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Model-based Real-Time Hybrid Simulation for Large-Scale Experimental Evaluation

Model-based Real-Time Hybrid Simulation for Large-Scale Experimental Evaluation. Brian M. Phillips University of Illinois. B. F. Spencer, Jr. University of Illinois. Yunbyeong Chae Lehigh University. Tony A. Friedman Purdue University. Karim Kazemibidokhti Lehigh University.

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Model-based Real-Time Hybrid Simulation for Large-Scale Experimental Evaluation

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  1. Model-based Real-Time Hybrid Simulation for Large-Scale Experimental Evaluation Brian M. Phillips University of Illinois B. F. Spencer, Jr. University of Illinois YunbyeongChae Lehigh University Tony A. Friedman Purdue University KarimKazemibidokhti Lehigh University James M. Ricles Lehigh University Shirley J. Dyke Purdue University Quake Summit 2012 Boston, Massachusetts June, 2012

  2. Introduction

  3. Large-Scale RTHS Project • Performance-based design and real-time, large-scale testing to enable implementation of advanced damping systems • Joint project between Illinois, Purdue, Lehigh, UConn, and CCNY

  4. Hybrid Simulation Loop Servo-Hydraulic System • Servo-hydraulic system introduces dynamics into the hybrid simulation loop • Actuator dynamics are coupled to the specimen through natural velocity feedback • When multiple actuators are connected to the same specimen, the actuator dynamics become coupled x u Numerical Substructure Experimental Substructure Loading System fmeas f Sensors

  5. Servo-Hydraulic System Model

  6. MIMOSystem Model Servo-Hydraulic System Gxu(s) + + − − Actuator Servo-Controllerand Servo-Valve Specimen Natural Velocity Feedback

  7. Multi-Actuator Setup Actuator 3 Servo-Controller 3 Actuator 2 Servo-Controller 2 Computer Interface Actuator 1 Servo-Controller 1 Equations of motion:

  8. MIMO System Model Component models: Servo-Hydraulic System Gxu(s) u f x + + − − Actuator Servo-Controllerand Servo-Valve Specimen Servo-hydraulic system model: Natural Velocity Feedback

  9. Model-Based Actuator Control

  10. Regulator Redesign Servo-hydraulic system transfer function in state space: Tracking error: Ideal system with perfect tracking: Deviation system:

  11. Model-Based ControlFeedforward Feedback Links Total control law is a combination of feedforward and feedback: uFF GFF(s) FeedforwardController + e uFB u + Gxu(s) LQG + - Feedback Controller Servo-Hydraulic Dynamics

  12. Large-ScaleExperimental Study

  13. Prototype Structure Actuator 3 Actuator 2 Actuator 1 Experimental Substructure

  14. MIMO Transfer FunctionMagnitude Input 1 Input 2 Input 3 Output 1 Output 2 Output 3

  15. MIMO Transfer FunctionPhase Input 1 Input 2 Input 3 Output 1 Output 2 Output 3

  16. 5 Hz BLWN Tracking RMS Error Norm No Comp: 44.8% FF + FB: 3.75 % No Comp: 47.8% FF + FB: 4.43 % No Comp: 50.8% FF + FB: 4.39 %

  17. 15 Hz BLWN Tracking RMS Error Norm No Comp: 97.8% FF + FB: 10.7 % No Comp: 96.6% FF + FB: 13.5 % No Comp: 98.1% FF + FB: 11.5 %

  18. Prototype Structure Actuator 3 Actuator 2 Actuator 1 Total Structure Experimental Substructure

  19. RTHS Parameters • Ground acceleration • 0.12x NS component 1994 Northridge earthquake • Numerical integration • CDM at 1024 Hz • Actuator control • FF + FB control w/ coupling • Structural control • Clipped-optimal control algorithm (Dyke et al., 1996)

  20. Semi-Active RTHS Results0.12x Northridge

  21. Conclusions

  22. Conclusions • The source of actuator dynamics including actuator coupling has been demonstrated and modeled • A framework for model-based actuator control has been developed addressing • Actuator dynamics • Control-structure interaction • Model-based control has proven successful for RTHS • Robust to changes in specimen conditions • Robust to nonlinearities • Naturally can be used for MIMO systems

  23. The authors would like to acknowledge the support of the National Science Foundation under award CMMI-1011534. Thank you for your attention

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