1. Auto-Calibration and Control Applied to Electro-Hydraulic ValvesBy
PATRICK OPDENBOSCH
Graduate Research Assistant
Manufacturing Research Center Room 259
(404)
April 11, 2006
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
High
Pressure
Low
Pressure
Spool Valve
Spool piece
Spool motion
Piston
Piston motion
April 11, 2006

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
April 11, 2006

4. MOTIVATION Electro-Hydraulic Poppet Valve (EHPV) Poppet type valve
Adjustment
Screw
Coil Cap
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
April 11, 2006

5. FULLY TURBULENT CHARACTERIZATION
MOTIVATION
VALVE CHARACTERIZATION
Flow Conductance Kv or
FULLY TURBULENT CHARACTERIZATION
April 11, 2006

6. MOTIVATION FORWARD MAPPING REVERSE MAPPING April 11, 2006 Side to nose
Forward Kv at different input currents [A]
Nose to side
Reverse Kv at different input currents [A]
April 11, 2006

7. MOTIVATION HUSCO’S CONTROL TOPOLOGY Obtain (Operator) desired speed, n
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 isol=f(Kv,DP,T)
April 11, 2006

8. INTERPOLATED AND INVERTED DATA
MOTIVATION DP
isol Kv T
EXPERIMENTAL DATA Kv DP
isol T
INTERPOLATED AND INVERTED DATA
April 11, 2006

9. 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
April 11, 2006

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
April 11, 2006

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
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
Liu, S. and Yao, B., (2005), Automated modeling of cartridge valve flow mapping, in Proc: IEEE/ASME International Conference on Advanced Intelligent Mechatronics pp
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
April 11, 2006

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
April 11, 2006

13. 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
April 11, 2006

14. FLOW CONDUCTANCE ESTIMATION
Liu and Yao (2005)
Flow rate observer based on pressure dynamics via sliding mode technique.
Needs piston’s position, velocity, rode side pressure, and cap side pressure feedback
Affected by parametric uncertainty in the knowledge of effective bulk modulus
April 11, 2006

15. FLOW CONDUCTANCE ESTIMATION
Liu and Yao (2005)
Modeling of valve’s 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 manufacturer’s calibration
April 11, 2006

16. FLOW CONDUCTANCE ESTIMATION
APPROACHES
Model based
Physical sensor
INCOVA based
Learning based
EHPV - Wheatstone Bridge used for motion control of hydraulic pistons
April 11, 2006

17. FLOW CONDUCTANCE ESTIMATION
MODEL BASED
Object oriented
Offline identification
Online identification
Customization
EHPV - Wheatstone Bridge used for motion control of hydraulic pistons
April 11, 2006

18. 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
April 11, 2006

19. FLOW CONDUCTANCE ESTIMATIONAA AB PA PB
KvA
KvB PS PR n QA QB
INCOVA BASED
Relies on expected pressures for given commanded speed
Power Extension Mode (PEM)
Actual System
PEQ
Equivalent System
April 11, 2006

20. FLOW CONDUCTANCE ESTIMATIONAA AB PA PB
KvA
KvB PS PR n QA QB
INCOVA BASED
Relies on expected pressures for given commanded speed
Power Extension Mode (PEM)
Actual System
PEQ
KEQ
Equivalent System
April 11, 2006

21. FLOW CONDUCTANCE ESTIMATIONAA AB PA PB
KvA
KvB PS PR n QA QB
INCOVA BASED
Relies on expected pressures for given commanded speed
Power Extension Mode (PEM)
Actual System
PEQ
KEQ
Equivalent System
April 11, 2006

22. 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
April 11, 2006

23. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
Let
Then
Differentiation yields
April 11, 2006

24. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
Let
Then
Let
How good is this approximation?
April 11, 2006

25. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
Assume that the “sup” norm of K is bounded, and that K is continuous on the compact set :
Then :
April 11, 2006

26. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
Actual system
Let the observer be
Let the error be
Then
April 11, 2006

27. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
SIMULATIONS
April 11, 2006

28. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
SIMULATIONS plots (d = 0)
April 11, 2006

29. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
SIMULATIONS plots (d ≠0, Friction error less than 0.3N)
April 11, 2006

30. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
Experimental data (offline)
Note: Signals low-pass filtered at 5Hz
April 11, 2006

31. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
How small is d?
The error is
d depends on how well we know the friction model
April 11, 2006

32. FLOW CONDUCTANCE ESTIMATION
LEARNING BASED
Actual Data
April 11, 2006

33. 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
April 11, 2006

34. 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
April 11, 2006

35. PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION
Reported work
Approaches
ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL
Fixed inverse mapping
Learning mapping response
FUTURE WORK
CONCLUSION
April 11, 2006

36. 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
April 11, 2006

37. MAPPING LEARNING & CONTROL
PUMP CONTROL
Single EHPV
Feedback compensation
Feedforward compensation
Pump pressure control scheme
April 11, 2006

38. MAPPING LEARNING & CONTROL
PUMP CONTROL
Single EHPV
Feedback compensation
Feedforward compensation
Feedforward mapping
Measured mapping
Pump pressure control scheme
April 11, 2006

39. MAPPING LEARNING & CONTROL
PUMP CONTROL
Single EHPV
Feedback compensation
Feedforward compensation
Closed loop step response
Closed loop tracking response
April 11, 2006

40. 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
April 11, 2006

41. 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
April 11, 2006

42. 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
April 11, 2006

43. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Tracking Error:
Error Dynamics:
April 11, 2006

44. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Error Dynamics:
Deadbeat Control Law:
April 11, 2006

45. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Deadbeat Control Law:
Proposed Control Law:
April 11, 2006

46. MAPPING LEARNING & CONTROL
Nominal inverse mapping
Inverse Mapping Correction uk xk
NLPN
PLANT
dxk
Adaptive Proportional Feedback
Jacobian Controllability Estimation
April 11, 2006

47. MAPPING LEARNING & CONTROL
MODELING: Single Valve
April 11, 2006

48. MAPPING LEARNING & CONTROL
MODELING: Full system
April 11, 2006

49. MAPPING LEARNING & CONTROL
MODELING: Full system
Supply, Piston, and Return Pressures
Actual and Commanded Speeds
April 11, 2006

50. MAPPING LEARNING & CONTROL
MODELING: Full system (Solenoid Currents)
April 11, 2006

51. MAPPING LEARNING & CONTROL
EXPERIMENTAL:
Learning applied to retract motion
Valve motion
Low
Pressure
High
Pressure
Piston motion
April 11, 2006

52. MAPPING LEARNING & CONTROL
EXPERIMENTAL: (30 mm/s commanded)
April 11, 2006

53. MAPPING LEARNING & CONTROL
EXPERIMENTAL:
April 11, 2006

54. MAPPING LEARNING & CONTROL
EXPERIMENTAL:
Learning applied to all four (4) EHPVs
Valve motion
Low
Pressure
High
Pressure
Piston motion
April 11, 2006

55. 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
April 11, 2006

56. 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
April 11, 2006

57. PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION
Reported work
Approaches
ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL
Fixed inverse mapping
Learning mapping response
FUTURE WORK
CONCLUSION
April 11, 2006

58. FUTURE WORK Investigate online application of observer
Complete velocity error comparison between system’s 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
April 11, 2006

59. PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION
Reported work
Approaches
ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL
Fixed inverse mapping
Learning mapping response
FUTURE WORK
CONCLUSION
April 11, 2006

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
April 11, 2006