AUTO-CALIBRATION AND CONTROL APPLIED TO ELECTRO-HYDRAULIC VALVES EXPERIMENTS ON HUSCO BLUE TELEHANDLER August 18, 2006 PATRICK OPDENBOSCH Graduate Research Intern INCOVA (262) 513 4408 patrick. opdenbosch@huscointl. com HUSCO International W 239 N 218 Pewaukee Rd. Waukesha, WI 53188 -1638
MOTIVATION HUSCO’S CONTROL TOPOLOGY US PATENT # 6, 732, 512 & 6, 718, 759 Steady State Mapping (Design) Hierarchical control: System controller, pressure controller, function controller Inverse Mapping (Control) HUSCO OPEN LOOP CONTROL FOR EHPV’s 2
MOTIVATION HUSCO’S CONTROL TOPOLOGY US PATENT # 6, 732, 512 & 6, 718, 759 Steady State Mapping (Design) Hierarchical control: System controller, pressure controller, function controller 3 Inverse Mapping (Control)
MOTIVATION Time Commanded Kv Actual Kv Commanded Velocity Actual Velocity Time 4
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 5
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 6
TOPIC REVIEW q 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. 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 7
TOPIC REVIEW q CATERPILLAR 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 (20060064971) § 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 (20060095163) 8
TOPIC REVIEW q CATERPILLAR PATENTS § 20060064971 “Adaptive Position Determining System for Hydraulic Cylinder” Limit Switches 9
TOPIC REVIEW q CATERPILLAR PATENTS Long-Jang Li, US Patent 5, 942, 892 (1999) § 5, 004, 264 “Position Control Device and Automotive Suspension System Employing Same” Position Detector 10
TOPIC REVIEW q CATERPILLAR PATENTS § 20060095163 “Electrohydraulic Control System” Position/Velocity sensor Adaptive scheme: no details found 11
TOPIC REVIEW q 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 12
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 13
SETUP q MOTION CONTROL § Independent coil current control § SIEMENS controller § Supply & return pressure from ISP Supply KSA KSB KAR HUSCO Blue Telehandler KBR Return Boom Function Kinematics 14
SETUP q MOTION CONTROL § Independent coil current control § SIEMENS controller § Supply & return pressure from ISP HUSCO Blue Telehandler PS Pump Unloader PA Diesel Engine Relief Valve PR KSA PB KAR Filter Tank 15 KSB KBR Boom Cylinder
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 16
IMPROVEMENTS q PUMP CONTROL Ripples Pressure override for pump pressure control (ISP code) 17
IMPROVEMENTS DATA SHOWN: Margin added on retract metering mode (PB signal is user commanded, not actual workport pressure) q PUMP CONTROL Current override for unloader coil current control (ISP code) 18
IMPROVEMENTS q ANTI-CAVITATION R 3 /4 KOUT_MAX PIN_MIN = m Keq_d Pmin KIN_MAX Unconstrained Operating Point Keq POUT_MAX Constrained Operating Point 20
IMPROVEMENTS q ANTI-CAVITATION Cavitation 21
IMPROVEMENTS q ANTI-CAVITATION Flow Sharing No Cavitation 22
IMPROVEMENTS q LEARNING Supply KSA KSB EXTEND KAR KBR Return Boom Function 23
IMPROVEMENTS q LEARNING Supply KSA KSB RETRACT KAR KBR Return Boom Function 24
IMPROVEMENTS q LEARNING Supply KSA KSB EXTEND/RETRACT KAR KBR Return Boom Function 25
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 26
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q MAPPING TO BE LEARNED (simplified) Expected curve shift 27
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q MAPPING TO BE LEARNED (simplified) Expected curve shift 28
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Tracking Error: § Error Dynamics: Linear Time Varying System 29
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Error Dynamics: § Deadbeat Control Law: § Closed loop 30
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Deadbeat Control Law: § Proposed Control Law: 31
MAPPING LEARNING & CONTROL Nominal inverse mapping Inverse Mapping Correction d. K icmd Servo NLPN EHPV V Adaptive Proportional Feedback Jacobian Controllability Estimation 32 KV
MAPPING LEARNING & CONTROL q LEARNING APPLIED TO NONLINEAR SYSTEM q CONTROL DESIGN § Proposed Control Law: § Closed loop 33
MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Methods: Ø Least Squares (Recursive) ▫ Noise rejection ▫ Poor time varying parameter tracking capabilities (add covariance reset and forgetting factor – dynamic or static) ▫ New research suggest variablelength moving window* Ø 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. 505 -521. 34
MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Approximations: Ø Previous-point Linearization Ø Stack Operator 35
MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Approximations: Ø Previous-point Linearization Ø Stack Operator Properties 36
MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Approximations: Ø Previous-point Linearization Ø Stack Operator Properties 37
MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Approximations: Ø Previous-point Linearization 38
MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Approximations: Ø Previous-point Linearization How are (d. J, d. Q) and (J*, Q*) related? 39
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 40
EXPERIMENTAL RESULTS Nominal inverse mapping icmd d. K Servo EHPV V Every valve uses a generic Table 41 KV
EXPERIMENTAL RESULTS q PUMP CONTROL: MARGIN 42
EXPERIMENTAL RESULTS 43
EXPERIMENTAL RESULTS 44
EXPERIMENTAL RESULTS q PUMP CONTROL: PS_SETPOINT 45
EXPERIMENTAL RESULTS 46
EXPERIMENTAL RESULTS 47
EXPERIMENTAL RESULTS Nominal inverse mapping Inverse Mapping Correction d. K icmd Servo NLPN V 48 EHPV KV
EXPERIMENTAL RESULTS q PUMP CONTROL: MARGIN 49
EXPERIMENTAL RESULTS 50
EXPERIMENTAL RESULTS 51
EXPERIMENTAL RESULTS q PUMP CONTROL: PS_SETPOINT 55
EXPERIMENTAL RESULTS 56
EXPERIMENTAL RESULTS 57
EXPERIMENTAL RESULTS Nominal inverse mapping Inverse Mapping Correction d. K icmd Servo NLPN V FIXED Proportional Feedback 58 EHPV KV
EXPERIMENTAL RESULTS q PUMP CONTROL: MARGIN 59
EXPERIMENTAL RESULTS 60
EXPERIMENTAL RESULTS 61
EXPERIMENTAL RESULTS 62
EXPERIMENTAL RESULTS 63
EXPERIMENTAL RESULTS 64
EXPERIMENTAL RESULTS 65
EXPERIMENTAL RESULTS 66
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 68
FUTURE WORK q Improve EHPV performance using adaptive proportional feedback q Study convergence properties of adaptive proportional input and its impact on overall stability q Incorporate fault Diagnostics capabilities along with mapping learning q Refine pump controls 69
PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & CONTROL EXPERIMENTAL RESULTS FUTURE WORK CONCLUSIONS 70
CONCLUSIONS q 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. q Satisfactory experimental results were obtained on applying feedforward learning and fixed proportional control to four (4) EHPVs q Experimental verification of improved commanded velocity achievement using mapping learning was presented q The need for good velocity sensor was observed (potential idea for customized sensor was presented) 71
CONCLUSIONS q More refined code (constraints) allowed better control q Unresolved Issues still exist with parameter estimation and adaptive proportional control portion q Experimental validation of faster mapping learning with proportional feedback in place (fixed) q Learning grid can be fixed based on curve shifting behavior 72
QUESTIONS? ? 73
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