, 8.9 m/s, 0.09 rad amplitude, TR Params
1 1.5 2 2.50
0.02
0.04
0.06
0.08
0.1Step, Steering vs. Time
Time (s)
Ste
erin
g A
ngle
(rad
)
1 1.5 2 2.50
0.05
0.1
0.15
0.2
0.25
0.3
Yaw Rate vs. Time
Time(s)
Yaw
Rat
e (ra
d/s)
MeasuredModel 1Model 2Model 3Model 4
1 1.5 2 2.5
0
0.5
1
Lat. Accel. vs. Time
Time (s)
Lat.
Acc
el. (
m/s
2 )
2.5 3 3.5 40
0.02
0.04
0.06
0.08
0.1
Steering vs. Time
Time (s)
Ang
le (r
ad)
2.5 3 3.5 4
-0.02
0
0.02
0.04
0.06
0.08
0.1Roll Rate vs. Time
Time (s)
Rol
l Rat
e (ra
d/s)
Dept. Of Mechanical and Nuclear Engineering, Penn State University
22/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Time Response Tests Lane Change Maneuver, 17.8 m/s, Right-to-Left, then Left-to-Right,
FR
0 2 4 6 8-0.04
-0.02
0
0.02
0.04Lane Change, Steering Angle vs. Time
Time (s)
Ang
le (r
ad)
0 2 4 6 8
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Yaw Rate vs. Time
Time(s)
Yaw
Rat
e (ra
d/s)
2 4 6 8
-0.5
0
0.5
Lat. Accel. vs. Time
Time (s)
Lat.
Acc
el. (
m/s
2 )
MeasuredModel 1Model 2Model 3Model 4
0 2 4 6 8
-0.04
-0.02
0
0.02
0.04
Steering Angle vs. Time
Time (s)
Ang
le (r
ad)
0 2 4 6 8
-0.1
-0.05
0
0.05
0.1
Roll Rate vs. Time
Time (s)
Rol
l Rat
e (ra
d/s)
Dept. Of Mechanical and Nuclear Engineering, Penn State University
23/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Time Response Tests Lane Change Maneuver, 17.8 m/s, Right-to-Left, then Left-to-Right,
Time
0 2 4 6 8-0.04
-0.02
0
0.02
0.04Lane Change, Steering Angle vs. Time
Time (s)
Ang
le (r
ad)
0 2 4 6 8
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
Yaw Rate vs. Time
Time(s)
Yaw
Rat
e (ra
d/s)
0 2 4 6 8
-0.5
0
0.5
Lat. Accel. vs. Time
Time (s)
Lat.
Acc
el. (
m/s
2 )
MeasuredModel 1Model 2Model 3Model 4
0 2 4 6 8
-0.04
-0.02
0
0.02
0.04
Steering Angle vs. Time
Time (s)
Ang
le (r
ad)
0 2 4 6 8
-0.1
-0.05
0
0.05
0.1
Roll Rate vs. Time
Time (s)
Rol
l Rat
e (ra
d/s)
Dept. Of Mechanical and Nuclear Engineering, Penn State University
24/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Experiments Performed Determination of Understeer Gradient
Understeer gradient is a constant indicating the additional amount of steering necessary to maintain a steady-state turn per g of lateral acceleration (e.g. units are rad/g)
Provides a relationship between the front and rear cornering stiffness‘
Lateral acceleration was measured on a 30.5 m radius circle at 6.7, 8.9, and 11.2 m/s
r
f
f
rus C
WCW
K
22
r
f
f
rus C
WCWK
22 fusr
ffr CKW
CWC
2
Dept. Of Mechanical and Nuclear Engineering, Penn State University
25/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Model Fitting Procedure Step 1 – Determine understeer gradient
Plotting additional steering angle vs. lateral acceleration, the understeer gradient is simply the slope of the line
y = 0.045x + 0.018R2 = 0.9965
0.024
0.026
0.028
0.03
0.032
0.034
0.036
0.125 0.175 0.225 0.275 0.325 0.375 0.425
Lat. Accel (g's)
Add
ition
al A
ngle
(rad
)
Dept. Of Mechanical and Nuclear Engineering, Penn State University
26/23 Vehicle Dynamic Modeling for the Prediction and Prevention of Vehicle Rollover
Analytical Vehicle ModelsPaper Model Order Method of validation Who are they withWilliams, 1995, Nonlinear control of roll moment distribution… NL 2DOF No roll dynamics included, only a "roll moment factor" Georgia Institute of TechnologyRosam, 1997, Development and simulation of a novel roll… ? No model or Free Body Diagram Given University of BathDarling, 1998, An Experimental Study of a Prototype… ? No model or Free Body Diagram Given University of BathFeng, 1998, Automatic Steering Control of Vehicle Lateral... 2 & 3DOF Errors in published formulation PATH
Feng, 2000, Decoupling Steering Control For Vehicles… 2 & 3DOF Errors in published formulation PATHKrishnaswami, 1998, A Regularization Approach To Robust… 2DOF Not enough information given UMTRIWielenga, 1999, A Method for Reducing On Road Rollover… 3DOF Model formulation not given DynomotiveChen, 1999, A Real Time Rollover ThreatIndex For SUV's coupled 2DOF Decoupled approach UMTRIChen, 2001, Differential Braking Based Rollover Prevention… 3DOF Parameters difficult to obtain UMTRIKitajima, 2000, Control For Integrated Side Slip Roll 8DOF*, 3DOF Equations complex, not enough information given UMTRIEger, 2003, Modeling of rollover sequences 2DOF Covers tripped rollovers University of Karlsruhe, GermanyKueperkoch, 2003, Novel Stability Control Using SBW… 3DOF Not relevant to our study Bosch CorporationRossetter, 2003, A Gentle Nudge Towards Safety… 2DOF Not relevant to our study StanfordTakano, 2003, Study on a vehicle dynamics model for… 3DOF Errors in published information Tokyo University of Ag. and Tech.Oh, 2004, The Design of a Controller for the SBW System 9DOF Model formulation not given Hyundai/Hanyang University
Paper Model Order Comments Who are they withSharp, 1993, On the design of an active control system for a… 3DOF Complex formulation, parameters are difficult to obtain Cranfield Institute of TechnologyChen, C, 1998, Steering Control of High-Speed Vehicles 2DOF Not relevant to our study PATHMammar, 1999, Speed Scheduled Vehicle Lateral Control 3DOF Nicely derived, but no experimental validation. Includes a Evry University, France
mathematical proof on its model matching abilities.Cole, 2000, Evaluation Of Design Alternatives For Roll Control… 3DOF Model is developed through a software package University of NottinghamHyun, 2000, Vehicle Modeling And Prediction Of… NL 8DOF Not relevant to our study Texas A&MIkenaga, 2000, Active Suspension Control Of Ground… 7DOF No description of lateral dynamics Texas ArlingtonManning, 2000, Coordination Of Chassis Control Systems NL 5DOF Not enough information given University of Leeds, UKKim, 2003, Investigation Of Robust Roll Motion Control… 3DOF Clean presentation, parameters given, model worked Samchok University, South KoreaSprague, 2002, Automated stability analysis of a vehicle… 6DOF Model formulation not given Exponent Failure Analysis AssociatesHuh, 2002, Monitoring System Design For Estimating... 4DOF No roll dynamics included, only lateral weight transfer Samchok University, South KoreaCarlson, 2003, Optimal rollover prevention with SBW and diff… NL4DOF, L3DOFAll work done in simulation Stanford
Models With Experimental Validation
Models Not Experimentally Validated