Next steps: Implementation of the fault diagnostics schemes is to be

completed.

SUPPLY RETURN

COIL 2: SA

COIL 1: AR

COIL 3: BR

COIL 4: SB

COIL 0: SR

MANUAL BYPASS

MANUAL ISOLATION MANUAL ISOLATION

HYDRAULIC PISTON

LOAD

Thrust 1 affiliated projectAuto-Calibration and Control Applied to

Electro-Hydraulic Valves

Industrial Involvement:Industrial Involvement:

Wayne Book, GT

facu

lty

& s

taff

Nader Sadegh, GT

Patrick Opdenbosch, GT

stu

den

ts

Project Goals: Development of a general formulation for control of nonlinear systems with Development of a general formulation for control of nonlinear systems with

parametric uncertainty, time-varying characteristics, and input saturation. parametric uncertainty, time-varying characteristics, and input saturation. Improve the performance of electro-hydraulic valves via online Improve the performance of electro-hydraulic valves via online inverse flow

conductance mapping learning (auto-calibration)auto-calibration) and controlAnalyze effects from input saturation and time-varying dynamicsAchieve small tracking error and adequate transient behavior while learning

Study of online learning dynamics along with fault diagnosticsStudy of online learning dynamics along with fault diagnosticsInvestigate the combination of learning mappings and fault detection

Support of Strategic Plan: This section is also taken

from the revised strategic plan, with careful paraphrasing. Also include test beds that will utilize this result.

The Problem: In an effort to improve In an effort to improve

efficiency, motion control of hydraulic pistons is pursued efficiency, motion control of hydraulic pistons is pursued through the idea of independent metering using Electro-through the idea of independent metering using Electro-Hydraulic Poppet Valves. Currently, the valve opening is Hydraulic Poppet Valves. Currently, the valve opening is achieved by changing the valve’s conductance coefficient achieved by changing the valve’s conductance coefficient KKv v in an open loop manner using the inverse input-output map in an open loop manner using the inverse input-output map obtained through offline calibration. Without any online obtained through offline calibration. Without any online correction, the map cannot be adjusted to accurately reflect correction, the map cannot be adjusted to accurately reflect the behavior of the valve as it undergoes continuous the behavior of the valve as it undergoes continuous operation. For that reason, this research is concerned with operation. For that reason, this research is concerned with the development of a control methodology in which the the development of a control methodology in which the valves learn their own inverse mapping at the same time that valves learn their own inverse mapping at the same time that their performance is improved. The their performance is improved. The inverse mapping learning is accomplished by the implementation of adaptive look-up tables, and not only the performance can be enhanced, but also health monitoring can be enabled.

The Approach: The mid-level controller is improved from a fixed

loop-up table into an active learner of the inverse dynamical map of the EHPV. Two methods are exposed. One based on using an adaptive look-up table (NLPN) that is trained to correct the inverse nominal mapping and it is assisted by proportional control. The other method is based on learning the inverse dynamical map by matching the input (current [mA]) sent to the valve from the state of the valve (Opening or Conductance, Kv).

Results to date:

Coupled Metering

Independent Metering

0

12

34

5

0

0.5

1

1.50

20

40

60

80

100

120

dP [MPa]

Constant Temperature (T = 30 C)

Input [A]

Kv

[(LP

M)/

sqrt

(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

EHPV Steady State Direct & Inverse Mapping

EHPV valve

Spool valve

Hierarchical control: Top Level Controller, Mid-Level Controller, and Low Level Controller

COIL CURRENTSERVO

(PWM + dither)

MAPPING LEARNING &

CONTROL

INCOVA LOGIC (VELOCITY

BASED CONTROL)

OPERATOR INPUT:Commanded Velocity

EHPVOpening

93

Nominal inverse

mapping

Inverse Mapping

Correction

FIXED Proportional Feedback

NLPNKV

dKV

icmd

GATECH - EXPERIMENTAL DATA

EHPVServo

56

Control Input Mapping via Input Matching

MAPPING LEARNING & CONTROL

uk

z-1

z-1

PLANT (z1,z2)

(z1,z2)

Wk

xkxk+1

*

+-

k

NLPN Based Input Matching Control

How can state tracking error be bounded arbitrarily close to the origin?

How should the NLPN be started?

Choice of adaptation methodInverse Mapping Learning and Control Methods

NLPN Based Nominal Mapping Correction Learning NLPN Based Input Matching Learning

dKVKVicmd

94

GATECH - EXPERIMENTAL DATA

PUMP CONTROL: MARGIN

0 5 10 15 20 250

150

300

450

600

750

900

Time [sec]

Pos

ition

[m

m]

0 5 10 15 20 25-20

-5

10

25

40

55

70

Ang

le [

deg]

0 5 10 15 20 25-250

-150

-50

50

150

250

Time [sec]

Spe

ed [

mm

/s]

VcmdVmeas

AnglePosition

96

GATECH - EXPERIMENTAL DATA

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

KSBcKSBm

KBRcKBRm

KARcKARm

KSAcKSAmNLPN Based Nominal Mapping

Correction Learning:

Hydraulic Testbed at the HIBAY (Manufacturing Research Complex - Georgia Tech)

Experimental results are obtained from the hydraulic testbed shown on the right. Standard loads and overrunning loads are present in this testbed.

94

GATECH - EXPERIMENTAL DATA

PUMP CONTROL: MARGIN

0 5 10 15 20 250

150

300

450

600

750

900

Time [sec]

Pos

ition

[m

m]

0 5 10 15 20 25-20

-5

10

25

40

55

70

Ang

le [

deg]

0 5 10 15 20 25-250

-150

-50

50

150

250

Time [sec]

Spe

ed [

mm

/s]

VcmdVmeas

AnglePosition

96

GATECH - EXPERIMENTAL DATA

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

0 5 10 15 20 25-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

Time [sec]

Kv

[LP

H/s

qrt(

MP

a)]

KSBcKSBm

KBRcKBRm

KARcKARm

KSAcKSAmNLPN Based Input Matching

Learning:

James D. Huggins, GT