Скачать презентацию AUTO-CALIBRATION AND CONTROL APPLIED TO ELECTRO-HYDRAULIC VALVES EXPERIMENTS Скачать презентацию AUTO-CALIBRATION AND CONTROL APPLIED TO ELECTRO-HYDRAULIC VALVES EXPERIMENTS

5f330ff3b9d12868158a014c3defc0f4.ppt

  • Количество слайдов: 68

AUTO-CALIBRATION AND CONTROL APPLIED TO ELECTRO-HYDRAULIC VALVES EXPERIMENTS ON HUSCO BLUE TELEHANDLER August 18, 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. [email protected] 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 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 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 Time Commanded Kv Actual Kv Commanded Velocity Actual Velocity Time 4

MOTIVATION q Flow conductance online estimation § Accuracy § Computation effort q Online inverse 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 & 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 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. , 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” 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) § 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: 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 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 & 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 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 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 & 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 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, 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 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 Cavitation 21

IMPROVEMENTS q ANTI-CAVITATION Flow Sharing No Cavitation 22 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 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 RETRACT KAR KBR Return Boom Function 24

IMPROVEMENTS q LEARNING Supply KSA KSB EXTEND/RETRACT KAR KBR Return Boom Function 25 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 & 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 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 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 § 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 § 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 § 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 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 § 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) ▫ 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 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 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 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 38

MAPPING LEARNING & CONTROL q IDENTIFICATION DESIGN § Approximations: Ø Previous-point Linearization How are 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 & 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 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 q PUMP CONTROL: MARGIN 42

EXPERIMENTAL RESULTS 43 EXPERIMENTAL RESULTS 43

EXPERIMENTAL RESULTS 44 EXPERIMENTAL RESULTS 44

EXPERIMENTAL RESULTS q PUMP CONTROL: PS_SETPOINT 45 EXPERIMENTAL RESULTS q PUMP CONTROL: PS_SETPOINT 45

EXPERIMENTAL RESULTS 46 EXPERIMENTAL RESULTS 46

EXPERIMENTAL RESULTS 47 EXPERIMENTAL RESULTS 47

EXPERIMENTAL RESULTS Nominal inverse mapping Inverse Mapping Correction d. K icmd Servo NLPN V 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 q PUMP CONTROL: MARGIN 49

EXPERIMENTAL RESULTS 50 EXPERIMENTAL RESULTS 50

EXPERIMENTAL RESULTS 51 EXPERIMENTAL RESULTS 51

EXPERIMENTAL RESULTS q PUMP CONTROL: PS_SETPOINT 55 EXPERIMENTAL RESULTS q PUMP CONTROL: PS_SETPOINT 55

EXPERIMENTAL RESULTS 56 EXPERIMENTAL RESULTS 56

EXPERIMENTAL RESULTS 57 EXPERIMENTAL RESULTS 57

EXPERIMENTAL RESULTS Nominal inverse mapping Inverse Mapping Correction d. K icmd Servo NLPN V 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 q PUMP CONTROL: MARGIN 59

EXPERIMENTAL RESULTS 60 EXPERIMENTAL RESULTS 60

EXPERIMENTAL RESULTS 61 EXPERIMENTAL RESULTS 61

EXPERIMENTAL RESULTS 62 EXPERIMENTAL RESULTS 62

EXPERIMENTAL RESULTS 63 EXPERIMENTAL RESULTS 63

EXPERIMENTAL RESULTS 64 EXPERIMENTAL RESULTS 64

EXPERIMENTAL RESULTS 65 EXPERIMENTAL RESULTS 65

EXPERIMENTAL RESULTS 66 EXPERIMENTAL RESULTS 66

PRESENTATION OUTLINE q q q q MOTIVATION TOPIC REVIEW SETUP IMPROVEMENTS MAPPING LEARNING & 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 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 & 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 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 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 QUESTIONS? ? 73