Transcript EHPV Technology Electro-Hydraulic Valves Auto-Calibration and Control Applied to GOALS

EHPV Technology
Auto-Calibration and Control Applied to Electro-Hydraulic Valves
by Patrick Opdenbosch
PUMP PRESSURE CONTROL
PSdes
PS
PR
atm
2
1
i_COIL
PID Response: Manual Setting at 1000psi & KI = 30 KP = 350
sw2
DISCRETE PID uk
1
1
1
ek
isol
isol_m
isol
1
5
12
isolm
EHPV CONTROL
10
Pdes
4
1
Setup
PR
Target Scope
Id: 4
1
MEASUREMENTS PR
err
6
PSdes
PS
PR
atm
3
2
1
1300
Controller Implementation (SIMULINK/XPC-Target)
0
1000
SE
T
800
PR
E
SS
UR
E
400
[P
SI
500
600
700
900
800
0
1400 1500
1200 1300
1000 1100
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Time [sec]
Kv [LPH/sqrt(MPa)]
NU
AL
Closed-loop Tracking Response
COIL CURRENT [mA]
]
Steady State Data used for Feedforward Compensation
100
90
80
10
9
8
70
60
7
6
50
40
30
5
4
3
Kv
Kvd
Vsol
20
10
0
FIXED INCOVA CONTROL
0
0.5
1
1.5
2
2.5
Time [sec]
3
100
Pressure [MPa]
-50
1000
800
800
600
400
200
0
5
3.5
4
10
0
0
2
0.5
2
4
6
Time [sec]
8
Temperature [C]
350
1000
800
800
0.9
80
60
340
0.6
40
25
Position [mm]
320
8
0
0.5
1
1.5
2
2.5
Time [sec]
310
0.375
0.94
0.25
0.92
0.125
0.9
0
0.5
1
1.5
2
2.5
Time [sec]
3
3.5
4
0
5
4.5
800
ADAPTIVE Retract Control: Pump Margin Pressure Control
10
Desired
Actual
9
8
7
20
0
3
300
10
0.5
Q
0.96
3.5
4
0
5
4.5
-20
-40
0.5
1
1.5
2
2.5
Time [sec]
3
3.5
4
4.5
-100
5
PSET
PS
PA
PB
PR
ATM
4
0.5
1
1.5
2
2.5
Time [sec]
3
3.5
4
4.5
0
5
220
100
200
80
200
0
1
0
0.5
1
1.5
2
2.5
Time [sec]
3
3.5
4
4.5
System Pressures
2
3
Time [sec]
4
5
400
200
0
1
2
3
Time [sec]
4
5
0
1
2
3
Time [sec]
4
5
200
100
50
0
-50
600
0
5
150
1
0
400
5
2
-80
800
600
0
3
-60
0
6
0
1
2
3
Time [sec]
4
5
150
100
50
0
-50
Input Currents to EHPV Solenoids
Valve motion
Desired
Actual
4
6
Time [sec]
5
J
 Learning on 4 EHPV for piston motion control
2
4.5
0.98
0.3
330
200
1000
1.2
100
30
20
4
0.625
Piston’s Position Closed Loop Response Piston’s Velocity Closed Loop Response
0
3.5
0.75
ADAPTIVE Retract Control: Pump Margin Pressure Control
35
360
400
0
3
1.5
ADAPTIVE Retract Control: Pump Margin Pressure Control
40
370
600
10
2.5
Time [sec]
Single EHPV Trajectory Error Parameter Estimation
dP
60
1
-250
0
0
1
2
3
4
5
Time [sec]
6
7
8
9
10
0
1
2
3
4
5
Time [sec]
6
7
8
9
400
200
0
10
0
2
4
6
Time [sec]
8
600
400
200
0
10
Velocity [mm/s]
-200
600
Position [mm]
A- Input Current [mA]
2
-150
160
140
120
Piston Speed Response
0
2
4
6
Time [sec]
8
10
0
Low
Pressure
-20
-40
Input Solenoid Currents to EHPV’s
System Pressures Response
20
100
80
-60
60
40
High
Pressure
-80
0
0.5
1
1.5
2
2.5
Time [sec]
3
3.5
4
4.5
-100
5
0
0.5
1
1.5
2
2.5
Time [sec]
3
3.5
4
4.5
Piston motion
5
Piston’s Position Closed Loop Response Piston’s Velocity Closed Loop Response
FLOW CONDUCTANCE OBSERVER
æ¶ r
+ AAx )çç
çè¶ P
ö
é
æ¶ r
dPA
÷
&
ç
ê
&
÷
=
r
K
P
P
Q
A
x
T
v
+
A
x
( A0
(
A
S
A
l
A )
A )ç
÷
ê
÷
è¶ T
A øT dt
ë
HYDRAULIC TESTBED
ù
ö
÷
ú
÷
÷
øP ú
û
Wheatstone Bridge Arrangement
Electronics
Wheatstone Bridge
Assembly (EHPV)
PS
Supply
Piston Dynamics
&= u + d x&
EHPV Wheatstone Bridge Arrangement for Motion Control
FL
Workport Pressure Dynamics
1
m
u @ m1 (PAAA - PBAB - fˆf )
FL
d @-
1
m
fˆf = l 1 + l 3PA - l 4PB
Df
PB
AB
x
Pump
QBKvB-
M
KvA+
PB
A Ax&= K A PS - PA @ K A h
&= AA (u + d) = K&A h + K A h&
AAx&
KA
AA
PA
PA
KvP
KvB+
PS
QB
KvT
1000
ˆ T F&(x )h + W
ˆ T F (x )h&= A uˆ
W
A
KvAPR
1500
OBSERVER:
Return
QA+
ff
Ql
Resulting System
QB+
QA-
10
QA
QL
500
5
FL=0
1000
-120
-105
-90
-75
-60
-45
-30
-15
800
0
15
30
45
60
75
90
105
120
-500
600
-1000
PA
0
135
Estimation Error [N]
-135
Force [kN]
-150
PB
Tank
0
Actual
Estimated
Flow Conductance [LPH/sqrtMPa]
1.5
40
3
(v A0
1
1
0
5
4.5
Temp
45
180
4
-100
PROJECT TASKS
1000
B- Input Current [mA]
Velocity [mm/s]
6
1200
B+ Input Current [mA]
7
A+ Input Current [mA]
PUMP PRESSURE
MARGIN CONTROL
PSET
PS
PA
PB
PR
ATM
8
0
20
Learning on 2 EHPV for piston motion control
270
9
Desired
Actual
50
30
1.8
280
150
40
Single EHPV Flow Conductance Closed Loop Response
290
200
50
1.02
2
1
50
 Single EHPV for pump control + 4 EHPV for piston motion control
 Pressure Feedback to INCOVA logic
 Open loop valve opening control
 No adaptation (fixed lookup tables) of inverse I/O valve mapping
 Same inverse I/O mapping for all 4 EHPV in Wheatstone Bridge Arrangement
250
Jacobian
Controllability
Estimation
Kvd
Kv
Kvappx
60
 Learning on single EHPV on pressure control mode
2
MA
Adaptive
Proportional
Feedback
LEARNING INCOVA CONTROL
4
0
1500
PLANT
70
Pressure [MPa]
ABSTRACT
Target Scope
Id: 3
1
8
J[]
sw1
Input Voltage [V]
0
Pdes
Target Scope
Id: 2
FFWD
Pressure Diff [MPa]
3
Target Scope
Id: 1
Velocity [mm/s]
PSD[MPa]
Flow Conductance [LPM/sqrt(MPa)]
FEEDFORWARD
COMPENSATION
CAN-AC2-PCI B1
CAN 1 / CAN 2
Standard / Extended
 Experimental merging of flow conductance
estimator and learning control
 Incorporate fault diagnostics capabilities along
with online I/O mapping learning
4
6
PS
FUTURE WORK
3.5
Time [sec]
Closed-loop Step Response
PS
 Development of hydraulic testbed employing
the EHPV Wheatstone bridge arrangement
 Design and test EHPV Pump pressure control
scheme
 Design and test INCOVA control scheme
without online learning of the valves’
input/output (I/O) mapping
 Design and test INCOVA control scheme with
online learning of the valves’ input/output
mapping
 Design and test flow conductance observer
 Conduct performance evaluation
3
NLPN
Q [LPM/V-sqrt(MPa)]
2.5
KV
B- Nominal Input Current [mA]
3
isol
B- NLPN Input Current [mA]
4
Inverse
Mapping
Correction
A- Nominal Input Current [mA]
Pressure [MPa]
5
PRESSURE CONTROL EHPV
(SINGLE CARTRIDGE)
SENSORS
Motion control of hydraulic pistons can be
accomplished with independent metering using
Electro-Hydraulic Poppet Valves. Currently, the
valve opening is achieved by changing the
valve’s conductance coefficient Kv (output) in an
open loop manner via PWM current (input),
computed from an inverse input-output map
obtained through offline calibration. Without any
online correction, the map cannot be adjusted to
accurately reflect the behavior of the valve as it
undergoes continuous operation. The intention is
to develop a control methodology to have the
valve learn its own inverse mapping at the same
time that it’s performance is improved.
The control input is composed of:
 Feedback compensation via online
identification of trajectory error parameters
 Feedforward compensation via
nominal inverse mapping
 Feedforward correction via
learned adjustment to inverse nominal
mapping
6
0
PS_DES
Nominal
inverse
mapping
A- NLPN Input Current [mA]
 Single EHPV
 Feedback compensation via discrete PI controller
with anti-windup
 Feedforward compensation via inverse experimental
steady state response
Patrick Opdenbosch
November 9, 2005
ver 1.0
Sampling: 10 msec
LEARNING CONTROL
PID Response: Manual Setting at 1000psi & KI = 30 KP = 350
V
7
Pressure [MPa]
 Development of a general formulation for
control of nonlinear systems with parametric
uncertainty, time-varying characteristics, and
input saturation
 Improve the performance of electro-hydraulic
valves via online auto-calibration and feedback
control
 Study of online learning dynamics along with
fault diagnostics
SUPPLY PRESSURE [MPa]
GOALS
dK
m
Valves for Pump Control
-5
x
-10
400
Needle Valve
-1500
-15
-2000
-20
-2500
-25
-135
x
VB0
AB
AA
VA0
Hydraulic Piston
HYDRAULIC CIRCUIT
200
Speed Command Knob
0
-200
Piston Speed [mm/s]
0
10
20
30
40
Time [sec]
50
60
70
Hydraulic Piston
-110
-85
-60
-35
Simulation Results: Estimated KA vs Actual KA
RUN 02
RUN 04
RUN 06
15
40
65
90
RUN 07
Experimental Piston’s Friction Force
RUN 01
RUN 02
RUN 04
RUN 06
Position/Velocity Sensor
RUN 07
Friction Force Model Error Df
Sponsors: HUSCO International and FPMC Center
Email: [email protected]
Bypass Hose
115
Piston Speed [mm/s]
80
RUN 01
-10
website: http://www.imdl.gatech.edu/opdenbosch Spring 2006
HYDRAULIC COMPONENTS
Collaborators:
•James D. Huggins
Advisors:
•Dr. Nader Sadegh, Dr. Wayne Book