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A Ph.D. Dissertation Defense Presented to the Academic Faculty By PATRICK OP DEN BOSCH

Auto-calibration & Control Applied To Electro-Hydraulic Valves. A Ph.D. Dissertation Defense Presented to the Academic Faculty By PATRICK OP DEN BOSCH Committee Members : Dr. Nader Sadegh (Co-Chair, ME) Dr. Wayne Book (Co-Chair, ME) Dr. Chris Paredis (ME)

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A Ph.D. Dissertation Defense Presented to the Academic Faculty By PATRICK OP DEN BOSCH

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  1. Auto-calibration & Control Applied To Electro-Hydraulic Valves A Ph.D. Dissertation Defense Presented to the Academic Faculty By PATRICK OP DEN BOSCH Committee Members: Dr. Nader Sadegh (Co-Chair, ME) Dr. Wayne Book (Co-Chair, ME) Dr. Chris Paredis (ME) Dr. Bonnie Heck Ferri (ECE) Dr. Roger Yang (HUSCO Intl.) The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA October 30, 2007

  2. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  3. RESEARCH MOTIVATION Excavator • CURRENT APPROACH • Electronic control • 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 Kramer (1984), Roberts (1988), Garnjost (1989), Jansson and Palmberg (1990), Aardema (1999), Tabor (2005) Piston Piston motion

  4. RESEARCH MOTIVATION Backhoes • CURRENT APPROACH • Electronic control • 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 Kramer (1984), Roberts (1988), Garnjost (1989), Jansson and Palmberg (1990), Aardema (1999), Tabor (2005)

  5. RESEARCH MOTIVATION • ADVANTAGES • Independent control • More degrees of freedom • More efficient operation • Simple circuit • Ease in maintenance • Distributed system • No need to customize NASA Ames Flight Simulator • DISADVANTAGES • Nonlinear system • Complex control Kramer (1984), Roberts (1988), Garnjost (1989), Jansson and Palmberg (1990), Aardema (1999), Tabor (2005)

  6. RESEARCH MOTIVATION Tabor and Pfaff (2004), Tabor (2004,2005) HUSCO’S CONTROL TOPOLOGY INCOVA LOGIC (VELOCITY BASED CONTROL) Steady State Mapping (Design) OPERATOR INPUT: Commanded Velocity INVERSE MAPPING (FIXED LOOK-UP TABLE) EHPV Opening COIL CURRENT SERVO (PWM + dither) Inverse Mapping (Control) HUSCO OPEN LOOP CONTROL FOR EHPV’s

  7. RESEARCH MOTIVATION • Theoretical Research Questions • How well can the system’s inverse input-state mapping be learned online while trying to achieve state tracking control? • How can the tracking error dynamics and mapping errors be driven arbitrarily close to zero with an auto-calibration method? • Experimental Research Questions • How can the performance of solenoid driven poppet valves be improved? • How well can these calibration mappings be learned online? • How can the learned mappings be used for fault detection?

  8. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  9. PROBLEM STATEMENT Consider a general discrete-time nonlinear dynamic plant

  10. PROBLEM STATEMENT Consider a general discrete-time nonlinear dynamic plant CONTROL PROBLEM:

  11. PROBLEM STATEMENT Proposition: Similar Results in: Levin and Narendra (1993,1996), Sadegh(1991,2001)

  12. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  13. INVERSE MAPPING LEARNING & CTRL Inverse Model Control Internal Model Control Recurrent hybrid NN Direct and indirect learning approach Backpropagation training Requires feedback controller Pham and Yildirim (2000, 2002)

  14. INVERSE MAPPING LEARNING & CTRL The plant is linearized about a desired state trajectory A Nodal Link Perceptron Network (NLPN) is employed in the feedforward loop and trained with feedback state error The control scheme needs the plant Jacobian and controllability matrices, obtained offline Approximations of the Jacobian and controllability matrices can be used without loosing closed loop stability Sadegh (1991,1993,1995)

  15. INVERSE MAPPING LEARNING & CTRL NLPN Based Input Matching Control (INMAC) Direct learning accomplished via: Feedforward control by:

  16. INVERSE MAPPING LEARNING & CTRL NLPN Based Input Matching Control (INMAC) Direct learning accomplished via: Functional Approximator: Perceptron with single hidden layer Nodal Link Perceptron Network (NLPN) Compatible with lookup tables Local basis function activation

  17. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM)

  18. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM)

  19. INVERSE MAPPING LEARNING & CTRL Deadbeat Control and Non-deadbeat Control Deadbeat Control Law: Non-deadbeat Control Law: Example: Linear Time Invariant Plant Deadbeat: Non-deadbeat:

  20. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 1: Steepest Descent (SD) (and non-deadbeat) Control Law: Adaptation: Conditions: Meets PE condition

  21. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 1: Steepest Descent (SD) (and non-deadbeat) Control Law: If: Then:

  22. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 2: Recursive Least Squares (RLS) (and non-deadbeat) Control Law: Adaptation: Conditions: Meets PE condition

  23. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 2: Recursive Least Squares (RLS) (and non-deadbeat) Control Law: If: Then:

  24. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) General Case Plant: Example:

  25. INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) General Case Plant: Feedforward: Direct Learning:

  26. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  27. SIMULATION RESULTS FIRST ORDER LINEAR PLANT Sampling Time: Plant: Parameters:

  28. SIMULATION RESULTS FIRST ORDER NONLINEAR PLANT Plant: Initial Mapping:

  29. SIMULATION RESULTS FIRST ORDER NONLINEAR PLANT RLS: SD:

  30. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  31. APPLICATION TO HYDRAULICS ELECTRO-HYDRAULIC POPPET VALVE (EHPV) Adjustment Screw Coil Cap • 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

  32. APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Forward Kv Reverse Kv at different input currents [A]

  33. APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Reverse Kv Reverse Kv at different input currents [A]

  34. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  35. EXPERIENTAL VALIDATION HYDRAULIC TEST-BED CAN bus interface Balluff position/velocity transducer XPC-Target (SIMULINK) Pressure Control Flow Control

  36. EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL Desired Flow Conductance Kv Pump Flow Characteristics

  37. EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: Generic Initial mapping Flow Conductance Kv Supply Pressure PS

  38. EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: Calibrated Initial mapping Flow Conductance Kv Supply Pressure PS

  39. EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: SD COMPIM with Generic Initial mapping Flow Conductance Kv Supply Pressure PS

  40. EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: RLS COMPIM with Generic Initial map Flow Conductance Kv Supply Pressure PS

  41. EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL SD Flow Conductance Kv RLS Flow Conductance Kv

  42. EXPERIENTAL VALIDATION FLOW CONTROL Control Topology INCOVA LOGIC (VELOCITY BASED CONTROL) OPERATOR INPUT: Commanded Velocity INVERSE MAPPING (FIXED LOOK-UP TABLE) INVERSE MAPPING (ADAPTIVE LOOK-UP TABLE) EHPV Opening COIL CURRENT SERVO (PWM + dither)

  43. EXPERIENTAL VALIDATION FLOW CONTROL Flow Conductance Kv Piston Position/Velocity

  44. EXPERIENTAL VALIDATION FLOW CONTROL Flow Conductance Kv Piston Position/Velocity

  45. EXPERIENTAL VALIDATION FLOW CONTROL Flow Conductance Kv Piston Position/Velocity

  46. EXPERIENTAL VALIDATION HEALTH MONITORING Flow Conductance Bounds Control Topology

  47. EXPERIENTAL VALIDATION HEALTH MONITORING

  48. PRESENTATION OUTLINE • RESEARCH MOTIVATION • PROBLEM STATEMENT • INVERSE MAPPING LEARNING & STATE CONTROL • SIMULATION RESULTS • APPLICATION TO HYDRAULICS • EXPERIMENTAL VALIDATION • CONCLUSION

  49. CONCLUSIONS RESEARCH CONTRIBUTIONS • Deadbeat/non-deadbeat control method based on input matching with composite adaptation • Rigorous closed-loop stability analyses for the above controllers using steepest descent and recursive least squares methods • A procedure to handle arbitrary state and input delays • A model of the EHPV • Intelligent control technology for the EHPV RESEARCH IMPACT • An alternative discrete-time control design based on an auto-calibration scheme for nonlinear systems • Improvement of hydraulic controls using solenoid driven valves based on calibration routines • Intelligent control technology for the hydraulic industry • Easily extended to other engineering applications

  50. CONCLUSIONS FUTURE RESEARCH • Extend these results for output control • Consider/develop other schemes that suffers less from the curse of dimensionality • Relax the PE condition • Apply this scheme to other hydraulic component with higher order dynamics • Apply this control method to other metering modes along with multi-function cases and mode switching THANK YOU FOR YOUR ATTENTION

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