Control of Electro-Hydraulic Poppet Valves (EHPV)

1
Control of Electro-Hydraulic Poppet Valves (EHPV)
PATRICK OPDENBOSCH Graduate Research
Assistant Manufacturing Research Center  Room
259 Ph. [phone] gte608g_at_mail.gatech.edu
Georgia Institute of Technology
George W. Woodruff School of Mechanical
Engineering
Sponsored by HUSCO International and the Fluid
Power Motion Control Center
2
AGENDA

• INTRODUCTION.
• NLPN.
• CURRENT TO Kv MAP.
• CONTROL APPROACH.
• FUTURE WORK.
• CONCLUSIONS.

3
INTRODUCTION
4

• EHPV FEATURES
• Proportional flow area control
• Bidirectional Capability
• Zero leakage
• Low Hysteresis
• 12 Volt,1.5 Amp max (per solenoid)

US PATENT 6,745,992 6,328,275
Modulating Spring
Coil
Pilot

• ADVANTAGES OVER SPOOL VALVES
• EHPVs offer excellent sealing capabilities
• Less faulting
• High resistance to contamination
• High flow to poppet displacement ratios,
• Low cost and low maintenance,
• Applicable to a variety of control functions.

Armature
Armature Bias Spring
Pilot Seat Sensing Piston
Pressure Compensating Spring

• APPLICATIONS
• Pressure Flow Control
• Construction machinery
• Robotics/manufacturing
• Automotive industry (active suspension)

Main Poppet
Side Port
Nose Port
5

• EMPLOYMENT OF POPPET VALVES IN ACTUATOR MOTION
  CONTROL

US PATENT 5,878,647
4 EHPV on wheatstone bridge arrangement

• METERING MODES
• Standard metering extend
• Low side regeneration extend
• Low side regeneration retract
• High side regeneration
• Standard float

Wheatstone bridge assembly view
6
Standard Metering Extend

• INCOVA offers force dependent metering ratios
• Total valve loss lower than spool
• Force Limiting

7
Low Side Regeneration Extend

• Metered LS extension w/out cavitation
• No pump flow consumption
• Overrunning loads
• Reduced multi-function cycle time

8
Low Side Regeneration Retract

• Metered LS regeneration w/out cavitation
• No pump flow consumption
• Overrunning loads
• Reduced multi-function cycle time

9
High Side Regeneration

• Metered HS Regeneration Only Possible with INCOVA
• Low pump flow consumption
• Overrunning or powered loads
• Reduced multi-function cycle time

10
Standard Float

• INCOVA allows for a smart float function.
• INCOVA also allows for float damping if required

11
Calculate desired speed, n
HIERARCHICAL CONTROL
US PATENT 6,732,512 6,718,759
Read port pressures, Ps PR PA PB
Calculate equivalent KvEQ
Determine Individual Kv
KvB

• Hierarchical control System controller, pressure
  controller, function controller

KvA
Determine input current to EHPV isolf(Kv,DP,T)
12
LOCAL (LOWER LEVEL) CONTROL

• INPUT-OUTPUT MAP
• Currently obtained through extensive offline
  calibration
• Different valves (sizes) require different maps
  (specifically tailored)
• Offline map might not accurately reflect valve
  behavior after considerable continuous operation

• PROBLEMS
• EHPV transients might not be as desired
• Open loop sensitivity to disturbances
• Flow forces on the main poppet and the pilot harm
  the hydro-mechanical compensation especially at
  high DP
• Effects are different between forward and reverse
  flow

Flow conductance coefficient Kv as a function of
input current and pressure differential
13
IMPROVED LOCAL CONTROL

• INPUT-OUTPUT MAP PROPOSED SOLUTIONS
• Online learning of the input-output map through
  suitable training criterion.
• Compatibility of adaptive look-up table with
  existing industrial trends
• Improve mapping that more accurately reflects
  valve behavior after considerable continuous
  operation
• Development of robust observer for the online
  estimation of the KV.

• OBJECTIVES
• Implementation of feedback control with the aid
  of soft sensor technology and online training
  algorithms
• Improve transient behavior
• Make the valve more intelligent and self contained

14
NLPN
15
NODAL LINK PERCEPTRON NETWORK (NLPN)

• MAIN FEATURE
• Approximates nonlinear functions using a number
  of local adjustable functions.

The NLPN is a three-layer perceptron network
whose input is related to the output by

• Basis functions are chosen so that
• f11
• The set Bfi is a linearly independent set
  i.e. if

then
for i 1 N
NLPN structure

• For some l gt 0, it is true that

The idea is to choose wi and fi so that
More details found at Sadegh, N. (1998) A
multilayer nodal link perceptron network with
least squares training algorithm, Int. J.
Control, Vol.70, No. 3, 385-404.
16

• TRAINING
• Once a basis function structure is chosen, train
  the network to learn the weights.

DELTA RULE
LEAST SQUARES

• HOW IT WORKS (1D EX)
• Triangular basis function structure is chosen
• Weights are computed using least squares

f1
f2
f3
Function to be approximated
x
17
(No Transcript)
18
COMMON BASIS FUNCTIONS
Triangular
Gaussian
Hyperbolic
A B
C
A B
C
A B
C
So that at most 2n components of F are nonzero.
For multidimensional input space
For example,
19
COMMON APPLICATIONS

• Offline curve fitting

Actual Map
NLPN approximation
Approximation Error

• Filtering

• System identification

• Selmic, R. R., Lewis, F. L., (2000)
  Identification of Nonlinear Systems Using RBF
  Neural Networks Application to Multimodel
  Failure Detection, Proceedings of the IEEE
  Conference on Decision and Control, v 4, 2001, p
  3128-3133
• Sanner, R. M., J. E. Slotine, (1991) Stable
  Adaptive Control and Recursive Identification
  Using Radial Gaussian Networks, Proceedings of
  the IEEE Conference on Decision and Control, v 3,
  1991, p 2116-2123.
• Sadegh, N., (1993), A Perceptron Network for
  Functional Identification and Control of
  Nonlinear Systems, IEEE trans. N. Networks, Vol.
  4, No. 6, 982-988

20
CURRENT TO Kv MAP
21
APPLICATION TO CONTROL OF EHPV
Initially proposed control scheme
Feedback adaptive control scheme
Testing of NLPN map learning
CITGO A/W Hydraulic Oil 32
Oil Properties
Viscosity
A 5.68x10-9 Ns/m2 B 4827.6 1/K
Density
C 1056.1 kg/m3 B -0.647 kg/m3K
22
SIMULATED STEADY STATE EHPV Kv
Forward flow
At constant temperature
At constant opening
23
SIMULATED INVERSE MAP ESTIMATION
Forward flow
24
EXPERIMENTAL ESTIMATION

• Steady state data was obtained from the Hydraulic
  circuit employed at the Hardware-In-the-Loop
  (HIL) Simulator

Hardware-In-the Loop (HIL) Simulator
Hydraulic circuit employed at the HIL
EHPV mounted on the HIL. Quick connections for
forward and reverse flow
25
EXPERIMENTAL MEASUREMENT OF STEADY STATE FLOW
CONDUCTANCE COEFFICIENT Kv.
Forward Kv as a function of Pressure differential
and input current
Reverse Kv as a function of Pressure differential
and input current
Forward Side to nose
Reverse Nose to side
26
FORWARD Kv AND isol MAP LEARNING
Kv map
Kv map learning
isol map
isol map learning
27
REVERSE Kv AND isol MAP LEARNING
Kv map
Kv map learning
isol map
isol map learning
28
CONTROL APPROACHES
29
EHPV NONLINEAR MAP

• Nonlinearities arise from
• State constraints
• Nonlinear flow models
• Bidirectional mode
• Model switching
• Electromagnetic nonlinearities

Response is dominated by second order dynamics
PRELIMINAR STEP BLOCK-INPUT FORM
Let a system be described by
Then, it can be transformed to a system such that
Trivial example
For m2
30
TRACKING CONTROL
Let the nonlinear Function representing the
behavior of the EHPV be expressed in block-input
form by
where,
Then linearizing about,
yields,
Assumptions
1. The system is strongly controllable there is
a unique input so that
2. The controllability matrix Q has full rank for
all inputs and states.
31
Proposed control law
where,
(NLPN learning)
Upon substitution into the error equation,
Assumption
This result combined with the training law is
used to study the stability of the closed loop
system.
32
ESTIMATION TASK
The control law requires the knowledge of the
Jacobian Jk and the input matrix Qk
Furthermore, the control law requires the
knowledge of the desired states dXk
For the desired states dXk
To obtain an estimate of the of the Jacobian Jk
and the input matrix Qk
33
The Jacobian Jk and the input matrix Qk can be
approximated by
Procedure
Applying the stack operator and the Kronecker
product
or
Looking for
34
MODIFIED BROYDEN METHOD
TESTING
Simulation Results
SIMULINK model
35
FUTURE WORK
36

• TASKS TO BE ACCOMPLISHED
• Debug algorithms
• Investigation of other possible algorithms for
  matrix estimation
• Tune up and testing of NLPN controller and matrix
  estimators in the Hardware-In-the Loop simulator
• Investigate robustness
• Development of nonlinear Kv observer
• Research possible online calibration methods.
• Explore position control accuracy

37
CONCLUSIONS
38
RESEARCH OBJECTIVE

• Investigation and development of an advanced
  control methodology for the EHPV using online
  training and soft sensing technology.

NLPN

• Development of nonlinear mapping tool.
• Design with flexibility in basis functions
• Approximation of f Rn Rm

CURRENT TO Kv MAPPING

• Simulation of direct and inverse mappings
• Simulation of steady state mapping including
  temperature effects
• Experimental application for forward and reverse
  flow conditions on both direct and inverse
  mappings

CONTROL APPROACH

• Development of NLPN controller
• Matrix estimation through modified Broyden method