Auto-Calibration%20and%20Control%20Applied%20to%20Electro-Hydraulic%20Valves

1
Auto-Calibration and Control Applied to
Electro-Hydraulic Valves

• By
• PATRICK OPDENBOSCH
• Graduate Research Assistant
• Manufacturing Research Center Room 259
• [phone]
• patrick.opdenbosch_at_gatech.edu

Sponsored by HUSCO International and the Fluid
Power Motion Control Center
2
MOTIVATION

• MOTION CONTROL
• Electronic approach
• Use of solenoid Valves
• Energy efficient operation
• New electrohydraulic valves
• Conventional hydraulic spool valves are being
  replaced by assemblies of 4 independent valves
  for metering control

Low Pressure
High Pressure
Spool Valve
Spool piece
Spool motion
Piston
Piston motion
3
MOTIVATION

• MOTION CONTROL
• Electronic approach
• Use of solenoid Valves
• Energy efficient operation
• New electrohydraulic valves
• Conventional hydraulic spool valves are being
  replaced by assemblies of 4 independent valves
  for metering control

Valve motion
Low Pressure
High Pressure
Piston motion
4
MOTIVATION
Coil Cap
Adjustment Screw

• Electro-Hydraulic Poppet Valve (EHPV)
• Poppet type valve
• Pilot driven
• Solenoid activated
• Internal pressure compensation
• Virtually zero leakage
• Bidirectional
• Low hysteresis
• Low gain initial metering
• PWM current input

Modulating Spring
Input Current
Coil
Armature
Pilot Pin
Control Chamber
Armature Bias Spring
U.S. Patents (6,328,275) (6,745,992)
Pressure Compensating Spring
Main Poppet
Forward (Side) Flow
Reverse (Nose) Flow
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MOTIVATION

• VALVE CHARACTERIZATION
• Flow Conductance Kv
• or

FULLY TURBULENT CHARACTERIZATION
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MOTIVATION

• FORWARD MAPPING
• REVERSE MAPPING

Side to nose
Forward Kv at different input currents A
Nose to side
Reverse Kv at different input currents A
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MOTIVATION
Obtain (Operator) desired speed, n
HUSCOS CONTROL TOPOLOGY
Calculate desired flow, nAB Q
US PATENT 6,732,512 6,718,759
Read port pressures, Ps PR PA PB
Calculate equivalent KvEQ
Determine Individual Kv
KvB
KvA

• Hierarchical control System controller, pressure
  controller, function controller

Determine input current to EHPV isolf(Kv,DP,T)
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MOTIVATION
EXPERIMENTAL DATA
INTERPOLATED AND INVERTED DATA
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MOTIVATION

• 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

10
PRESENTATION OUTLINE

• FLOW CONDUCTANCE ESTIMATION
• Reported work
• Approaches
• ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND
  CONTROL
• Fixed inverse mapping
• Learning mapping response
• FUTURE WORK
• CONCLUSION

11
FLOW CONDUCTANCE ESTIMATION

• REPORTED WORK
• O'hara, D.E., (1990), Smart valve, in Proc
  Winter Annual Meeting of the American Society of
  Mechanical Engineers pp. 95-99
• Book, R., (1998), "Programmable electrohydraulic
  valve", Ph.D. dissertation, Agricultural
  Engineering, University of Illinois at
  Urbana-Champaign
• Garimella, P. and Yao, B., (2002), Nonlinear
  adaptive robust observer for velocity estimation
  of hydraulic cylinders using pressure measurement
  only, in Proc ASME International Mechanical
  Engineering Congress and Exposition pp. 907-916
• 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

12
FLOW CONDUCTANCE ESTIMATION

• O'hara (1990), Book (1998)
• Concept of Inferred Flow Feedback
• Requires a priori knowledge of the flow
  characteristics of the valve via offline
  calibration

Squematic Diagram for Programmable Valve
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FLOW CONDUCTANCE ESTIMATION

• Garimella and Yao (2002)
• Velocity observer based on cylinder cap and rod
  side pressures
• Adaptive robust techniques
• Parametric uncertainty for bulk modulus, load
  mass, friction, and load force
• Nonlinear model based
• Discontinuous projection mapping
• Adaptation is used when PE conditions are
  satisfied

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FLOW CONDUCTANCE ESTIMATION

• Liu and Yao (2005)
• Flow rate observer based on pressure dynamics via
  sliding mode technique.
• Needs pistons position, velocity, rode side
  pressure, and cap side pressure feedback
• Affected by parametric uncertainty in the
  knowledge of effective bulk modulus

15
FLOW CONDUCTANCE ESTIMATION

• Liu and Yao (2005)
• Modeling of valves flow mapping
• Online approach without removal from overall
  system
• Combination of model based approach,
  identification, and NN approximation
• Comparison among automated modeling, offline
  calibration, and manufacturers calibration

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FLOW CONDUCTANCE ESTIMATION

• APPROACHES
• Model based
• Physical sensor
• INCOVA based
• Learning based

EHPV - Wheatstone Bridge used for motion control
of hydraulic pistons
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FLOW CONDUCTANCE ESTIMATION

• MODEL BASED
• Object oriented
• Offline identification
• Online identification
• Customization

EHPV - Wheatstone Bridge used for motion control
of hydraulic pistons
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FLOW CONDUCTANCE ESTIMATION

• PHYSICAL SENSOR
• Position sensor
• Position/velocity sensor
• Venturi type flow meter
• Efficiency compromise
• Sensor safety compromise
• Design compromise
• Cost

EHPV - Wheatstone Bridge used for motion control
of hydraulic pistons
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FLOW CONDUCTANCE ESTIMATION

• INCOVA BASED
• Relies on expected pressures for given commanded
  speed

• Power Extension Mode (PEM)

Actual System
PEQ
Equivalent System
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FLOW CONDUCTANCE ESTIMATION

• INCOVA BASED
• Relies on expected pressures for given commanded
  speed

• Power Extension Mode (PEM)

Actual System
PEQ
KEQ
Equivalent System
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FLOW CONDUCTANCE ESTIMATION

• INCOVA BASED
• Relies on expected pressures for given commanded
  speed

• Power Extension Mode (PEM)

Actual System
PEQ
KEQ
Equivalent System
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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED
• Assumptions
• bulk modulus is sufficiently high
• Variable volume is sufficiently small.
• Negligible temperature change
• Negligible leakage

• Chamber pressure equation

EHPV - Wheatstone Bridge used for motion control
of hydraulic pistons
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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• Let

• Then

• Differentiation yields

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• Let

• Then

• Let

• How good is this approximation?

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• Assume that the sup norm of K is bounded, and
  that K is continuous on the compact set ?

• Then

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• Actual system

• Let the observer be

• Let the error be

• Then

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• SIMULATIONS

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• SIMULATIONS plots (d 0)

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• SIMULATIONS plots (d ?0, Friction error less than
  0.3N)

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• Experimental data (offline)

Note Signals low-pass filtered at 5Hz
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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED

• How small is d?

• The error is

• d depends on how well we know the friction model

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED
• Actual Data

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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED
• Friction model

Bonchis, A., Corke, P.I., and Rye, D.C.,
(1999), A pressure-based, velocity independent,
friction model for asymmetric hydraulic
cylinders, in Proc IEEE International Conference
on Robotics and Automation pp. 1746-1751
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FLOW CONDUCTANCE ESTIMATION

• LEARNING BASED
• Friction model

Bonchis, A., Corke, P.I., and Rye, D.C.,
(1999), A pressure-based, velocity independent,
friction model for asymmetric hydraulic
cylinders, in Proc IEEE International Conference
on Robotics and Automation pp. 1746-1751
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PRESENTATION OUTLINE

• FLOW CONDUCTANCE ESTIMATION
• Reported work
• Approaches
• ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND
  CONTROL
• Fixed inverse mapping
• Learning mapping response
• FUTURE WORK
• CONCLUSION

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MAPPING LEARNING CONTROL

• PUMP CONTROL
• Single EHPV
• Feedback compensation (discrete PI controller)
• Feedforward compensation (lookup table)

EHPV - Wheatstone Bridge used for motion control
of hydraulic pistons
EHPV for pump control
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MAPPING LEARNING CONTROL

• PUMP CONTROL
• Single EHPV
• Feedback compensation
• Feedforward compensation

Pump pressure control scheme
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MAPPING LEARNING CONTROL

• PUMP CONTROL
• Single EHPV
• Feedback compensation
• Feedforward compensation

Feedforward mapping
Measured mapping
Pump pressure control scheme
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MAPPING LEARNING CONTROL

• PUMP CONTROL
• Single EHPV
• Feedback compensation
• Feedforward compensation

Closed loop step response
Closed loop tracking response
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MAPPING LEARNING CONTROL

• FIXED TABLE CONTROL
• Pump control INCOVA control
• No adaptation of inverse Kv mapping
• Same inverse Kv mapping for all valves

Fixed Set Pump Pressure
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MAPPING LEARNING CONTROL

• FIXED TABLE CONTROL
• Pump control INCOVA control
• No adaptation of inverse Kv mapping
• Same inverse Kv mapping for all valves

Pump Margin Control
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MAPPING LEARNING CONTROL

• FIXED TABLE CONTROL
• Pump control INCOVA control
• No adaptation of inverse Kv mapping
• Same inverse Kv mapping for all valves

• VELOCITY ERRORS
• Inaccuracy of inverse tables
• Physical limitations/constraints

Velocity Errors with Pump Margin Control and
Fixed Inverse Tables
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MAPPING LEARNING CONTROL

• LEARNING APPLIED TO NONLINEAR SYSTEM
• CONTROL DESIGN
• Tracking Error
• Error Dynamics

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MAPPING LEARNING CONTROL

• LEARNING APPLIED TO NONLINEAR SYSTEM
• CONTROL DESIGN
• Error Dynamics
• Deadbeat Control Law

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MAPPING LEARNING CONTROL

• LEARNING APPLIED TO NONLINEAR SYSTEM
• CONTROL DESIGN
• Deadbeat Control Law
• Proposed Control Law

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MAPPING LEARNING CONTROL
Nominal inverse mapping
Inverse Mapping Correction
uk
xk
NLPN
PLANT
dxk
Adaptive Proportional Feedback
Jacobian Controllability Estimation
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MAPPING LEARNING CONTROL

• MODELING Single Valve

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MAPPING LEARNING CONTROL

• MODELING Full system

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MAPPING LEARNING CONTROL

• MODELING Full system

Supply, Piston, and Return Pressures
Actual and Commanded Speeds
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MAPPING LEARNING CONTROL

• MODELING Full system (Solenoid Currents)

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MAPPING LEARNING CONTROL

• EXPERIMENTAL
• Learning applied to retract motion

Valve motion
Low Pressure
High Pressure
Piston motion
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MAPPING LEARNING CONTROL

• EXPERIMENTAL (30 mm/s commanded)

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MAPPING LEARNING CONTROL

• EXPERIMENTAL

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MAPPING LEARNING CONTROL

• EXPERIMENTAL
• Learning applied to all four (4) EHPVs

Valve motion
Low Pressure
High Pressure
Piston motion
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MAPPING LEARNING CONTROL

• ADAPTIVE TABLE CONTROL
• Pump margin control INCOVA control
• NLPN approximation of inverse Kv mapping using 4
  NLPN

Velocity Performance
Piston Displacement Retraction
Velocity Errors
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MAPPING LEARNING CONTROL

• ADAPTIVE TABLE CONTROL
• Pump margin control INCOVA control
• NLPN approximation of inverse Kv mapping using 4
  NLPN

Velocity Performance
Piston Displacement Extension
Velocity Errors
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PRESENTATION OUTLINE

• FLOW CONDUCTANCE ESTIMATION
• Reported work
• Approaches
• ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND
  CONTROL
• Fixed inverse mapping
• Learning mapping response
• FUTURE WORK
• CONCLUSION

58
FUTURE WORK

• Investigate online application of observer
• Complete velocity error comparison between
  systems response under fixed inverse tables and
  adaptive inverse tables
• Study convergence properties of adaptive
  proportional input and its impact on overall
  stability
• Improve learning applied to 4 EHPVs by NLPN
  adaptive proportional feedback
• Incorporate fault Diagnostics capabilities along
  with mapping learning

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PRESENTATION OUTLINE

• FLOW CONDUCTANCE ESTIMATION
• Reported work
• Approaches
• ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND
  CONTROL
• Fixed inverse mapping
• Learning mapping response
• FUTURE WORK
• CONCLUSION

60
CONCLUSIONS

• Discussed several approaches to the flow
  conductance estimation problem
• Presented a learning method for estimating flow
  conductance
• Presented performance of the INCOVA control
  system under constant and margin pump control for
  fixed inverse valve opening mapping
• Presented Simulations and experimental results on
  applying learning control to the Wheatstone
  Bridge EHPV arrangement