1. Uncertainty issues in Micro/Nano Manipulation by Parallel Manipulator
ICRA 2011 workshop on uncertainty in Automation
Yangmin Li, Professor
University of Macau

2. Summary
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

3. Summary 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.

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!