AUTO-CALIBRATION AND CONTROL APPLIED TO ELECTRO-HYDRAULIC VALVES

PATRICK OPDENBOSCHGraduate Research Intern

INCOVA(262) 513 4408

patrick.opdenbosch@huscointl.com

EXPERIMENTS ON HUSCO BLUE TELEHANDLER

August 18, 2006

HUSCO InternationalW239 N218 Pewaukee Rd.Waukesha, WI 53188-1638

2

MOTIVATION

Hierarchical control: System controller, pressure controller, function controller

US PATENT # 6,732,512 & 6,718,759

HUSCO’S CONTROL TOPOLOGY

Inverse Mapping (Control)

Steady State Mapping (Design)

HUSCO OPEN LOOP CONTROL FOR EHPV’s

0 200 400 600 800 1000 1200 1400 1600 18000

1000

2000

3000

4000

5000

6000

7000

8000

Coil Current [mA]

Flow

Con

duct

ance

, Kv

[LP

H/s

qrtM

Pa]

0 1000 2000 3000 4000 5000 6000 7000 80000

200

400

600

800

1000

1200

1400

1600

1800

Flow Conductance, Kv [LPH/sqrtMPa]

Coi

l Cur

rent

[mA

]

3

MOTIVATION

Hierarchical control: System controller, pressure controller, function controller

US PATENT # 6,732,512 & 6,718,759

HUSCO’S CONTROL TOPOLOGY

01

23

45

0

0.5

1

1.50

20

40

60

80

100

120

dP [MPa]

Constant Temperature (T = 30 C)

Input [A]

Kv

[(LP

M)/s

qrt(M

Pa)

]

020

4060

80100

0

1

2

3

4

50

0.2

0.4

0.6

0.8

1

1.2

1.4

Kv [(LPM)/sqrt(MPa)]dP [MPa]

Inpu

t [A

]

T = 20 C

Inverse Mapping (Control)

Steady State Mapping (Design)

4

MOTIVATION

Time

Time

Commanded Velocity

Actual Velocity

Commanded Kv

Actual Kv

5

MOTIVATION

01

23

45

0

0.5

1

1.50

20

40

60

80

100

120

dP [MPa]

Constant Temperature (T = 30 C)

Input [A]

Kv

[(LP

M)/s

qrt(

MP

a)]

020

4060

80100

0

1

2

3

4

50

0.2

0.4

0.6

0.8

1

1.2

1.4

Kv [(LPM)/sqrt(MPa)]dP [MPa]

Inpu

t [A

]

T = 20 C

Flow conductance online estimation Accuracy Computation effort

Online inverse flow conductance mapping learning and control Effects by input saturation and time-

varying dynamics Maintain tracking error dynamics stable

while learning

Fault diagnostics How can the learned mappings be used

for fault detection

6

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

7

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

8

TOPIC REVIEWCATERPILLAR 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)

9

TOPIC REVIEW

CATERPILLAR PATENTS 20060064971 “Adaptive Position Determining System

for Hydraulic Cylinder”

Limit Switches

10

TOPIC REVIEW

CATERPILLAR PATENTS 5,004,264 “Position Control Device and Automotive

Suspension System Employing Same”

Position Detector

Long-Jang Li, US Patent 5,942,892 (1999)

11

TOPIC REVIEW

CATERPILLAR PATENTS 20060095163 “Electrohydraulic Control System”

Position/Velocity sensor

Adaptive scheme: no details found

12

TOPIC REVIEW

CATERPILLAR PATENTS 7,043,975 “Hydraulic System Health Indicator”

Health Monitoring using Bulk modulus and other

model-based parameters

(Position/velocity sensor)

Using Lyapunov stability theory

Based on pump pressure discharge dynamics or cylinder head end control pressure

13

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

14

SETUP MOTION CONTROL

Independent coil current control

SIEMENS controller Supply & return pressure from

ISP Supply

Boom Function Kinematics

HUSCO Blue Telehandler

Boom Function

KSA

Return

KSB

KBRKAR

15

SETUP MOTION CONTROL

Independent coil current control

SIEMENS controller Supply & return pressure from

ISP

HUSCO Blue Telehandler

Diesel Engine

Pump

Filter

Tank

Relief Valve

UnloaderKSA KSB

KAR KBR Boom Cylinder

PS

PA

PR

PB

16

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

17

IMPROVEMENTS PUMP CONTROL

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]

PspPSPAPBPR

Pressure override for pump pressure control (ISP code)

Ripples

18

IMPROVEMENTS PUMP CONTROL

Current override for unloader coil current control

(ISP code)

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]

PSspPSPRPB

DATA SHOWN: Margin added on retract metering mode

(PB signal is user commanded, not actual workport pressure)

20

IMPROVEMENTS ANTI-CAVITATION

0 1000 2000 3000 4000 5000 6000 7000 80000

1000

2000

3000

4000

5000

6000

7000

8000INCOVA Parametric Valve Calculation Standard Metering Retract

KSB [LPH/sqrtMPa]

KA

R [L

PH

/sqr

tMP

a]

KOUT_MAX

PIN

_MIN

Keq_dPmin

KIN

_MA

X

POUT_MAX

Keq

= R

3/4

Unconstrained Operating Point

Constrained Operating Point

21

IMPROVEMENTS ANTI-CAVITATION

55 56 57 58 59 60 61600

700

800

Time [sec]

Pos

ition

[mm

]

55 56 57 58 59 60 61-1

-0.5

0

0.5

Time [sec]

Vel

ocity

[kph

]

MeasDes

55 56 57 58 59 60 610

5

10

15

Time [sec]

Pre

ssur

e [M

Pa]

PSPAPBPR

55 56 57 58 59 60 610

2000

4000

6000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

A+B+A-B-

Cavitation

22

IMPROVEMENTS ANTI-CAVITATION

No Cavitation

0 2 4 6 8 10 12 14 16 18 200

150

300

450

600

750

900

Time [sec]

Pos

ition

[mm

]

0 2 4 6 8 10 12 14 16 18 20-20

-5

10

25

40

55

70

Ang

le [d

eg]

Position Angle

0 2 4 6 8 10 12 14 16 18 20-250

-150

-50

50

150

250

Time [sec]

Spe

ed [m

m/s

]VdesVcmdVmeas

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]

PspPSPAPBPR

Flow Sharing

23

IMPROVEMENTS LEARNING

EXTEND

Supply

Boom Function

KSA

Return

KSB

KBRKAR

24

IMPROVEMENTS LEARNING

RETRACT

Supply

Boom Function

KSA

Return

KSB

KBRKAR

25

IMPROVEMENTS LEARNING

EXTEND/RETRACT

Supply

Boom Function

KSA

Return

KSB

KBRKAR

26

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

27

LEARNING APPLIED TO NONLINEAR SYSTEM

MAPPING TO BE LEARNED (simplified)

MAPPING LEARNING & CONTROL

1 sol,,k k kK F K i [ ]MAX0,K KÎ[ ]sol, 0,1500mAki Î

0 1000 2000 3000 4000 5000 6000 7000 80000

200

400

600

800

1000

1200

1400

1600

1800

Flow Conductance, Kv [LPH/sqrtMPa]

Coi

l Cur

rent

Com

man

d [m

A]

Expected curve shift

28

0 2000 4000 6000 8000 10000 120000

200

400

600

800

1000

1200

1400

1600

1800

2000

Flow Conductance, Kv [LPH/sqrtMPa]

Coi

l Cur

rent

[mA

]

MapGrid

LEARNING APPLIED TO NONLINEAR SYSTEM

MAPPING TO BE LEARNED (simplified)

MAPPING LEARNING & CONTROL

1 sol,,k k kK F K i [ ]MAX0,K KÎ[ ]sol, 0,1500mAki Î

Expected curve shift

29

MAPPING LEARNING & CONTROL LEARNING APPLIED TO NONLINEAR SYSTEM

CONTROL DESIGN Tracking Error: Error Dynamics:

1 sol,,k k kK F K i [ ]MAX0,K KÎ[ ]sol, 0,1500mAki Î

sol, sol,1 sol, sol,

sol,

, ,,

d d d dk k k k d

k k k k k kk k

F K i F K ie e i i o e

K i

dk k kK K= -e

1 sol, sol,d d d

k k k k k ke J e Q i i Linear Time

Varying System

30

LEARNING APPLIED TO NONLINEAR SYSTEM

CONTROL DESIGN Error Dynamics:

Deadbeat Control Law:

Closed loop

MAPPING LEARNING & CONTROL

1 sol,,k k kK F K i [ ]MAX0,K KÎ[ ]sol, 0,1500mAki Î

1 sol, sol,d d d

k k k k k ke J e Q i i

1sol, sol,

d d dk k k k ki i Q J e-= -

1 0ke

31

MAPPING LEARNING & CONTROL

1 sol,,k k kK F K i [ ]MAX0,K KÎ[ ]sol, 0,1500mAki Î

1sol, sol,

d d dk k k k ki i Q J e-= -

LEARNING APPLIED TO NONLINEAR SYSTEM

CONTROL DESIGN Deadbeat Control Law:

Proposed Control Law:

1sol, sol, sol, 1 1k k k k k ki i i Q J e

( )sol,d

k ki Kg=( )sol,

T dk k ki KD = FW%%

32

Nominal inverse

mapping

Inverse Mapping

Correction

Adaptive Proportional Feedback

NLPN

Jacobian Controllability

Estimation

KVdKV

icmd

MAPPING LEARNING & CONTROL

EHPVServo

33

LEARNING APPLIED TO NONLINEAR SYSTEM

CONTROL DESIGN Proposed Control Law:

Closed loop

MAPPING LEARNING & CONTROL

1 sol,,k k kK F K i [ ]MAX0,K KÎ[ ]sol, 0,1500mAki Î

1sol, sol, sol, 1 1k k k k k ki i i Q J e

( )sol,d

k ki Kg=( )sol,

T dk k ki KD = FW%%

11 sol, sol, 1 1 sol,

11 1 sol, sol, sol,

d d dk k k k k k k k k k

d d d dk k k k k k k k k

e J e Q i i Q J e i

J Q Q J e Q i i i

34

IDENTIFICATION DESIGN Methods:

Least Squares (Recursive)▫ Noise rejection▫ Poor time varying parameter

tracking capabilities (add covariance reset and forgetting factor – dynamic or static)

▫ New research suggest variable-length moving window*

Gradient Based▫ Sensitive to noise▫ Better time varying parameter

tracking capabilities▫ Gradient step size must be chosen

carefully

MAPPING LEARNING & CONTROL

0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Parameter "a" Estimation

Time [sec]

a

TrueBroydenRLSRLS w/EW

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4Parameter "a" Estimation

Time [sec]

a

TrueBroydenRLSRLS w/EW

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.

T ˆky

35

IDENTIFICATION DESIGN

Approximations: Previous-point Linearization

Stack Operator

MAPPING LEARNING & CONTROL

1 sol, sol,d d d

k k k k k ke J e Q i i

* *1 1 1 sol, sol, 1k k k k k kK J K Q i i

1k k kK K K

TT T T1 2 1 2( ) , wheren nS x x x x x x x x

36

IDENTIFICATION DESIGN

Approximations: Previous-point Linearization

Stack Operator Properties

MAPPING LEARNING & CONTROL

1 sol, sol,d d d

k k k k k ke J e Q i i

* *1 1 1 sol, sol, 1k k k k k kK J K Q i i

1k k kK K K

T

( )S S S

S S

x z x z

xyz z x y

37

IDENTIFICATION DESIGN

Approximations: Previous-point Linearization

Stack Operator Properties

MAPPING LEARNING & CONTROL

1 sol, sol,d d d

k k k k k ke J e Q i i

* *1 1 1 sol, sol, 1k k k k k kK J K Q i i

1k k kK K K

11 1

1

n

m mn

x x

x x

z zx z

z z

38

IDENTIFICATION DESIGN

Approximations: Previous-point Linearization

MAPPING LEARNING & CONTROL

* *1 1 1 sol, sol, 1k k k k k kK J K Q i i

* *1 1 1 sol, sol, 1

* *1 1 sol, sol, 1

TT * *1 sol, sol, 1 1

TTT * *sol, sol, 1 1 1| |

k k k k k k

n k k n k k k

k n k k k n k

k n k k n k k

K S J K S Q i i

S I J K S I Q i i

K I S J i i I S Q

K I i i I S J S Q

1 sol, sol,d d d

k k k k k ke J e Q i i

39

IDENTIFICATION DESIGN

Approximations: Previous-point Linearization

MAPPING LEARNING & CONTROL

* *1 1 1 sol, sol, 1k k k k k kK J K Q i i

TTT * *1 sol, sol, 1 1 1| |k k n k k n k kK K I i i I S J S Q

1 sol, sol,d d d

k k k k k ke J e Q i i

T ˆky

How are (dJ,dQ) and (J*,Q*) related?

40

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

41

Nominal inverse

mapping

KVdKV

icmd

EXPERIMENTAL RESULTS

EHPVServo

0 1000 2000 3000 4000 5000 6000 7000 80000

200

400

600

800

1000

1200

1400

1600

1800

Flow Conductance, Kv [LPH/sqrtMPa]

Coi

l Cur

rent

Com

man

d [m

A]

Every valve uses a generic Table

42

EXPERIMENTAL RESULTS

0 2 4 6 8 10 12 14 16 18 200

150

300

450

600

750

900

Time [sec]

Pos

ition

[mm

]

0 2 4 6 8 10 12 14 16 18 20-20

-5

10

25

40

55

70

Ang

le [d

eg]

Position Angle

0 2 4 6 8 10 12 14 16 18 20-250

-150

-50

50

150

250

Time [sec]

Spe

ed [m

m/s

]

VdesVcmdVmeas

PUMP CONTROL: MARGIN

43

EXPERIMENTAL RESULTS

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]

PspPSPAPBPR

44

EXPERIMENTAL RESULTS

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KSAcKSAm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KARcKARm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KBRcKBRm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KSBcKSBm

45

EXPERIMENTAL RESULTS PUMP CONTROL: PS_SETPOINT

0 2 4 6 8 10 12 14 16 18 200

150

300

450

600

750

900

Time [sec]

Pos

ition

[mm

]

0 2 4 6 8 10 12 14 16 18 20-20

-5

10

25

40

55

70

Ang

le [d

eg]

Position Angle

0 2 4 6 8 10 12 14 16 18 20-250

-150

-50

50

150

250

Time [sec]

Spe

ed [m

m/s

]

VdesVcmdVmeas

46

EXPERIMENTAL RESULTS

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]PspPSPAPBPR

47

EXPERIMENTAL RESULTS

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]KSAcKSAm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KARcKARm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KBRcKBRm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KSBcKSBm

48

Nominal inverse

mapping

Inverse Mapping

Correction

NLPNKV

dKV

icmd

EXPERIMENTAL RESULTS

EHPVServo

49

EXPERIMENTAL RESULTS PUMP CONTROL: MARGIN

0 2 4 6 8 10 12 14 16 18 200

150

300

450

600

750

900

Time [sec]

Pos

ition

[mm

]

0 2 4 6 8 10 12 14 16 18 20-20

-5

10

25

40

55

70

Ang

le [d

eg]

Position Angle

0 2 4 6 8 10 12 14 16 18 20-250

-150

-50

50

150

250

Time [sec]

Spe

ed [m

m/s

]

VdesVcmdVmeas

50

EXPERIMENTAL RESULTS

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]

PspPSPAPBPR

51

EXPERIMENTAL RESULTS

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]KSAcKSAm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KARcKARm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KBRcKBRm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KSBcKSBm

55

EXPERIMENTAL RESULTS PUMP CONTROL: PS_SETPOINT

0 2 4 6 8 10 12 14 16 18 200

150

300

450

600

750

900

Time [sec]

Pos

ition

[mm

]

0 2 4 6 8 10 12 14 16 18 20-20

-5

10

25

40

55

70

Ang

le [d

eg]

Position Angle

0 2 4 6 8 10 12 14 16 18 20-250

-150

-50

50

150

250

Time [sec]

Spe

ed [m

m/s

]

VdesVcmdVmeas

56

EXPERIMENTAL RESULTS

0 2 4 6 8 10 12 14 16 18 20-5

0

5

10

15

20

25

30

35

Time [sec]

Pre

ssur

e [M

Pa]

PspPSPAPBPR

57

EXPERIMENTAL RESULTS

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]KSAcKSAm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KARcKARm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KBRcKBRm

0 5 10 15 20-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

KSBcKSBm

58

Nominal inverse

mapping

Inverse Mapping

Correction

FIXED Proportional Feedback

NLPNKV

dKV

icmd

EXPERIMENTAL RESULTS

EHPVServo

59

EXPERIMENTAL RESULTS PUMP CONTROL: MARGIN

0 10 20 30 40 50 6015

20

25

30

35

40

45

50

55

60

65

70

Time [sec]

Ang

le [d

eg]

w/ FBw/o FB

60

EXPERIMENTAL RESULTS

0 10 20 30 40 50 60-80

-60

-40

-20

0

20

40

60

80

Time [sec]

Spe

ed [m

m/s

]

VcmdVm w/ FBVm w/o FB

61

EXPERIMENTAL RESULTS

0 10 20 30 40 50 600

5

10

15

20

25

30

Time [sec]

Pre

ssur

e [M

Pa]

Pressure Responses w/FB

PspPSPAPBPR

62

EXPERIMENTAL RESULTS

0 10 20 30 40 50 600

5

10

15

20

25

30

Time [sec]

Pre

ssur

e [M

Pa]

Pressure Responses w/o FB

PspPSPAPBPR

63

EXPERIMENTAL RESULTS

0 10 20 30 40 50 600

500

1000

1500

2000

2500

3000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

RED w/FB , BLUE w/o FB

KSAcKSAmKSAcKSAm

64

EXPERIMENTAL RESULTS

0 10 20 30 40 50 600

500

1000

1500

2000

2500

3000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

RED w/FB , BLUE w/o FB

KARcKARmKARcKARm

65

EXPERIMENTAL RESULTS

0 10 20 30 40 50 600

500

1000

1500

2000

2500

3000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

RED w/FB , BLUE w/o FB

KBRcKBRmKBRcKBRm

66

EXPERIMENTAL RESULTS

0 10 20 30 40 50 600

500

1000

1500

2000

2500

3000

Time [sec]

Kv

[LP

H/s

qrt(M

Pa)

]

RED w/FB , BLUE w/o FB

KSBcKSBmKSBcKSBm

68

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

69

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

70

PRESENTATION OUTLINE

MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS

71

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)

72

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

73

QUESTIONS?

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