HYDRAULIC TESTBED

1
EHPV Technology
Auto-Calibration and Control Applied to
Electro-Hydraulic Valves
by Patrick Opdenbosch

• LEARNING CONTROL
• 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

• GOALS
• 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

• PUMP PRESSURE CONTROL
• Single EHPV
• Feedback compensation via discrete PI controller
  with
  anti-windup
• Feedforward compensation via inverse experimental
  steady state
  response

Closed-loop Step Response

• LEARNING INCOVA CONTROL
• Learning on single EHPV on pressure control mode
• Learning on 2 EHPV for piston motion control
• Learning on 4 EHPV for piston motion control

ABSTRACT 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 valves
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 its performance is improved.
Controller Implementation (SIMULINK/XPC-Target)
Closed-loop Tracking Response
Steady State Data used for Feedforward
Compensation

• FIXED INCOVA CONTROL
• 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

Single EHPV Flow Conductance Closed Loop Response
Single EHPV Trajectory Error Parameter Estimation
Pistons Position Closed Loop Response
Pistons Velocity Closed Loop Response
System Pressures
Input Currents to EHPV Solenoids
PUMP PRESSURE MARGIN CONTROL

• PROJECT TASKS
• 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

Piston Speed Response
System Pressures Response
Input Solenoid Currents to EHPVs
EHPV Wheatstone Bridge Arrangement for Motion
Control
Pistons Position Closed Loop Response
Pistons Velocity Closed Loop Response
FLOW CONDUCTANCE OBSERVER
HYDRAULIC TESTBED
Workport Pressure Dynamics
Electronics
Wheatstone Bridge Assembly (EHPV)
Wheatstone Bridge Arrangement
Return
Supply
Piston Dynamics
Resulting System
OBSERVER

• FUTURE WORK
• Experimental merging of flow conductance
  estimator and learning control
• Incorporate fault diagnostics capabilities along
  with online I/O mapping learning

Valves for Pump Control
HYDRAULIC CIRCUIT
Needle Valve
Speed Command Knob
Position/Velocity Sensor
Hydraulic Piston
Bypass Hose
HYDRAULIC COMPONENTS
Simulation Results Estimated KA vs Actual KA
Experimental Pistons Friction Force
Friction Force Model Error Df

• Collaborators
• James D. Huggins

Sponsors HUSCO International and FPMC Center

• Advisors
• Dr. Nader Sadegh, Dr. Wayne Book

website http//www.imdl.gatech.edu/opdenbosch
Spring 2006
Email patrick.opdenbosch_at_gatech.edu