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Uncertainty issues in Micro/Nano Manipulation by Parallel Manipulator ICRA 2011 workshop on uncertainty in Automation

Uncertainty issues in Micro/Nano Manipulation by Parallel Manipulator ICRA 2011 workshop on uncertainty in Automation. Yangmin Li , Professor University of Macau http://www.sftw.umac.mo/~yangmin/. Uncertainty problems In the field of Micro/Nano parallel manipulator

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Uncertainty issues in Micro/Nano Manipulation by Parallel Manipulator ICRA 2011 workshop on uncertainty in Automation

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  1. Uncertainty issues in Micro/Nano Manipulation by Parallel Manipulator ICRA 2011 workshop on uncertainty in Automation Yangmin Li, Professor University of Macau http://www.sftw.umac.mo/~yangmin/

  2. Uncertainty problems In the field of Micro/Nano parallel manipulator Mechanical structure and mechanical architecture parameters, installation errors, manufacturing tolerances and clearances uncertainty performance of driving actuators uncertainty dynamic model errors for control strategy the uncertainty outside disturbances or noises from the sensors, and the task uncertainty Summary

  3. Measures taken Mechanical structure and mechanical architectural parameters should be optimized Hysteresis model such as the Preisach model, Duhem model, Maxwell model, and Bouc–Wen model, etc can be adopted. Sliding mode control (SMC) strategy can be used to deal with the system model uncertainty Sliding mode control with perturbation estimation (SMCPE) can be adopted to deal with the uncertain external disturbances. Summary

  4. Structure selection: flexure-based micro-positioning stage • Flexure-based: be capable of positioning with ultrahigh precision • based on the elastic deformations of the structures • no backlash property and no non-linear friction • simple structure , easy manufacture and installation. • Decoupled parallel structures • Redundant parallel structure • Less freedom parallel structure

  5. Structure selection: flexure-based micro-positioning stage • Be capable of positioning with ultrahigh precision • based on the elastic deformations of the structures • No backlash property and no non-linear friction • simple structure and easy manufacture and installation. • Be driven by unconventional motors • piezoelectric actuator (PZT) • voice coil motor • magnetic levitation motor • Be applied in various applications • MEMS sensors and actuators • optical fiber alignment • biological cell manipulation • scanning probe microscopy (SPM)

  6. Mechanical architectural parameters optimal design • The conventional error transformation matrix (ETM) can be derived based on the differentiation of kinematic equations • Error amplification index (EAI) over a usable workspace as an error performance index can be optimization via PSO or GA.

  7. Mechanical architectural parameters optimal design • To obtain the largest natural frequency subject to performance constraints of workspace, stiffness, etc. • Based on established analytical models

  8. Uncertainty performance of driving actuators • Be driven by unconventional motors • - piezoelectric actuator (PZT) • - voice coil motor • magnetic levitation motor • Hysteresis model and optimal identification process can be adopted to compensate the errors • - Preisach model • - Duhem model • - Maxwell model • - Bouc–Wen model

  9. FF+FB control strategy to compensate the hysteresis error • Inverse Dahl model is used as Feed Forward control channel combined with PID to compensate the hysteresis error

  10. Kinematic and Dynamic modeling • Structure is simplified • Each flexure hinge has 2-DOF compliances • Analytical models are established for • Amplification ratio • Stiffness • Workspace • Stress • Dynamics

  11. Kinematic and Dynamic modeling • Amplification ratio = 6.58 • Input stiffness = 13.2 N/um << 208 N/um • Maximum stress = 64.8 MPa << 503 MPa • Natural frequency = 78.7 Hz • Output coupling = 0.18% • Input coupling = 0.31%

  12. Kinematic and Dynamic model uncertainty • Inverse kinematic model based open loop 3D trajectory control • The model is rate dependent

  13. Kinematic and Dynamic model uncertainty • Kinematic and Dynamic Model is build through simplification and have errors respect to the real system • Sliding mode control (SMC) strategy can be used to deal with the system model uncertainty

  14. SMCPE With PID Sliding Surface and Adaptive Gains • System model • Perturbation • Perturbation estimation strategy • The sliding surface • The control law

  15. SMCPE With PID Sliding Surface and Adaptive Gains • Adaptive Sliding Mode Control With Perturbation Estimation and PID Sliding Surface for Motion Tracking of a Piezo-Driven Micromanipulator

  16. SMCPE With PID Sliding Surface and Adaptive Gains

  17. Experimental tests - 3D decoupled parallel micro-positioning stage • Motions • Input = 20um • Output: X=164.8um, Y=6.7um, Z=7.2um • Coupling: dY=4.1%, dZ=4.4% • Nonlinearity • Hysteresis between input and output

  18. Experimental test • 2D decoupled parallel micro-positioning stage

  19. Experimental test • Less freedom 3D- pure translational parallel micro-positioning and active vibration isolation manipulator

  20. Summary Summary • Uncertainty in Nanomanipulation • Mechanism and mechanical structure • Actuators and sensors • Control method 20

  21. Thank you for your attention! • www.sftw.umac.mo/~yangmin • Email: ymli@umac.mo

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