Auto-Calibration and Control Applied to Electro-Hydraulic Valves By PATRICK OPDENBOSCH Graduate Research Assistant Manufacturing Research Center Room 259 (404) 894 3256 patrick. opdenbosch@gatech. edu April 11, 2006 Sponsored by: HUSCO International and the Fluid Power Motion Control Center
MOTIVATION q 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 Spool Valve Spool piece Spool motion April 11, 2006 Low Pressure Piston motion Piston 2
MOTIVATION q 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 Valve motion Pressure High Pressure April 11, 2006 Piston motion 3
MOTIVATION § § § § § Poppet type valve Pilot driven Solenoid activated Internal pressure compensation Virtually ‘zero’ leakage Bidirectional Low hysteresis Low gain initial metering PWM current input April 11, 2006 Modulating Spring Armature Control Chamber Pressure Compensating Spring Coil Cap Input Current Coil U. S. Patents (6, 328, 275) & (6, 745, 992) q Electro-Hydraulic Poppet Valve (EHPV) Adjustment Screw Pilot Pin Armature Bias Spring Main Poppet Forward (Side) Flow Reverse (Nose) Flow 4
MOTIVATION q VALVE CHARACTERIZATION Flow Conductance Kv or FULLY TURBULENT CHARACTERIZATION April 11, 2006 5
![MOTIVATION q FORWARD MAPPING Side to nose Forward Kv at different input currents [A]](https://present5.com/presentation/46c73e8cfdeca0742127d5b7b13f864f/image-6.jpg "MOTIVATION q FORWARD MAPPING Side to nose Forward Kv at different input currents [A]")
MOTIVATION q FORWARD MAPPING Side to nose Forward Kv at different input currents [A] q REVERSE MAPPING Nose to side Reverse Kv at different input currents [A] April 11, 2006 6
MOTIVATION Obtain (Operator) desired speed, n HUSCO’S CONTROL TOPOLOGY Calculate desired flow, n. AB = Q US PATENT # 6, 732, 512 & 6, 718, 759 Read port pressures, Ps PR PA PB Calculate equivalent Kv. EQ Determine Individual Kv Kv. B Hierarchical control: System controller, pressure controller, function controller April 11, 2006 Kv. A Determine input current to EHPV isol=f(Kv, DP, T) 7
MOTIVATION DP T EXPERIMENTAL DATA Kv isol Kv T INTERPOLATED AND INVERTED DATA i sol DP April 11, 2006 8
MOTIVATION q Flow conductance online estimation § Accuracy § Computation effort q Online inverse flow conductance mapping learning and control § Effects by input saturation and timevarying dynamics § Maintain tracking error dynamics stable while learning q Fault diagnostics § How can the learned mappings be used for fault detection April 11, 2006 9
PRESENTATION OUTLINE q FLOW CONDUCTANCE ESTIMATION § Reported work § Approaches q ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL § Fixed inverse mapping § Learning mapping response q q April 11, 2006 FUTURE WORK CONCLUSION 10
FLOW CONDUCTANCE ESTIMATION q 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 April 11, 2006 11
FLOW CONDUCTANCE ESTIMATION q 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 12
FLOW CONDUCTANCE ESTIMATION q 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 13
FLOW CONDUCTANCE ESTIMATION q 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 14
FLOW CONDUCTANCE ESTIMATION q 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 15
FLOW CONDUCTANCE ESTIMATION q APPROACHES § § Model based Physical sensor INCOVA based Learning based EHPV - Wheatstone Bridge used for motion control of hydraulic pistons April 11, 2006 16
FLOW CONDUCTANCE ESTIMATION q MODEL BASED § § Object oriented Offline identification Online identification Customization EHPV - Wheatstone Bridge used for motion control of hydraulic pistons April 11, 2006 17
FLOW CONDUCTANCE ESTIMATION q 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 18
FLOW CONDUCTANCE ESTIMATION n q INCOVA BASED § Relies on expected pressures for given commanded speed PR Kv. B QB PB AB QA PA q Power Extension Mode (PEM) PS AA Kv. A Actual System PEQ Equivalent System April 11, 2006 19
FLOW CONDUCTANCE ESTIMATION n q INCOVA BASED § Relies on expected pressures for given commanded speed PR Kv. B QB PB AB QA PA q Power Extension Mode (PEM) PS AA Kv. A Actual System PEQ KEQ Equivalent System April 11, 2006 20
FLOW CONDUCTANCE ESTIMATION n q INCOVA BASED § Relies on expected pressures for given commanded speed PR Kv. B QB PB AB QA PA q Power Extension Mode (PEM) PS AA Kv. A Actual System PEQ KEQ Equivalent System April 11, 2006 21
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Assumptions: Ø bulk modulus is sufficiently high Ø Variable volume is sufficiently small. Ø Negligible temperature change Ø Negligible leakage § Chamber pressure equation EHPV - Wheatstone Bridge used for motion control of hydraulic pistons April 11, 2006 22
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Let § Then § Differentiation yields April 11, 2006 23
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Let § Then § Let § How good is this approximation? April 11, 2006 24
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Assume that the “sup” norm of K is bounded, and that K is continuous on the compact set : § Then : April 11, 2006 25
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Actual system § Let the observer be § Let the error be § Then April 11, 2006 26
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § SIMULATIONS April 11, 2006 27
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § SIMULATIONS plots (d = 0) April 11, 2006 28
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Experimental data (offline) Note: Signals low-pass filtered at 5 Hz April 11, 2006 30
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § How small is d? § The error is § d depends on how well we know the friction model April 11, 2006 31
FLOW CONDUCTANCE ESTIMATION q LEARNING BASED § Actual Data April 11, 2006 32
FLOW CONDUCTANCE ESTIMATION q 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. 17461751 April 11, 2006 33
FLOW CONDUCTANCE ESTIMATION q 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. 17461751 April 11, 2006 34
PRESENTATION OUTLINE q FLOW CONDUCTANCE ESTIMATION § Reported work § Approaches q ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL § Fixed inverse mapping § Learning mapping response q q April 11, 2006 FUTURE WORK CONCLUSION 35
MAPPING LEARNING & CONTROL q PUMP CONTROL § Single EHPV § Feedback compensation (discrete PI controller) § Feedforward compensation (lookup table) EHPV for pump control April 11, 2006 EHPV - Wheatstone Bridge used for motion control of hydraulic pistons 36
MAPPING LEARNING & CONTROL q PUMP CONTROL § Single EHPV § Feedback compensation § Feedforward compensation Pump pressure control scheme April 11, 2006 37
MAPPING LEARNING & CONTROL q PUMP CONTROL § Single EHPV § Feedback compensation § Feedforward compensation Feedforward mapping Measured mapping Pump pressure control scheme April 11, 2006 38
MAPPING LEARNING & CONTROL q PUMP CONTROL § Single EHPV § Feedback compensation § Feedforward compensation Closed loop step response Closed loop tracking response April 11, 2006 39
MAPPING LEARNING & CONTROL q FIXED TABLE CONTROL § Pump control + INCOVA control § No adaptation of inverse Kv mapping § Same inverse Kv mapping for all valves April 11, 2006 Fixed Set Pump Pressure 40
MAPPING LEARNING & CONTROL q FIXED TABLE CONTROL § Pump control + INCOVA control § No adaptation of inverse Kv mapping § Same inverse Kv mapping for all valves April 11, 2006 Pump Margin Control 41
MAPPING LEARNING & CONTROL q FIXED TABLE CONTROL § Pump control + INCOVA control § No adaptation of inverse Kv mapping § Same inverse Kv mapping for all valves q VELOCITY ERRORS § Inaccuracy of inverse tables § Physical limitations/constraints Velocity Errors with Pump Margin Control and Fixed Inverse Tables April 11, 2006 42
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Tracking Error: § Error Dynamics: April 11, 2006 43
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Error Dynamics: § Deadbeat Control Law: April 11, 2006 44
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Deadbeat Control Law: § Proposed Control Law: April 11, 2006 45
MAPPING LEARNING & CONTROL Nominal inverse mapping Inverse Mapping Correction dx k NLPN Adaptive Proportional Feedback April 11, 2006 uk PLANT xk Jacobian Controllability Estimation 46
MAPPING LEARNING & CONTROL q MODELING: Single Valve April 11, 2006 47
MAPPING LEARNING & CONTROL q MODELING: Full system April 11, 2006 48
MAPPING LEARNING & CONTROL q MODELING: Full system Supply, Piston, and Return Pressures Actual and Commanded Speeds April 11, 2006 49
MAPPING LEARNING & CONTROL q MODELING: Full system (Solenoid Currents) April 11, 2006 50
MAPPING LEARNING & CONTROL q EXPERIMENTAL: § Learning applied to retract motion Valve motion Low Pressure High Pressure Piston motion April 11, 2006 51
MAPPING LEARNING & CONTROL q EXPERIMENTAL: (30 mm/s commanded) April 11, 2006 52
MAPPING LEARNING & CONTROL q EXPERIMENTAL: April 11, 2006 53
MAPPING LEARNING & CONTROL q EXPERIMENTAL: § Learning applied to all four (4) EHPVs Valve motion Low Pressure High Pressure Piston motion April 11, 2006 54
MAPPING LEARNING & CONTROL q ADAPTIVE TABLE CONTROL § Pump margin control + INCOVA control § NLPN approximation of inverse Kv mapping using 4 NLPN Velocity Performance Piston Displacement: Retraction April 11, 2006 Velocity Errors 55
MAPPING LEARNING & CONTROL q ADAPTIVE TABLE CONTROL § Pump margin control + INCOVA control § NLPN approximation of inverse Kv mapping using 4 NLPN Velocity Performance Piston Displacement: Extension April 11, 2006 Velocity Errors 56
PRESENTATION OUTLINE q FLOW CONDUCTANCE ESTIMATION § Reported work § Approaches q ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL § Fixed inverse mapping § Learning mapping response q q April 11, 2006 FUTURE WORK CONCLUSION 57
FUTURE WORK q Investigate online application of observer q Complete velocity error comparison between system’s response under fixed inverse tables and adaptive inverse tables q Study convergence properties of adaptive proportional input and its impact on overall stability q Improve learning applied to 4 EHPVs by NLPN + adaptive proportional feedback q Incorporate fault Diagnostics capabilities along with mapping learning April 11, 2006 58
PRESENTATION OUTLINE q FLOW CONDUCTANCE ESTIMATION § Reported work § Approaches q ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL § Fixed inverse mapping § Learning mapping response q q April 11, 2006 FUTURE WORK CONCLUSION 59
CONCLUSIONS q Discussed several approaches to the flow conductance estimation problem q Presented a learning method for estimating flow conductance q Presented performance of the INCOVA control system under constant and margin pump control for fixed inverse valve opening mapping q Presented Simulations and experimental results on applying learning control to the Wheatstone Bridge EHPV arrangement April 11, 2006 60
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