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Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper

EASEC-9, Bali, Indonesia 16-18, December, 2003. Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper. Heon-Jae Lee *: Graduate Student , KAIST, Korea Hyung-Jo Jung: Professor, Sejong University, Korea Nguyen Xuan Thanh: Graduate Student , KAIST, Korea

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Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper

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  1. EASEC-9, Bali, Indonesia 16-18, December, 2003 Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper Heon-Jae Lee*: Graduate Student, KAIST, Korea Hyung-Jo Jung: Professor, Sejong University, Korea Nguyen Xuan Thanh: Graduate Student, KAIST, Korea Sun-Kyu Pakr: Professor, Sungkyunkwan University, Korea In-Won Lee: Professor, KAIST, Korea

  2. CONTENTS • Introduction • Proposed Semiactive Control Algorithm • Numerical Example • Conclusion Structural Dynamics & Vibration Control Lab., KAIST, Korea

  3. Introduction • Backgrounds • Vibration control of seismically excited structure using artificial neural network was proposed by Ghaboussi et al. (1995) and Chen et al. (1995). • Neuro-controllers do not need mathematical models and • can be said to be robust controllers. • There are some problems with training neural network. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  4. Predetermining the Desired Response Need of Emulator Neural Network Problems New Training Algorithm using Cost Function Kim et al. (2000, 2001) Sensitivity Evaluation Algorithm Solutions Structural Dynamics & Vibration Control Lab., KAIST, Korea

  5. Semiactive Control Systems • not only offer the reliability of passive control systems but also maintain the versatility and adaptability of fully active control system. • Clipped optimal algorithm • Representative algorithm for semiactive control system • Proposed by Dyke et al. (1996) • Device: MR damper • Combination of LQG and clipped algorithm Structural Dynamics & Vibration Control Lab., KAIST, Korea

  6. Objective • To propose a new semiactive control method using MR damper for seismically excited structures in conjunction with a neural network algorithm Structural Dynamics & Vibration Control Lab., KAIST, Korea

  7. Proposed Semiactive Control Algorithm • Clipped neuro-algorithm • New efficient algorithm for semiactive control system • Device: MR damper • Combination of neural network and clipped algorithm • Neural network does not require any mathematical model of the structure. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  8. STRUCTURE Clipped Algorithm MR Damper Neural Network Clipped Neuro-Control Block diagram of the proposed algorithm Structural Dynamics & Vibration Control Lab., KAIST, Korea

  9. Control device: MR damper Modified Bouc-Wen model (Spencer et al., 1996) Schematic of MR damper Structural Dynamics & Vibration Control Lab., KAIST, Korea

  10. Governing equations of modified Bouc-Wen model: (1) (2) (3) (4) (5) (6) (7) Structural Dynamics & Vibration Control Lab., KAIST, Korea

  11. Clipped algorithm • desired force (by neural network) : • generated force (by MR damper) : Structural Dynamics & Vibration Control Lab., KAIST, Korea

  12. Control algorithm: neural network • Outline of the neural network Output layer Input layer Hidden layer Structural Dynamics & Vibration Control Lab., KAIST, Korea

  13. Training algorithm (Kim et al., 2000) The neuro-controller is trained by minimizing the cost function, . (8) : state vector : control signal : weighting matrix • If the neuro-controller is trained by minimizing the cost function, there is no need to predetermining the desired response. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  14. Numerical Example • Three-story building structure (Dyke et al., 1996) Structural Dynamics & Vibration Control Lab., KAIST, Korea

  15. Neural network used in the numerical example output input Structural Dynamics & Vibration Control Lab., KAIST, Korea

  16. Procedure of numerical analysis • Training • • Earthquake • a part of NS component of the 1940 El Centro • earthquake ( 0 ~ 3 sec) • (PGA : 0.348 g) • • The cost function is minimized during the training. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  17. Verification 1 • • After the neuro-controller is sufficiently trained, the • model is controlled by the trained neuro-controller under • the three earthquake records. • • The whole El Centro earthquake • • Kobe earthquake • • California earthquake Structural Dynamics & Vibration Control Lab., KAIST, Korea

  18. Verification 2 • • To investigate the relationship between the magnitude of • earthquake and the control performance, simulations are • also conducted with several scaled earthquakes. • • The whole El Centro earthquake (50%, 200% scaled) • • Kobe earthquake (25%, 50% scaled) • • California earthquake (200%, 300% scaled) El Centro earthquake Kobe earthquake California earthquake 0.2 0.4 0.8 1.0 0.6 Peak Ground Acceleration (g) Structural Dynamics & Vibration Control Lab., KAIST, Korea

  19. Control algorithms • Proposed algorithm • Clipped optimal algorithm (Dyke et al., 1996) • Performance comparisons • maximum displacement • maximum drift • maximum acceleration • maximum control force Structural Dynamics & Vibration Control Lab., KAIST, Korea

  20. The ratio of the peak responses for each controller to those of uncontrolled system under El Centro earthquake •The performance of the clipped optimal algorithm is slightly better than that of proposed algorithm in reducing displacements and inter-story drift. • The absolute acceleration of the clipped optimal algorithm is larger than that of the proposed controller. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  21. The ratio of the peak responses for each controller to those of uncontrolled system under 50% scaled El Centro earthquake •It is similar to those of El Centro earthquake. • But the 1st floor acceleration of the clipped optimal algorithm is greater than that of uncontrolled system. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  22. The ratio of the peak responses for each controller to those of uncontrolled system under 200% scaled El Centro earthquake •The performance of the proposed algorithm is better than that of clipped optimal algorithm. • The clipped optimal algorithm is more sensitive than proposed algorithm to the change of the magnitude of earthquake !!! Structural Dynamics & Vibration Control Lab., KAIST, Korea

  23. The ratio of the peak responses for each controller to those of uncontrolled system under Kobe earthquake •The performance of the proposed algorithm is better than that of clipped optimal algorithm. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  24. The ratio of the peak responses for each controller to those of uncontrolled system under California earthquake •The performance of the proposed algorithm is better than that of clipped optimal algorithm. • The clipped optimal algorithm is more sensitive than proposed algorithm to the different frequency components of the earthquake !!! Structural Dynamics & Vibration Control Lab., KAIST, Korea

  25. El Centro earthquake Kobe earthquake California earthquake Clipped optimal Proposed algorithm • Maximum drift of 3rd floor • (Normalized by those of active neuro-control algorithm) •Maximum interstory drift often occurs at 3rd floor. Normalized Maximum drift of 3rd floor Active neuro-control Peak Ground Acceleration (g) •Proposed algorithm shows a better performance than clipped optimal algorithm for all cases. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  26. El Centro earthquake Kobe earthquake California earthquake Clipped optimal Proposed algorithm • Maximum acceleration of 1st floor • (Normalized by those of active neuro-control algorithm) •Maximum acceleration often occurs at 1st floor. Normalized Maximum drift of 3rd floor Active neuro-control Peak Ground Acceleration (g) •Proposed algorithm shows the best performance among the three algorithm. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  27. El Centro earthquake Kobe earthquake California earthquake Clipped optimal Proposed algorithm • Maximum control force • (Normalized by those of active neuro-control algorithm) Active neuro-control Normalized Maximum drift of 3rd floor Peak Ground Acceleration (g) •Proposed algorithm needs less control force than the others. •Proposed algorithm shows a better performance than the other conventional algorithms with less control force!!! Structural Dynamics & Vibration Control Lab., KAIST, Korea

  28. Conclusions • A semiactive neuro-control technique using MR damper for seismically excited structure is proposed. • The clipped optimal algorithm is more sensitive than proposed algorithm to the change of the magnitude and the different frequency components of earthquake. • Proposed algorithm shows a better performance than the other conventional algorithms with less control force. Structural Dynamics & Vibration Control Lab., KAIST, Korea

  29. The proposed semiactive neuro-control technique using MR dampers could be effectively used for control of seismically excited structures! Structural Dynamics & Vibration Control Lab., KAIST, Korea

  30. Thank you for your attention. Structural Dynamics & Vibration Control Lab., KAIST, Korea

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