Transcript Slide 1

AUTO-CALIBRATION AND CONTROL
APPLIED TO ELECTRO-HYDRAULIC
VALVES
EXPERIMENTS ON HUSCO
BLUE TELEHANDLER
August 18, 2006
PATRICK OPDENBOSCH
Graduate Research Intern
INCOVA
(262) 513 4408
[email protected]
HUSCO International
W239 N218 Pewaukee Rd.
Waukesha, WI 53188-1638
MOTIVATION
8000
7000
US PATENT # 6,732,512 & 6,718,759
Flow Conductance, Kv [LPH/sqrtMPa]
HUSCO’S CONTROL TOPOLOGY
6000
5000
4000
3000
2000
1000
0
0
200
400
600
800
1000
Coil Current [mA]
1200
1400
1600
1800
Steady State Mapping (Design)
1800
1600
1400
Coil Current [mA]
1200
1000
800
600
400
200
0
Hierarchical control: System controller, pressure controller,
function controller
0
1000
2000
3000
4000
5000
Flow Conductance, Kv [LPH/sqrtMPa]
6000
7000
8000
Inverse Mapping (Control)
HUSCO OPEN LOOP CONTROL
FOR EHPV’s
2
MOTIVATION
Constant Temperature (T = 30 C)
100
Kv [(LPM)/sqrt(MPa)]
HUSCO’S CONTROL TOPOLOGY
120
80
60
40
20
US PATENT # 6,732,512 & 6,718,759
0
1.5
5
1
4
3
0.5
2
1
0
Input [A]
0
dP [MPa]
Steady State Mapping (Design)
T = 20 C
1.4
1.2
Input [A]
1
0.8
0.6
0.4
0.2
0
5
4
100
3
80
60
2
40
1
dP [MPa]
Hierarchical control: System controller, pressure controller,
function controller
3
20
0
0
Kv [(LPM)/sqrt(MPa)]
Inverse Mapping (Control)
MOTIVATION
Time
Commanded Kv
Actual Kv
Commanded Velocity
Actual Velocity
Time
4
MOTIVATION
Flow conductance online
estimation
 Accuracy
 Computation effort

120
Kv [(LPM)/sqrt(MPa)]
100
80
60
40
20
Online inverse flow
conductance mapping
learning and control
 Effects by input saturation and timevarying dynamics
 Maintain tracking error dynamics stable
while learning

Constant Temperature (T = 30 C)
0
1.5
5
1
4
3
0.5
2
1
0
Input [A]
0
dP [MPa]
T = 20 C
1.4
1.2
1
Input [A]

0.8
0.6
0.4
0.2
0
5
4
Fault diagnostics
100
3
80
60
2
40
1
dP [MPa]
 How can the learned mappings be used
for fault detection
5
20
0
0
Kv [(LPM)/sqrt(MPa)]
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
6
TOPIC REVIEW
PURDUE PAPERS
 Liu, S. and Yao, B., (2005), Automated modeling of
cartridge valve flow mapping, in Proc: IEEE/ASME
International Conference on Advanced Intelligent
Mechatronics pp. 789-794
 Liu, S. and Yao, B., (2005), On-board system
identification of systems with unknown input
nonlinearity and system parameters, in Proc: ASME
International Mechanical Engineering Congress and
Exposition
 Liu, S. and Yao, B., (2005), Sliding mode flow rate
observer design, in Proc: Sixth International
Conference on Fluid Power Transmission and
Control pp. 69-73
7
TOPIC REVIEW
CATERPILLAR PATENTS
 Aardema, J.A. and Koehler, D.W., (1999) System and method for
controlling an independent metering valve, U.S. Patent (5,960,695)
 Aardema, J.A. and Koehler, D.W., (1999) System and method for
controlling an independent metering valve, U.S. Patent (5,947,140)
 Kozaki, T., Ishikawa, H., Yasui, H., et al., (1991) Position control device
and automotive suspension system employing same, U.S. Patent
(5,004,264)
NEW PATENTS
 Reedy, J.T., Cone, R.D., Kloeppel, G.R., et al., (2006) Adaptive position
determining system for hydraulic cylinder, U.S. Patent (20060064971)
 Du, H., (2006) Hydraulic system health indicator, U.S. Patent
(7,043,975)
 Wear, J.A., Du, H., Ferkol, G.A., et al., (2006) Electrohydraulic control
system, U.S. Patent (20060095163)
8
TOPIC REVIEW
CATERPILLAR PATENTS
 20060064971 “Adaptive Position Determining System
for Hydraulic Cylinder”
Limit Switches
9
TOPIC REVIEW
CATERPILLAR PATENTS
Long-Jang Li, US
Patent 5,942,892
(1999)
 5,004,264 “Position Control Device and Automotive
Suspension System Employing Same”
Position Detector
10
TOPIC REVIEW
CATERPILLAR PATENTS
 20060095163 “Electrohydraulic Control System”
Position/Velocity
sensor
Adaptive scheme: no
details found
11
TOPIC REVIEW
CATERPILLAR PATENTS
 7,043,975 “Hydraulic System Health Indicator”
Using Lyapunov
stability theory
Health Monitoring using
Bulk modulus and other
model-based
parameters
(Position/velocity
sensor)
Based on pump pressure
discharge dynamics or cylinder
head end control pressure
12
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
13
SETUP
 MOTION CONTROL
 Independent coil current
control
 SIEMENS controller
 Supply & return pressure from
ISP Supply
KSA
KSB
KAR
HUSCO Blue Telehandler
KBR
Return
Boom Function
Boom Function Kinematics
14
SETUP
 MOTION CONTROL
 Independent coil current
control
 SIEMENS controller
 Supply & return pressure from
ISP
HUSCO Blue Telehandler
PS
Pump
KSA
KSB
Unloader
PA
Diesel
Engine
Relief
Valve
PR
PB
KAR
Filter
Tank
15
KBR
Boom
Cylinder
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
16
IMPROVEMENTS
 PUMP CONTROL
35
Psp
PS
PA
PB
PR
30
25
Ripples
Pressure override
for pump pressure
control (ISP code)
Pressure [MPa]
20
15
10
5
0
-5
0
2
4
6
8
10
Time [sec]
17
12
14
16
18
20
IMPROVEMENTS
DATA SHOWN: Margin added on retract
metering mode
(PB signal is user commanded, not
actual workport pressure)
 PUMP CONTROL
35
PSsp
PS
PR
PB
30
25
Current override
for unloader coil
current control
(ISP code)
Pressure [MPa]
20
15
10
5
0
-5
0
2
4
6
8
10
Time [sec]
18
12
14
16
18
20
IMPROVEMENTS
 ANTI-CAVITATION
INCOVA Parametric Valve Calculation Standard Metering Retract
8000
KOUT_MAX
PIN_MIN
7000
5000
4000
KIN_MAX
AR
K
Unconstrained
Operating Point
[LPH/sqrtMPa]
6000
3000
2000
Keq
1000
0
Constrained
Operating Point
POUT_MAX
0
1000
2000
20
3000
5000
4000
KSB [LPH/sqrtMPa]
6000
7000
8000
IMPROVEMENTS
 ANTI-CAVITATION
Position [mm]
800
Velocity [kph]
600
55
0.5
56
57
58
Time [sec]
59
60
61
0
-0.5
Pressure [MPa]
Kv [LPH/sqrt(MPa)]
Cavitation
700
Meas
Des
-1
55
15
10
5
2000
0
55
57
58
Time [sec]
59
60
61
56
57
58
Time [sec]
59
60
61
56
57
58
Time [sec]
59
60
61
PS
PA
PB
PR
0
55
6000
4000
56
A+
B+
AB-
21
900
70
Angle
Position [mm]
IMPROVEMENTS
750
55
600
40
450
25
300
10
150
-5
 ANTI-CAVITATION
0
0
2
4
6
8
10
Time [sec]
12
14
16
18
-20
20
250
Speed [mm/s]
150
Flow Sharing
50
-50
Vdes
Vcmd
Vmeas
-150
-250
0
2
4
6
8
10
Time [sec]
12
14
16
18
20
4
6
8
10
Time [sec]
12
14
16
18
20
35
Psp
PS
PA
PB
PR
30
25
Pressure [MPa]
20
15
10
5
No Cavitation
0
-5
0
2
22
Angle [deg]
Position
IMPROVEMENTS
 LEARNING
Supply
KSA
KSB
EXTEND
KAR
KBR
Return
Boom Function
23
IMPROVEMENTS
 LEARNING
Supply
KSA
KSB
RETRACT
KAR
KBR
Return
Boom Function
24
IMPROVEMENTS
 LEARNING
Supply
KSA
KSB
EXTEND/RETRACT
KAR
KBR
Return
Boom Function
25
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
26
MAPPING LEARNING & CONTROL
 LEARNING APPLIED TO NONLINEAR SYSTEM
K Î [0, K MAX ]
K k 1  F  K k , isol,k 
isol,k Î [0,1500mA]
 MAPPING TO BE LEARNED (simplified)
1800
1600
Coil Current Command [mA]
1400
1200
1000
Expected curve
shift
800
600
400
200
0
0
1000
2000
3000
4000
5000
Flow Conductance, Kv [LPH/sqrtMPa]
27
6000
7000
8000
MAPPING LEARNING & CONTROL
 LEARNING APPLIED TO NONLINEAR SYSTEM
K Î [0, K MAX ]
K k 1  F  K k , isol,k 
isol,k Î [0,1500mA]
 MAPPING TO BE LEARNED (simplified)
2000
Map
Grid
1800
1600
Coil Current [mA]
1400
1200
Expected curve
shift
1000
800
600
400
200
0
0
2000
4000
6000
8000
Flow Conductance, Kv [LPH/sqrtMPa]
28
10000
12000
MAPPING LEARNING & CONTROL
 LEARNING APPLIED TO NONLINEAR SYSTEM
K Î [0, K MAX ]
K k 1  F  K k , isol,k 
isol,k Î [0,1500mA]
 CONTROL DESIGN
 Tracking Error:
 Error Dynamics:
ek = K k -
d
Kk
 F  d Kk , d isol,k  
 F  d Kk , d isol,k  
 ek  
  isol,k  d isol,k   o  ek , k 
ek 1  




Kk
isol,k




Linear Time
Varying System
ek 1  d J k ek  d Qk  isol,k  d isol,k 
29
MAPPING LEARNING & CONTROL
 LEARNING APPLIED TO NONLINEAR SYSTEM
K Î [0, K MAX ]
K k 1  F  K k , isol,k 
 CONTROL DESIGN
 Error Dynamics:
ek 1  d J k ek  d Qk  isol,k  d isol,k 
 Deadbeat Control Law:
 Closed loop
isol,k = disol,k - dQk- 1 dJ kek
ek 1  0
30
isol,k Î [0,1500mA]
MAPPING LEARNING & CONTROL
 LEARNING APPLIED TO NONLINEAR SYSTEM
K Î [0, K MAX ]
K k 1  F  K k , isol,k 
isol,k Î [0,1500mA]
 CONTROL DESIGN
 Deadbeat Control Law:
isol,k = disol,k - dQk- 1 dJ kek
 Proposed Control Law:
isol,k = g (d K k )
isol,k   isol, k  isol, k   Q J k 1ek
1
k 1
31
%T F (d K )
D i%
=
W
sol,k
k
k
MAPPING LEARNING & CONTROL
Nominal
inverse
mapping
Inverse
Mapping
Correction
dK
V
icmd
Servo
NLPN
Adaptive
Proportional
Feedback
EHPV
Jacobian
Controllability
Estimation
32
KV
MAPPING LEARNING & CONTROL
 LEARNING APPLIED TO NONLINEAR SYSTEM
K Î [0, K MAX ]
K k 1  F  K k , isol,k 
isol,k Î [0,1500mA]
 CONTROL DESIGN
 Proposed Control Law:
isol,k = g (d K k )
isol,k   isol, k  isol, k   Q J k 1ek
1
k 1
 Closed loop
ek 1  d J k ek  d Qk


d
 i
sol, k
%T F (d K )
D i%
=
W
sol,k
k
k
 isol,k   Qk11 J k 1ek  d isol,k


J k  d Qk Qk11 J k 1 ek  d Qk  isol,k  isol,k  d isol,k 
33
MAPPING LEARNING & CONTROL
Parameter "a" Estimation
1
0.9
0.8
0.7
0.6
a
 IDENTIFICATION DESIGN
 Methods:
yk   Tˆ
 Least Squares (Recursive)
0.5
0.4
▫ Noise rejection
▫ Poor time varying parameter
tracking capabilities (add
covariance reset and forgetting
factor – dynamic or static)
▫ New research suggest variablelength moving window*
 Gradient Based
True
Broyden
RLS
RLS w/EW
0.3
0.2
0.1
0
0
1
2
3
4
5
Time [sec]
6
7
8
9
10
Parameter "a" Estimation
1.4
True
Broyden
RLS
RLS w/EW
1.2
1
a
0.8
▫ Sensitive to noise
▫ Better time varying parameter
tracking capabilities
▫ Gradient step size must be chosen
carefully
0.6
0.4
0.2
0
0
1
2
3
4
5
Time [sec]
6
7
8
9
10
Identification of time varying parameter for a linear system
(*) Jiang, J. and Zhang, Y. (2004), A Novel Variable-Length Sliding Window Blockwise
Least-Squares Algorithm for Online Estimation of Time-Varying Parameters, Intl. J.
Adaptive Ctrl & Signal Proc., Vol 18, No. 6, pp. 505-521.
34
MAPPING LEARNING & CONTROL
 IDENTIFICATION DESIGN
ek 1  d J k ek  d Qk  isol,k  d isol,k 
 Approximations:
 Previous-point Linearization
Kk
Kk  Kk 1
K k 1  J k*1K k  Qk*1  isol,k  isol, k 1 
 Stack Operator
S ( x)
 x
T
1
T
2
x
x  , where x   x1
T
n
T
35
x2
xn 
MAPPING LEARNING & CONTROL
 IDENTIFICATION DESIGN
ek 1  d J k ek  d Qk  isol,k  d isol,k 
 Approximations:
 Previous-point Linearization
Kk
Kk  Kk 1
K k 1  J k*1K k  Qk*1  isol,k  isol, k 1 
 Stack Operator Properties
S (x  z )  S  x   S  z 
S  xyz    z T  x  S  y 
36
MAPPING LEARNING & CONTROL
 IDENTIFICATION DESIGN
ek 1  d J k ek  d Qk  isol,k  d isol,k 
 Approximations:
 Previous-point Linearization
Kk
Kk  Kk 1
K k 1  J k*1K k  Qk*1  isol,k  isol, k 1 
 Stack Operator Properties
xz
 x11z


 xm1z
37
x1n z 


xmn z 
MAPPING LEARNING & CONTROL
 IDENTIFICATION DESIGN
ek 1  d J k ek  d Qk  isol,k  d isol,k 
 Approximations:
 Previous-point Linearization
K k 1  J k*1K k  Qk*1  isol,k  isol, k 1 

K k 1  S  J k*1K k   S Qk*1  isol,k  isol, k 1 


 S  I n J k*1K k   S I nQk*1  isol,k  isol, k 1 
  K  I n  S  J
T
k
  K kT  I n  |

*
k 1
  i
sol, k
i
sol, k


 isol, k 1   I n S  Qk*1 
T
 isol, k 1 
38
T

T
*
*

 I n  S  J k 1  | S  Qk 1  

MAPPING LEARNING & CONTROL
 IDENTIFICATION DESIGN
ek 1  d J k ek  d Qk  isol,k  d isol,k 
 Approximations:
 Previous-point Linearization
K k 1  J k*1K k  Qk*1  isol,k  isol, k 1 
K k 1   K kT  I n  |

i
sol, k
 isol,k 1 
T

T
*
*

 I n  S  J k 1  | S  Qk 1  

yk   Tˆ
How are (dJ,dQ) and (J*,Q*) related?
39
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
40
EXPERIMENTAL RESULTS
Nominal
inverse
mapping
icmd
Servo
dK
V
EHPV
1800
1600
Coil Current Command [mA]
1400
1200
1000
800
600
400
Every valve uses a
generic Table
200
0
0
1000
2000
3000
4000
5000
Flow Conductance, Kv [LPH/sqrtMPa]
6000
7000
8000
41
KV
EXPERIMENTAL RESULTS
 PUMP CONTROL: MARGIN
900
70
Angle
750
55
600
40
450
25
300
10
150
-5
0
0
2
4
6
8
10
Time [sec]
12
14
16
18
-20
20
250
Vdes
Vcmd
Vmeas
Speed [mm/s]
150
50
-50
-150
-250
0
2
4
6
8
10
Time [sec]
42
12
14
16
18
20
Angle [deg]
Position [mm]
Position
EXPERIMENTAL RESULTS
35
Psp
PS
PA
PB
PR
30
25
Pressure [MPa]
20
15
10
5
0
-5
0
2
4
6
8
10
Time [sec]
43
12
14
16
18
20
EXPERIMENTAL RESULTS
8000
8000
KSAc
KSAm
6000
5000
4000
3000
2000
1000
0
-1000
0
5
10
Time [sec]
15
5000
4000
3000
2000
1000
-1000
20
0
5
10
Time [sec]
15
20
8000
KARc
KARm
7000
6000
KBRc
KBRm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
6000
0
8000
5000
4000
3000
2000
1000
0
-1000
KSBc
KSBm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
7000
6000
5000
4000
3000
2000
1000
0
0
5
10
Time [sec]
15
-1000
20
44
0
5
10
Time [sec]
15
20
EXPERIMENTAL RESULTS
 PUMP CONTROL: PS_SETPOINT
900
70
Angle
750
55
600
40
450
25
300
10
150
-5
0
0
2
4
6
8
10
Time [sec]
12
14
16
18
-20
20
250
Vdes
Vcmd
Vmeas
Speed [mm/s]
150
50
-50
-150
-250
0
2
4
6
8
10
Time [sec]
45
12
14
16
18
20
Angle [deg]
Position [mm]
Position
EXPERIMENTAL RESULTS
35
Psp
PS
PA
PB
PR
30
25
Pressure [MPa]
20
15
10
5
0
-5
0
2
4
6
8
10
Time [sec]
46
12
14
16
18
20
EXPERIMENTAL RESULTS
8000
8000
KSAc
KSAm
6000
5000
4000
3000
2000
1000
0
-1000
0
5
10
Time [sec]
15
5000
4000
3000
2000
1000
-1000
20
0
5
10
Time [sec]
15
20
8000
KARc
KARm
7000
6000
KBRc
KBRm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
6000
0
8000
5000
4000
3000
2000
1000
0
-1000
KSBc
KSBm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
7000
6000
5000
4000
3000
2000
1000
0
0
5
10
Time [sec]
15
-1000
20
47
0
5
10
Time [sec]
15
20
EXPERIMENTAL RESULTS
Nominal
inverse
mapping
Inverse
Mapping
Correction
dK
V
icmd
Servo
NLPN
48
EHPV
KV
EXPERIMENTAL RESULTS
 PUMP CONTROL: MARGIN
900
70
Angle
750
55
600
40
450
25
300
10
150
-5
0
0
2
4
6
8
10
Time [sec]
12
14
16
18
-20
20
250
Vdes
Vcmd
Vmeas
Speed [mm/s]
150
50
-50
-150
-250
0
2
4
6
8
10
Time [sec]
49
12
14
16
18
20
Angle [deg]
Position [mm]
Position
EXPERIMENTAL RESULTS
35
Psp
PS
PA
PB
PR
30
25
Pressure [MPa]
20
15
10
5
0
-5
0
2
4
6
8
10
Time [sec]
50
12
14
16
18
20
EXPERIMENTAL RESULTS
8000
8000
KSAc
KSAm
6000
5000
4000
3000
2000
1000
0
-1000
0
5
10
Time [sec]
15
5000
4000
3000
2000
1000
-1000
20
0
5
10
Time [sec]
15
20
8000
KARc
KARm
7000
6000
KBRc
KBRm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
6000
0
8000
5000
4000
3000
2000
1000
0
-1000
KSBc
KSBm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
7000
6000
5000
4000
3000
2000
1000
0
0
5
10
Time [sec]
15
-1000
20
51
0
5
10
Time [sec]
15
20
EXPERIMENTAL RESULTS
 PUMP CONTROL: PS_SETPOINT
900
70
Angle
750
55
600
40
450
25
300
10
150
-5
0
0
2
4
6
8
10
Time [sec]
12
14
16
18
-20
20
250
Vdes
Vcmd
Vmeas
Speed [mm/s]
150
50
-50
-150
-250
0
2
4
6
8
10
Time [sec]
55
12
14
16
18
20
Angle [deg]
Position [mm]
Position
EXPERIMENTAL RESULTS
35
Psp
PS
PA
PB
PR
30
25
Pressure [MPa]
20
15
10
5
0
-5
0
2
4
6
8
10
Time [sec]
56
12
14
16
18
20
EXPERIMENTAL RESULTS
8000
8000
KSAc
KSAm
6000
5000
4000
3000
2000
1000
0
-1000
0
5
10
Time [sec]
15
5000
4000
3000
2000
1000
-1000
20
0
5
10
Time [sec]
15
20
8000
KARc
KARm
7000
6000
KBRc
KBRm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
6000
0
8000
5000
4000
3000
2000
1000
0
-1000
KSBc
KSBm
7000
Kv [LPH/sqrt(MPa)]
Kv [LPH/sqrt(MPa)]
7000
6000
5000
4000
3000
2000
1000
0
0
5
10
Time [sec]
15
-1000
20
57
0
5
10
Time [sec]
15
20
EXPERIMENTAL RESULTS
Nominal
inverse
mapping
Inverse
Mapping
Correction
dK
V
icmd
Servo
NLPN
FIXED
Proportional
Feedback
58
EHPV
KV
EXPERIMENTAL RESULTS
 PUMP CONTROL: MARGIN
70
w/ FB
w/o FB
65
60
55
Angle [deg]
50
45
40
35
30
25
20
15
0
10
20
30
Time [sec]
59
40
50
60
EXPERIMENTAL RESULTS
80
60
40
Speed [mm/s]
20
0
-20
-40
-60
-80
Vcmd
Vm w/ FB
Vm w/o FB
0
10
20
30
Time [sec]
60
40
50
60
EXPERIMENTAL RESULTS
Pressure Responses w/FB
30
Psp
PS
PA
PB
PR
25
Pressure [MPa]
20
15
10
5
0
0
10
20
30
Time [sec]
61
40
50
60
EXPERIMENTAL RESULTS
Pressure Responses w/o FB
30
Psp
PS
PA
PB
PR
25
Pressure [MPa]
20
15
10
5
0
0
10
20
30
Time [sec]
62
40
50
60
EXPERIMENTAL RESULTS
RED w/FB , BLUE w/o FB
3000
KSAc
KSAm
KSAc
KSAm
2500
Kv [LPH/sqrt(MPa)]
2000
1500
1000
500
0
0
10
20
30
Time [sec]
63
40
50
60
EXPERIMENTAL RESULTS
RED w/FB , BLUE w/o FB
3000
KARc
KARm
KARc
KARm
2500
Kv [LPH/sqrt(MPa)]
2000
1500
1000
500
0
0
10
20
30
Time [sec]
64
40
50
60
EXPERIMENTAL RESULTS
RED w/FB , BLUE w/o FB
3000
KBRc
KBRm
KBRc
KBRm
2500
Kv [LPH/sqrt(MPa)]
2000
1500
1000
500
0
0
10
20
30
Time [sec]
65
40
50
60
EXPERIMENTAL RESULTS
RED w/FB , BLUE w/o FB
3000
KSBc
KSBm
KSBc
KSBm
2500
Kv [LPH/sqrt(MPa)]
2000
1500
1000
500
0
0
10
20
30
Time [sec]
66
40
50
60
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
68
FUTURE WORK
 Improve EHPV performance using adaptive proportional
feedback
 Study convergence properties of adaptive proportional
input and its impact on overall stability
 Incorporate fault Diagnostics capabilities along with
mapping learning
 Refine pump controls
69
PRESENTATION OUTLINE








MOTIVATION
TOPIC REVIEW
SETUP
IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
70
CONCLUSIONS
 The performance of the INCOVA control system under
Ps_setpoint and margin pump control was improved
when using mapping learning as oppose to using fixed
inverse valve opening mapping.
 Satisfactory experimental results were obtained on
applying feedforward learning and fixed proportional
control to four (4) EHPVs
 Experimental verification of improved commanded
velocity achievement using mapping learning was
presented
 The need for good velocity sensor was observed
(potential idea for customized sensor was presented)
71
CONCLUSIONS
 More refined code (constraints) allowed better control
 Unresolved Issues still exist with parameter estimation
and adaptive proportional control portion
 Experimental validation of faster mapping learning with
proportional feedback in place (fixed)
 Learning grid can be fixed based on curve shifting
behavior
72
QUESTIONS?
73