1. AUTO-CALIBRATION AND CONTROL APPLIED TO ELECTRO-HYDRAULIC VALVES
EXPERIMENTS ON HUSCO BLUE TELEHANDLER August 18, 2006
PATRICK OPDENBOSCH
Graduate Research Intern
INCOVA
(262)
HUSCO International
W239 N218 Pewaukee Rd.
Waukesha, WI

2. MOTIVATION HUSCO’S CONTROL TOPOLOGY Steady State Mapping (Design)
US PATENT # 6,732,512 & 6,718,759
Steady State Mapping (Design)
Inverse Mapping (Control)
Hierarchical control: System controller, pressure controller, function controller
HUSCO OPEN LOOP CONTROL FOR EHPV’s

3. MOTIVATION HUSCO’S CONTROL TOPOLOGY Steady State Mapping (Design)
US PATENT # 6,732,512 & 6,718,759
Steady State Mapping (Design)
Inverse Mapping (Control)
Hierarchical control: System controller, pressure controller, function controller

4. MOTIVATION Time Commanded Kv Actual Kv Commanded Velocity
Actual Velocity
Time

5. 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

6. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

7. TOPIC REVIEW PURDUE PAPERS
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

8. TOPIC REVIEW CATERPILLAR PATENTS NEW PATENTS
Aardema, J.A. and Koehler, D.W., (1999) System and method for controlling an independent metering valve, U.S. Patent (5,960,695)
Aardema, J.A. and Koehler, D.W., (1999) System and method for controlling an independent metering valve, U.S. Patent (5,947,140)
Kozaki, T., Ishikawa, H., Yasui, H., et al., (1991) Position control device and automotive suspension system employing same, U.S. Patent (5,004,264)
NEW PATENTS
Reedy, J.T., Cone, R.D., Kloeppel, G.R., et al., (2006) Adaptive position determining system for hydraulic cylinder, U.S. Patent ( )
Du, H., (2006) Hydraulic system health indicator, U.S. Patent (7,043,975)
Wear, J.A., Du, H., Ferkol, G.A., et al., (2006) Electrohydraulic control system, U.S. Patent ( )

9. TOPIC REVIEW CATERPILLAR PATENTS
“Adaptive Position Determining System for Hydraulic Cylinder”
Limit Switches

10. TOPIC REVIEW CATERPILLAR PATENTS
Long-Jang Li, US Patent 5,942,892 (1999)
CATERPILLAR PATENTS
5,004,264 “Position Control Device and Automotive Suspension System Employing Same”
Position Detector

11. TOPIC REVIEW CATERPILLAR PATENTS
“Electrohydraulic Control System”
Position/Velocity sensor
Adaptive scheme: no details found

12. TOPIC REVIEW CATERPILLAR PATENTS
7,043,975 “Hydraulic System Health Indicator”
Using Lyapunov stability theory
Health Monitoring using Bulk modulus and other model-based parameters
(Position/velocity sensor)
Based on pump pressure discharge dynamics or cylinder head end control pressure

13. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

14. SETUP MOTION CONTROL Independent coil current control
SIEMENS controller
Supply & return pressure from ISP
Supply
KSA
KSB
HUSCO Blue Telehandler
KAR
KBR
Return
Boom Function
Boom Function Kinematics

15. HUSCO Blue Telehandler
SETUP
MOTION CONTROL
Independent coil current control
SIEMENS controller
Supply & return pressure from ISP PS
HUSCO Blue Telehandler
Diesel Engine
Pump
Filter
Tank
Relief Valve
Unloader
KSA
KSB
KAR
KBR
Boom Cylinder PA PB PR

16. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

17. Pressure override for pump pressure control (ISP code)
IMPROVEMENTS
PUMP CONTROL
Ripples
Pressure override for pump pressure control (ISP code)

18. IMPROVEMENTS PUMP CONTROL
DATA SHOWN: Margin added on retract metering mode
(PB signal is user commanded, not actual workport pressure)
PUMP CONTROL
Current override for unloader coil current control (ISP code)

19. Current override for unloader coil current control (ISP code)
IMPROVEMENTS
PUMP CONTROL
Current override for unloader coil current control (ISP code)

20. IMPROVEMENTS ANTI-CAVITATION Unconstrained Operating Point
KOUT_MAX
m = R3/4
PIN_MIN
Unconstrained Operating Point
Keq_dPmin
KIN_MAX
Keq
POUT_MAX
Constrained Operating Point

21. IMPROVEMENTS
ANTI-CAVITATION
Cavitation

22. IMPROVEMENTS
ANTI-CAVITATION
Flow Sharing
No Cavitation

23. IMPROVEMENTS LEARNING Supply KSA KSB EXTEND KAR KBR Return
Boom Function

24. IMPROVEMENTS LEARNING Supply KSA KSB RETRACT KAR KBR Return
Boom Function

25. IMPROVEMENTS LEARNING Supply KSA KSB EXTEND/RETRACT KAR KBR Return
Boom Function

26. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

27. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
MAPPING TO BE LEARNED (simplified)
Expected curve shift

28. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
MAPPING TO BE LEARNED (simplified)
Expected curve shift

29. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Tracking Error:
Error Dynamics:
Linear Time Varying System

30. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Error Dynamics:
Deadbeat Control Law:
Closed loop

31. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Deadbeat Control Law:
Proposed Control Law:

32. MAPPING LEARNING & CONTROL
Nominal inverse mapping
Inverse Mapping Correction
icmd KV
NLPN
Servo
EHPV
dKV
Adaptive Proportional Feedback
Jacobian Controllability Estimation

33. MAPPING LEARNING & CONTROL
LEARNING APPLIED TO NONLINEAR SYSTEM
CONTROL DESIGN
Proposed Control Law:
Closed loop

34. MAPPING LEARNING & CONTROL
IDENTIFICATION DESIGN
Methods:
Least Squares (Recursive)
Noise rejection
Poor time varying parameter tracking capabilities (add covariance reset and forgetting factor – dynamic or static)
New research suggest variable-length moving window*
Gradient Based
Sensitive to noise
Better time varying parameter tracking capabilities
Gradient step size must be chosen carefully
Identification of time varying parameter for a linear system
(*) Jiang, J. and Zhang, Y. (2004), A Novel Variable-Length Sliding Window Blockwise Least-Squares Algorithm for Online Estimation of Time-Varying Parameters, Intl. J. Adaptive Ctrl & Signal Proc., Vol 18, No. 6, pp

35. MAPPING LEARNING & CONTROL
IDENTIFICATION DESIGN
Approximations:
Previous-point Linearization
Stack Operator

36. MAPPING LEARNING & CONTROL
IDENTIFICATION DESIGN
Approximations:
Previous-point Linearization
Stack Operator Properties

37. MAPPING LEARNING & CONTROL
IDENTIFICATION DESIGN
Approximations:
Previous-point Linearization
Stack Operator Properties

38. MAPPING LEARNING & CONTROL
IDENTIFICATION DESIGN
Approximations:
Previous-point Linearization

39. MAPPING LEARNING & CONTROL
IDENTIFICATION DESIGN
Approximations:
Previous-point Linearization
How are (dJ,dQ) and (J*,Q*) related?

40. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

41. Every valve uses a generic Table
EXPERIMENTAL RESULTS
Nominal inverse mapping
icmd KV
Servo
EHPV
dKV
Every valve uses a generic Table

42. EXPERIMENTAL RESULTS
PUMP CONTROL: MARGIN

43. EXPERIMENTAL RESULTS

44. EXPERIMENTAL RESULTS

45. EXPERIMENTAL RESULTS
PUMP CONTROL: PS_SETPOINT

46. EXPERIMENTAL RESULTS

47. EXPERIMENTAL RESULTS

48. EXPERIMENTAL RESULTS icmd KV dKV NLPN EHPV Nominal inverse mapping
Inverse Mapping Correction
icmd KV
NLPN
Servo
EHPV
dKV

49. EXPERIMENTAL RESULTS
PUMP CONTROL: MARGIN

50. EXPERIMENTAL RESULTS

51. EXPERIMENTAL RESULTS

52. EXPERIMENTAL RESULTS
PUMP CONTROL: MARGIN

53. EXPERIMENTAL RESULTS

54. EXPERIMENTAL RESULTS

55. EXPERIMENTAL RESULTS
PUMP CONTROL: PS_SETPOINT

56. EXPERIMENTAL RESULTS

57. EXPERIMENTAL RESULTS

58. EXPERIMENTAL RESULTS icmd KV dKV NLPN EHPV Nominal inverse mapping
Inverse Mapping Correction
icmd KV
NLPN
Servo
EHPV
dKV
FIXED Proportional Feedback

59. EXPERIMENTAL RESULTS
PUMP CONTROL: MARGIN

60. EXPERIMENTAL RESULTS

61. EXPERIMENTAL RESULTS

62. EXPERIMENTAL RESULTS

63. EXPERIMENTAL RESULTS

64. EXPERIMENTAL RESULTS

65. EXPERIMENTAL RESULTS

66. EXPERIMENTAL RESULTS

67. EXPERIMENTAL RESULTS
SHOW LEARNED MAPS

68. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

69. FUTURE WORK
Improve EHPV performance using adaptive proportional feedback
Study convergence properties of adaptive proportional input and its impact on overall stability
Incorporate fault Diagnostics capabilities along with mapping learning
Refine pump controls

70. PRESENTATION OUTLINE MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS
MAPPING LEARNING & CONTROL
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS

71. CONCLUSIONS
The performance of the INCOVA control system under Ps_setpoint and margin pump control was improved when using mapping learning as oppose to using fixed inverse valve opening mapping.
Satisfactory experimental results were obtained on applying feedforward learning and fixed proportional control to four (4) EHPVs
Experimental verification of improved commanded velocity achievement using mapping learning was presented
The need for good velocity sensor was observed (potential idea for customized sensor was presented)

72. CONCLUSIONS More refined code (constraints) allowed better control
Unresolved Issues still exist with parameter estimation and adaptive proportional control portion
Experimental validation of faster mapping learning with proportional feedback in place (fixed)
Learning grid can be fixed based on curve shifting behavior

73. QUESTIONS?
???