gastcollege han master control systems engineering
DESCRIPTION
Saskia Monsma gaf een gastcollege bij de HAN in het kader van de opleiding Master of Control Systems Engineering. De video van het college is hier te bekijken: http://www.hansonexperience.com/my_weblog/2009/05/liveblog_master_of_control_systems_engineering.htmlTRANSCRIPT
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Driver Models for Tyre Testing:Why and How?
Master Control Systems Engineering
27 May 2009
Ir. Saskia Monsma
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Overview
� Introduction
� Research project
� Driver modelling
� Simulation study
� Experiments
� Conclusions & Follow Up
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Introduction
� Researcher at Mobility Technology research & lecturer for Automotive engineering
� PhD-research: How to improve assessment methodsto judge driver-vehicle handlingin relationship with tyre characteristics?
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Handling, tyre characteristics
� Handling: cornering behaviour+ the driver’s perception
� Tyre characteristics
Tyre characteristics
Construction compound
ply-type
carcass
belt
Dimensionaspect ratio
size
Servicetemperature
wet/dry conditions
Inner pressure
Performance
aligning torque
cornering stiffness
pneumatic trail
peak lateral force coefficient
braking force
coefficient
Aging
wear-in
wear after normal use
slip angle α
V
Fy
0 5 10 15 (deg)
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Relation:Tyre Characteristics ���� Driver-Vehicle Handling is not straightforward
� Many different tyre parameters
� There is a lot between tyre characteristics and vehicle performance…
steer by wire
(active)
suspen
sion
electronic stability control
anti-lock bra
king system
traction contro
l
advanced driver assist
system
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Relation:Tyre Characteristics ���� Driver-Vehicle Handling is not straightforward
� Many different tyre parameters
� There is a lot between tyre characteristics and vehicle handling…
� Vehicle handling performance needs to be ‘translated’ into tyre characteristics
� What is good driver-vehicle handling?
– Subjective (depends on person, brand of vehicle, etc. )
– Depends on drivers mental workload and control effort
� How to judge driver-vehicle handling?
���� different assessment methods
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Assessment Methods to judge(Driver-)Vehicle Handling (1)
� Objective vehicle tests
– Driver = steering machine
– characteristic data (e.g., response times, overshoot, bandwidth,..)
� Subjective rating
– Controllability, steerability, etc.
– Questions, statements: agree/disagree
� Closed loop achievement
– Driver must perform task as best as he can
– Circuit, (double) lane change on max. speed, elk-test, slalom on max. speed, etc.
Real life testing
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Assessment Methods to judge(Driver-)Vehicle Handling (2)
� Workload measures– Driver performs a certain task (manoeuvre, sec. task)
– Steering Reversal Rate, High Frequency Area, Time to Line Crossing
� Combined primary and secondary task performance– Driver performs primary and secondary task (improve task)
– Performance on primary and/or secondary task
� Restriction of driver input– limited vision (glasses), driver decides for opening/closing
– task performance and frequency of opening/closing
� Physiological output– Muscle tension, blood pressure, heart rate variability
Real life testing
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Assessment Methods to judge(Driver-)Vehicle Handling (3)
= Simulating vehicle behaviour according to the procedures as prescribed in test protocols
– open loop: vehicle + tyres
– closed loop: vehicle + tyres + driver
� Advantage: optimisation of vehicle + tyres behaviour before the vehicle is built
� Used by vehicle manufacturers and by automotive suppliers
Virtual testing
drivermodels
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Driver Modelling
� In objective tests: driver = “steering machine”
� In subjective test: driver = “black box”
���� Driver model for opening the “black box”
Analysis gives further understanding of the relation: Tyre Characteristics ���� Driver-Vehicle Handling
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Research Topics
1. Driver models (professional test driver)
2. Drivers mental workload and control effort measures
3. Neural networks for the assessment of driver judgement and control of vehicle performance
4. Design of assessment tools(based on and refining research topics 1-3)
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Driver-Vehicle System Model
requiredtrajectory
roadconditions
driver
steeringcontrol
throttlebrake
vehicle
road air
deviation from path, in orientation, following time, distance,..
vibrations, noise,…
disturbances
Closed-loop system
Open-loop system
action
perception
action
perception
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Human behaviour and driving tasks
� There are many different driver models for different driver behaviour
– Provide insights into basic properties of human performance– Predict the performance of the driver-vehicle system
(stability)– Driver assistance systems
SRK-model for human behaviour (Rasmussen)
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DARPA Urban Challenge
� Vehicles with no driver and no remote control
� 60 miles urban area course with traffic
� Obeying all traffic regulations
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Model the Driver
requiredtrajectory
roadconditions
driver
steeringcontrol
throttlebrake
vehicle
road air
deviation from path, in orientation, following time, distance,..
vibrations, noise,…
disturbances
modelled withlinear differential equations
also?
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Model the Human Controller
� Describing functions (= approximate transfer functions) of human performance using “control language”
� Can you model human performance by linear models?– Thresholds
– Detect and remember patterns
– Learn and adapt
����Yes, with a quasi-linear model and with– Stationary tracking task by highly trained
controllers
– Unpredictable input
non-linear
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Quasi-Linear Model of the Human Controller
� YH = linear transfer function
� u(t) = linear response
� n(t) = internal noise (perceptual and motor system,
uncorrelated with input signal)
� u’(t) = quasi linear response
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Adaptive Nature of the Driver
� Drivers can adapt to changing vehicle behaviour
– although vehicle behaviour changes, overall driver-vehicle performance can remain the same
� Drivers can sense small differences in handling behaviour
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Relation with Mental Workload
���� Primary task performance measures will only be sensitive in regions D and B, not in A1, A2, A3. Most self report measures are sensitive in all but A2
boredom, loss of situation awareness and reduced alertness
overloaded
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YH(jω)
McRuer Crossover Model
limitations of the human
reaction time
neuromuscular lag
adjusted to achieve good control
gain
lead
lag
YH
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Simulation study
� Will the driver adapt his parameters for different tyres?
� Path tracking
path
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Simulation study models
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Optimisation of driver controller gains
� Based on minimisation of cost function:
J = ∫(current path error)2 + weight * ∫(steer workload)2
� Parameters:– Preview time = 1.5s
– Weight = 1
– V = 25m/s
– Path:
0 100 200 300 400 500 600 700 800 9000
100
200
x
yCurrent defined path
= steer speed=d(steer angle)/dt
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Different tyre characteristics:cornering stiffness
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Simulation with two virtual drivers
� Driver controller gains are optimised(based on cost function) for reference tyre characteristic (= reference driver gains)
� Simulations with different tyre characteristics for two virtual drivers
– non adaptive driver (with reference driver gains: )
– adaptive driver (with - for each different tyre characteristic - optimised driver gains)
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Errors non adaptive driver
0 5 10 15 20 25 30 35 40 45-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8lateral current error versus time
time(s)
late
ral cu
rren
t err
or
(m)
0 5 10 15 20 25 30 35 40 45
-10
-5
0
5
10
steer speed versus time
time(s)ste
er
sp
eed
(deg
/s)
Cornering stiffness 80%
Cornering stiffness 90%
Cornering stiffness 100% (reference)
Cornering stiffness 110%
Cornering stiffness 120%
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Errors adaptive driver
0 5 10 15 20 25 30 35 40 45-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8lateral current error versus time
time(s)
late
ral cu
rren
t err
or
(m)
0 5 10 15 20 25 30 35 40 45
-10
-5
0
5
10
steer speed versus time
time(s)ste
er
sp
eed
(deg
/s)
Cornering stiffness 80%
Cornering stiffness 90%
Cornering stiffness 100% (reference)
Cornering stiffness 110%
Cornering stiffness 120%
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Results non adaptive driver
0.0440.66
Cost function for different tyre characteristics
0%
50%
100%
150%
200%
250%
300%
350%
80% 90% 100% 110% 120%Cornering stiffness
J
sqr(current path error)
weight*sqr(steer workload)
Human controller gains versus
different tyre characterisitics
60%
70%
80%
90%
100%
110%
120%
130%
140%
80% 90% 100% 110% 120%
Cornering stiffness
Preview path error
gain (%)
Preview orientation
error gain (%)
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Results adaptive driver
Human controller gains versus
different tyre characterisitics
60%
70%
80%
90%
100%
110%
120%
130%
140%
80% 90% 100% 110% 120%
Cornering stiffness
Preview path error
gain (%)Preview orientation
error gain (%)
Cost function for different tyre characteristics
0%
50%
100%
150%
200%
250%
300%
350%
80% 90% 100% 110% 120%
Cornering stiffnessJ
sqr(current path error)
weight*sqr(steer workload)
0.0440.66
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Objectives experiments
� More Understanding on Subjective Evaluation
1. Correlation between objective criteria and subjective evaluation
2. Experimental derived workload measures (control effort, mental workload)
3. Evaluation of driver model parameters accounting for subjective evaluation
� Also
– New test vehicle
– Testing of driver measurements
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Experiments
� Same tests are performed with different tyres
– keeping driver, vehicle and environment as constant as possible ���� differences related to the tyres
– keeping tyres, vehicle and environment as constant as possible ���� differences related to the driver
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Experiments: Set Up
� Test vehicle + measurements
– Vehicle dynamics (x,y,z: velocities,
accelerations, angles, angl.vel.,)
– Steering wheel (steering angle,
steering angle velocity, moment)
� Two professional tyre test drivers
� Driver measurements
– Camera’s
– Heart beat
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Test Track: Test Centre Lelystad
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Experiments: Tyres
� Choice basedon expectedhandling behaviour
� Measured
slip angle [°]
Late
ral
forc
e [
N]
winter all season summer
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Experiments: Content
� Objective tests (ISO-standards):steady state circle, step steer, puls steer
– (10-20 repetitions of each driver-tyre combination)
� Subjective evaluation
– “Mini circuit”on highest possible speed
– “blind” testing in badges:1,2,3 / 2,3,4 / 5,6
– 9 evaluation aspects+ overall judgement
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Subjective evaluation aspects
� Steering precision while cornering
� Stability while cornering (no throttle change)
� Stability while cornering (throttle change)
� Yaw overshoot
� Predictability
� Yaw delay
� Steering angle
� Grip
� Controllability
� Overall judgment
Comment
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Test week impression
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Results Overall Judgement
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Influence Tyres on Evaluation Aspects
–� Yaw delay
+� Steering precision
� Stability while cornering (no throttle change)
� Grip
� Steering angle
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Correlation Objective Measurements with Subjective Evaluation
� Step steer response time for lateral acceleration
(time delay between 50% steering angle and 90% steady state value)
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Correlation Objective Measurements with Subjective Evaluation
� Step steer response time for lateral acceleration
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Results puls steer: bandwidth yaw rate
tyre in non linear range?
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Workload Measure: High Frequency Area
Indicator for workload: High Frequency Areaarea beneath curve fcut-flimit
area beneath curve 0-fcut
HFA =
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Results High Frequency Area
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Model BasedDriver Parameter Assessment
� Two-track model of test vehicle including lateral load transfer
� Tyre model: Magic Formula
� Driver tracking control models
Kd
prev .1
1.
τε
δ
+−=
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Optimisation ofDriver Model Parameters Ld and Kd
� Cost functional for optimising driver model parameters Ld and Kd for the different tyres
� Small variation in Ld and Kd
in contrast to non-extreme conditions!(Monsma: Tyre Technology Int., Annual Review, 2008)
( ) ( ) dtwdtFC ...2
2
∫ ∫+= δε δ�
path error weight factorsteering rate
tracking performance workload
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Conclusions & Follow Up
� HFA as workload measurement is promising for correlation with subjective evaluation
� Investigation of mental workload for extreme manoeuvring (heart rate measurements, video)
� Driver model parameter adjustment is limited in extreme manoeuvring conditions in contrast to non-extreme conditions.
� Explore driver parameter adjustment for relation:non–extreme conditions ���� subjective evaluation
� Workload measurements (and modelling)
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Videos test drivers