modelling a racing driver
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Modelling a racing driver. Robin Sharp Visiting Professor University of Surrey. Partners. Dr Simos Evangelou (Imperial College) Mark Thommyppillai (Imperial College) Robin Gearing (Williams F1). Published work. - PowerPoint PPT PresentationTRANSCRIPT
Modelling a racing driver
Robin Sharp
Visiting Professor
University of Surrey
Partners
• Dr Simos Evangelou (Imperial College)
• Mark Thommyppillai (Imperial College)
• Robin Gearing (Williams F1)
Published work• R. S. Sharp and V. Valtetsiotis, Optimal preview car steering control, ICTAM Selected
Papers from 20th Int. Cong. (P. Lugner and K. Hedrick eds), supplement to VSD 35, 2001, 101-117.
• R. S. Sharp, Driver steering control and a new perspective on car handling qualities, Journal of Mechanical Engineering Science, Proc. I. Mech. E., 219(C8), 2005, 1041-1051.
• R. S. Sharp, Optimal linear time-invariant preview steering control for motorcycles, The Dynamics of Vehicles on Roads and on Tracks (S. Bruni and G. Mastinu eds), supplement to VSD 44, Taylor and Francis (London), 2006, 329-340.
• R. S. Sharp, Motorcycle steering control by road preview, Trans. ASME, Journal of Dynamic Systems, Measurement and Control, 129(4), 2007, 373-381.
• R. S. Sharp, Optimal preview speed-tracking control for motorcycles, Multibody System Dynamics, 18(3), 397-411, 2007.
• R. S. Sharp, Application of optimal preview control to speed tracking of road vehicles, Journal of Mechanical Engineering Science, Proc. I. Mech. E., Part C, 221(12), 2007, 1571-1578.
• M. Thommyppillai, S. Evangelou and R. S. Sharp, Car driving at the limit by adaptive linear optimal preview control, Vehicle System Dynamics, in press, 2009.
Objectives
• Enable manoeuvre-based simulations
• Understand man-machine interactions
• Perfect virtual driver– able to fully exploit a virtual racecar– real-time performance
• Find best performance
• Find what limits performance
• Understand matching of car to circuit
Strategy
• Specify racing line and speed – (x, y, t) (x, y) gives the racing line, t the speed
• Track the demand with a high-quality tracking controller
• Continuously identify the vehicle
• Modify the t-array and iterate to find limit
Optimal tracking
• Linear Quadratic Regulator (LQR) control with preview– linear constant coefficient plant– discrete-time car model– road model by shift register (delay line)– join vehicle and road through cost function– specify weights for performance and control– apply LQR software
Close-up of car and road with sampling
car
yO x
roadyr0
yr1yr2
yr3yr4
uT
current road angle = (yr1-yr0)/(uT)
speed, u; time step, T
K21
K22
K2q
car state feedbackcar states
path yr1
path yr2
path yrq
steer angle
command
K11
K12
K13
K14
Optimal controls from Preview LQR
shift register state feedback
Discrete-time control scheme
xdem
ydem
car linearised for operation near to a trim state
K1
K2
car states
xc
yc
shift register; n = 14
throttlesteer
c
+- to cost function
+- to cost function
Minimal car modelx
ab
Fylf
FyrfFyrr
y
0
Fylr
Mass M; Inertia Iz
u, constant
v
2w
inertial system
0 10 20 30 40 50 60 70 80 90 100
-0.2
-0.1
0
0.1
0.2
prev
iew
gai
n va
lue
10 m/s20 m/s 30 m/s 40 m/s 50 m/s
q1 = 100, q2 = 0
0 10 20 30 40 50 60 70 80 90 100
-0.6
-0.4
-0.2
0
0.2
distance ahead, m
prev
iew
gai
n va
lue 10
20 30 4050
q1 = 100, q2 = 0
Buick
Ferrari
K2 (preview) gains for saloon and sports cars
0 50 100 150 200 250 300 350
-20
-10
0-y
coo
rdin
ate,
m
dotted; carsolid; road
0 50 100 150 200 250 300 350
05
1015
attit
ude,
deg
dotted; carsolid; road
0 50 100 150 200 250 300 350
0
10
20
stee
r, d
eg
0 50 100 150 200 250 300 350
02468
x coordinate, m
lata
cc,
m/s
/s
The rally car (1)
Tyre-force saturation
• Saturating nonlinearity of real car
• Optimal race car control idea
• Trim states and linearisation for small perturbations
• Storage and retrieval of gain sets
• Adaptive control by gain scheduling
car model tyre forces
)()( yrrylryrfylfz FFbFFarI
0 0.05 0.1 0.15 0.2 0.25 0.30
1000
2000
3000
4000
lateral slip ratio
late
ral f
orc
e, N
Tyre lateral force by Magic Formula
yrrylryrfylf FFFFurvM )(
)))arctan((arctan(sin(2 BBEBCDFy
,
Equilibrium states of front-heavy car
0 0.05 0.1 0.15 0.2 0.25 0.30
0.5
1
1.5
2
lateral slip ratio
Fy
/ axl
e w
eig
ht
a < b, understeer onlyRear axle
Front axle
unique rear slip for given front slip
Axle lateral force / axle weight
decreasing turn radius for fixed speed
Front tyre side slip angle (Rad)Preview length (s)
Optimal preview gain sequences as functions of front axle sideslip ratio
Frequency responses
0 2 4 6 8 10 12 14 16 18 20-10
-8
-6
-4
-2
0
2
4
6
8
10
IC
xinput
previous input stored in shift register
Perfect tracking requires:
unity gain
phase lag equal to transport lag
For cornering, trim involves circular datum
datum line
Controlled car frequency responses
10-3
10-2
10-1
100
101
-25
-20
-15
-10
-5
0
Bode plot for Front tyre side slip for a speed of 30 m/s
10-3
10-2
10-1
100
101
-3000
-2500
-2000
-1500
-1000
-500
0Phase plot for Front tyre side slip for a speed of 30 m/s
Frequency (Hz)
Front tyre side slip= 0Front tyre side slip= 0.040057Front tyre side slip= 0.06009Front tyre side slip= 0.38064
ydem4 from curved reference line
ydem3 from curved reference line
IC
reference line for straight-running trim state
reference line for cornering trim state
road path
ydem2
ydem3
ydem4
ydem1
Small perturbations from trimpath tangent for cornering trim state
Tracking runs of simple car at 30m/s(Fixed gain vs. Gain scheduled)
1
1
1
2
3
4
2
2
3
3
4
4
Fixed gain Gain scheduled
Conclusions
• Optimal preview controls found for cornering trim states
• Gain scheduling applied to nonlinear tracking problem
• Effectiveness demonstrated in simple application
• Rear-heavy car studied similarly
• Identification and learning work under way