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 Qk11 J k 1ek d isol,k
J k d Qk Qk11 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 Tˆ
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*1K 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*1K 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*1K k Qk*1 isol,k isol, k 1
Stack Operator Properties
xz
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*1K k Qk*1 isol,k isol, k 1
K k 1 S J k*1K k S Qk*1 isol,k isol, k 1
S I n J k*1K 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*1K 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 Tˆ
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