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
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 2
RESEARCH MOTIVATION Excavator q 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 High Pressure Spool Valve Spool piece Spool motion Kramer (1984), Roberts (1988), Garnjost (1989), Jansson and Palmberg (1990), Aardema (1999), Tabor (2005) October 30, 2007 Low Pressure Piston motion Piston 3
RESEARCH MOTIVATION q CURRENT APPROACH Backhoes § § 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) October 30, 2007 4
RESEARCH MOTIVATION q 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 q DISADVANTAGES § Nonlinear system § Complex control Kramer (1984), Roberts (1988), Garnjost (1989), Jansson and Palmberg (1990), Aardema (1999), Tabor (2005) October 30, 2007 5
RESEARCH MOTIVATION 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) Tabor and Pfaff (2004), Tabor (2004, 2005) October 30, 2007 HUSCO OPEN LOOP CONTROL FOR EHPV’s 6
RESEARCH MOTIVATION q 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? q 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? October 30, 2007 8
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 11
PROBLEM STATEMENT Consider a general discrete-time nonlinear dynamic plant October 30, 2007 12
PROBLEM STATEMENT Consider a general discrete-time nonlinear dynamic plant CONTROL PROBLEM: October 30, 2007 13
PROBLEM STATEMENT Proposition: Similar Results in: Levin and Narendra (1993, 1996), Sadegh(1991, 2001) October 30, 2007 18
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 19
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) October 30, 2007 23
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) October 30, 2007 24
INVERSE MAPPING LEARNING & CTRL NLPN Based Input Matching Control (INMAC) Direct learning accomplished via: Feedforward control by: October 30, 2007 25
INVERSE MAPPING LEARNING & CTRL NLPN Based Input Matching Control (INMAC) Direct learning accomplished via: Functional Approximator: Perceptron with single hidden layer Compatible with lookup tables Nodal Link Perceptron Network (NLPN) Local basis function activation October 30, 2007 27
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) October 30, 2007 29
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) October 30, 2007 30
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: October 30, 2007 31
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 1: Steepest Descent (SD) Control Law: (and non-deadbeat) Adaptation: Conditions: Meets PE condition October 30, 2007 32
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 1: Steepest Descent (SD) Control Law: (and non-deadbeat) If: Then: October 30, 2007 33
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 2: Recursive Least Squares (RLS) Control Law: (and non-deadbeat) Adaptation: Conditions: Meets PE condition October 30, 2007 34
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) Stability Analysis THEOREM 2: Recursive Least Squares (RLS) Control Law: (and non-deadbeat) If: Then: October 30, 2007 35
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) General Case Plant: Example: October 30, 2007 36
INVERSE MAPPING LEARNING & CTRL Composite Input Matching Control (COMPIM) General Case Plant: Feedforward: Direct Learning: October 30, 2007 37
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 38
SIMULATION RESULTS FIRST ORDER LINEAR PLANT Plant: Sampling Time: Parameters: October 30, 2007 39
SIMULATION RESULTS FIRST ORDER NONLINEAR PLANT Plant: Initial Mapping: October 30, 2007 41
SIMULATION RESULTS FIRST ORDER NONLINEAR PLANT RLS: SD: October 30, 2007 42
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 46
APPLICATION TO HYDRAULICS § § § § § Poppet type valve Pilot driven Solenoid activated Internal pressure compensation Virtually ‘zero’ leakage Bidirectional Low hysteresis Low gain initial metering PWM current input Adjustment Screw Modulating Spring Armature Control Chamber Pressure Compensating Spring Coil Cap Input Current Coil U. S. Patents (6, 328, 275) & (6, 745, 992) ELECTRO-HYDRAULIC POPPET VALVE (EHPV) Pilot Pin Armature Bias Spring Main Poppet Forward (Side) Flow October 30, 2007 Reverse (Nose) Flow 47
![APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Forward](https://present5.com/presentation/734ab70053d41e88181498431887605c/image-32.jpg "APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Forward")
APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Forward Kv October 30, 2007 Reverse Kv at different input currents [A] 50
![APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Reverse](https://present5.com/presentation/734ab70053d41e88181498431887605c/image-33.jpg "APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Reverse")
APPLICATION TO HYDRAULICS SIMPLIFIED EHPV MODEL Forward Kv at different input currents [A] Reverse Kv October 30, 2007 Reverse Kv at different input currents [A] 51
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 52
EXPERIENTAL VALIDATION HYDRAULIC TEST-BED CAN bus interface Balluff position/velocity transducer XPC-Target (SIMULINK) Pressure Control Flow Control October 30, 2007 53
EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL Desired Flow Conductance Kv Pump Flow Characteristics October 30, 2007 55
EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: Generic Initial mapping Flow Conductance Kv October 30, 2007 Supply Pressure PS 56
EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: Calibrated Initial mapping Flow Conductance Kv October 30, 2007 Supply Pressure PS 57
EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: SD COMPIM with Generic Initial mapping Flow Conductance Kv October 30, 2007 Supply Pressure PS 58
EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL: RLS COMPIM with Generic Initial map Flow Conductance Kv October 30, 2007 Supply Pressure PS 59
EXPERIENTAL VALIDATION SUPPLY PRESSURE CONTROL SD Flow Conductance Kv October 30, 2007 RLS Flow Conductance Kv 60
EXPERIENTAL VALIDATION FLOW CONTROL Control Topology INCOVA LOGIC (VELOCITY BASED CONTROL) OPERATOR INPUT: Commanded Velocity INVERSE MAPPING (ADAPTIVE LOOK(FIXED LOOK-UP UP TABLE) EHPV Opening COIL CURRENT SERVO (PWM + dither) October 30, 2007 62
EXPERIENTAL VALIDATION FLOW CONTROL Piston Position/Velocity October 30, 2007 Flow Conductance Kv 63
EXPERIENTAL VALIDATION FLOW CONTROL Piston Position/Velocity October 30, 2007 Flow Conductance Kv 64
EXPERIENTAL VALIDATION FLOW CONTROL Piston Position/Velocity October 30, 2007 Flow Conductance Kv 66
EXPERIENTAL VALIDATION HEALTH MONITORING Control Topology October 30, 2007 Flow Conductance Bounds 67
EXPERIENTAL VALIDATION HEALTH MONITORING October 30, 2007 70
PRESENTATION OUTLINE q q q q October 30, 2007 RESEARCH MOTIVATION PROBLEM STATEMENT INVERSE MAPPING LEARNING & STATE CONTROL SIMULATION RESULTS APPLICATION TO HYDRAULICS EXPERIMENTAL VALIDATION CONCLUSION 71
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 October 30, 2007 72
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 October 30, 2007 73
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