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April 11, 2006 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 Sponsored by: HUSCO International and the Fluid Power Motion Control Center Slide 2 April 11, 20062 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 Spool Valve Spool piece Piston Low Pressure High Pressure Piston motion Spool motion Slide 3 April 11, 20063 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 Piston motion Low Pressure High Pressure Valve motion Slide 4 April 11, 20064 MOTIVATION 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 Pilot Pin Main Poppet Reverse (Nose) Flow Forward (Side) Flow Control Chamber Modulating Spring Coil Armature Bias Spring Pressure Compensating Spring Coil CapAdjustment Screw Input Current U.S. Patents (6,328,275) & (6,745,992) Slide 5 April 11, 20065 MOTIVATION VALVE CHARACTERIZATION Flow Conductance K v or FULLY TURBULENT CHARACTERIZATION Slide 6 April 11, 20066 MOTIVATION FORWARD MAPPING REVERSE MAPPING Forward K v at different input currents [A] Reverse K v at different input currents [A] Side to nose Nose to side Slide 7 April 11, 20067 MOTIVATION Hierarchical control: System controller, pressure controller, function controller Obtain (Operator) desired speed, Calculate equivalent K vEQ Determine Individual K v US PATENT # 6,732,512 & 6,718,759 Read port pressures, P s P R P A P B K vB K vA Determine input current to EHPV i sol = f (K v, P,T) HUSCOS CONTROL TOPOLOGY Calculate desired flow, Q Slide 8 April 11, 20068 MOTIVATION PP i sol KvKv T KvKv PP T EXPERIMENTAL DATA INTERPOLATED AND INVERTED DATA Slide 9 April 11, 20069 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 Slide 10 April 11, 200610 PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION Reported work Approaches ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL Fixed inverse mapping Learning mapping response FUTURE WORK CONCLUSION Slide 11 April 11, 200611 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. 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 Slide 12 April 11, 200612 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 FLOW CONDUCTANCE ESTIMATION Slide 13 April 11, 200613 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 Slide 14 April 11, 200614 FLOW CONDUCTANCE ESTIMATION Liu and Yao (2005) Flow rate observer based on pressure dynamics via sliding mode technique. Needs pistons position, velocity, rode side pressure, and cap side pressure feedback Affected by parametric uncertainty in the knowledge of effective bulk modulus Slide 15 April 11, 200615 FLOW CONDUCTANCE ESTIMATION Liu and Yao (2005) Modeling of valves 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 manufacturers calibration Slide 16 April 11, 200616 APPROACHES Model based Physical sensor INCOVA based Learning based EHPV - Wheatstone Bridge used for motion control of hydraulic pistons FLOW CONDUCTANCE ESTIMATION Slide 17 April 11, 200617 MODEL BASED Object oriented Offline identification Online identification Customization EHPV - Wheatstone Bridge used for motion control of hydraulic pistons FLOW CONDUCTANCE ESTIMATION Slide 18 April 11, 200618 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 FLOW CONDUCTANCE ESTIMATION Slide 19 April 11, 200619 INCOVA BASED Relies on expected pressures for given commanded speed Actual System FLOW CONDUCTANCE ESTIMATION A ABAB PAPA PBPB KvAKvA KvBKvB PSPS PRPR QAQA QBQB P EQ Equivalent System Power Extension Mode (PEM) Slide 20 April 11, 200620 INCOVA BASED Relies on expected pressures for given commanded speed Actual System FLOW CONDUCTANCE ESTIMATION A ABAB PAPA PBPB KvAKvA KvBKvB PSPS PRPR QAQA QBQB P EQ K EQ Equivalent System Power Extension Mode (PEM) Slide 21 April 11, 200621 INCOVA BASED Relies on expected pressures for given commanded speed Actual System FLOW CONDUCTANCE ESTIMATION A ABAB PAPA PBPB KvAKvA KvBKvB PSPS PRPR QAQA QBQB P EQ K EQ Equivalent System Power Extension Mode (PEM) Slide 22 April 11, 200622 LEARNING BASED Assumptions: bulk modulus is sufficiently high Variable volume is sufficiently small. Negligible temperature change Negligible leakage EHPV - Wheatstone Bridge used for motion control of hydraulic pistons FLOW CONDUCTANCE ESTIMATION Chamber pressure equation Slide 23 April 11, 200623 LEARNING BASED FLOW CONDUCTANCE ESTIMATION Let Then Differentiation yields Slide 24 April 11, 200624 LEARNING BASED FLOW CONDUCTANCE ESTIMATION Let Then How good is this approximation? Let Slide 25 April 11, 200625 LEARNING BASED FLOW CONDUCTANCE ESTIMATION Assume that the sup norm of K is bounded, and that K is continuous on the compact set : Then : Slide 26 April 11, 200626 LEARNING BASED FLOW CONDUCTANCE ESTIMATION Actual system Let the observer be Let the error be Then Slide 27 April 11, 200627 LEARNING BASED FLOW CONDUCTANCE ESTIMATION SIMULATIONS Slide 28 April 11, 200628 LEARNING BASED FLOW CONDUCTANCE ESTIMATION SIMULATIONS plots ( = 0) Slide 29 April 11, 200629 LEARNING BASED FLOW CONDUCTANCE ESTIMATION SIMULATIONS plots ( 0, Friction error less than 0.3N) Slide 30 April 11, 200630 LEARNING BASED FLOW CONDUCTANCE ESTIMATION Experimental data (offline) Note: Signals low-pass filtered at 5Hz Slide 31 April 11, 200631 LEARNING BASED FLOW CONDUCTANCE ESTIMATION How small is ? The error is depends on how well we know the friction model Slide 32 April 11, 200632 LEARNING BASED Actual Data FLOW CONDUCTANCE ESTIMATION Slide 33 April 11, 200633 LEARNING BASED Friction model* FLOW CONDUCTANCE ESTIMATION *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. 1746- 1751 Slide 34 April 11, 200634 LEARNING BASED Friction model* FLOW CONDUCTANCE ESTIMATION *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. 1746- 1751 Slide 35 April 11, 200635 PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION Reported work Approaches ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL Fixed inverse mapping Learning mapping response FUTURE WORK CONCLUSION Slide 36 April 11, 200636 PUMP CONTROL Single EHPV Feedback compensation (discrete PI controller) Feedforward compensation (lookup table) EHPV - Wheatstone Bridge used for motion control of hydraulic pistons MAPPING LEARNING & CONTROL EHPV for pump control Slide 37 April 11, 200637 Pump pressure control scheme MAPPING LEARNING & CONTROL PUMP CONTROL Single EHPV Feedback compensation Feedforward compensation Slide 38 April 11, 200638 Pump pressure control scheme MAPPING LEARNING & CONTROL PUMP CONTROL Single EHPV Feedback compensation Feedforward compensation Measured mapping Feedforward mapping Slide 39 April 11, 200639 Closed loop step response MAPPING LEARNING & CONTROL PUMP CONTROL Single EHPV Feedback compensation Feedforward compensation Closed loop tracking response Slide 40 April 11, 200640 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 Slide 41 April 11, 200641 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 Slide 42 April 11, 200642 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 with Pump Margin Control and Fixed Inverse Tables VELOCITY ERRORS Inaccuracy of inverse tables Physical limitations/constraints Slide 43 April 11, 200643 MAPPING LEARNING & CONTROL LEARNING APPLIED TO NONLINEAR SYSTEM CONTROL DESIGN Tracking Error: Error Dynamics: Slide 44 April 11, 200644 MAPPING LEARNING & CONTROL LEARNING APPLIED TO NONLINEAR SYSTEM CONTROL DESIGN Error Dynamics: Deadbeat Control Law: Slide 45 April 11, 200645 MAPPING LEARNING & CONTROL LEARNING APPLIED TO NONLINEAR SYSTEM CONTROL DESIGN Deadbeat Control Law: Proposed Control Law: Slide 46 April 11, 200646 Nominal inverse mapping Inverse Mapping Correction Adaptive Proportional Feedback NLPN PLANT Jacobian Controllability Estimation xkxk dxkdxk ukuk MAPPING LEARNING & CONTROL Slide 47 April 11, 200647 MAPPING LEARNING & CONTROL MODELING: Single Valve Slide 48 April 11, 200648 MAPPING LEARNING & CONTROL MODELING: Full system Slide 49 April 11, 200649 MAPPING LEARNING & CONTROL MODELING: Full system Actual and Commanded Speeds Supply, Piston, and Return Pressures Slide 50 April 11, 200650 MAPPING LEARNING & CONTROL MODELING: Full system (Solenoid Currents) Slide 51 April 11, 200651 MAPPING LEARNING & CONTROL EXPERIMENTAL: Learning applied to retract motion Piston motion Low Pressure High Pressure Valve motion Slide 52 April 11, 200652 MAPPING LEARNING & CONTROL EXPERIMENTAL: (30 mm/s commanded) Slide 53 April 11, 200653 MAPPING LEARNING & CONTROL EXPERIMENTAL: Slide 54 April 11, 200654 MAPPING LEARNING & CONTROL EXPERIMENTAL: Learning applied to all four (4) EHPVs Piston motion Low Pressure High Pressure Valve motion Slide 55 April 11, 200655 MAPPING LEARNING & CONTROL ADAPTIVE TABLE CONTROL Pump margin control + INCOVA control NLPN approximation of inverse Kv mapping using 4 NLPN Velocity Errors Piston Displacement: Retraction Velocity Performance Slide 56 April 11, 200656 MAPPING LEARNING & CONTROL ADAPTIVE TABLE CONTROL Pump margin control + INCOVA control NLPN approximation of inverse Kv mapping using 4 NLPN Piston Displacement: Extension Velocity Errors Velocity Performance Slide 57 April 11, 200657 PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION Reported work Approaches ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL Fixed inverse mapping Learning mapping response FUTURE WORK CONCLUSION Slide 58 April 11, 200658 FUTURE WORK Investigate online application of observer Complete velocity error comparison between systems 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 Slide 59 April 11, 200659 PRESENTATION OUTLINE FLOW CONDUCTANCE ESTIMATION Reported work Approaches ONLINE FLOW CONDUCTANCE MAPPING LEARNING AND CONTROL Fixed inverse mapping Learning mapping response FUTURE WORK CONCLUSION Slide 60 April 11, 200660 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