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Uncertainty issues in Micro/Nano Manipulation by
Parallel ManipulatorICRA 2011 workshop on uncertainty in
Automation
Yangmin Li, Professor
University of Macauhttp://www.sftw.umac.mo/~yangmin/
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SummaryUncertainty 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
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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.
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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
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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)
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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.
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Mechanical architectural parameters optimal design
• To obtain the largest natural frequency subject to performance constraints of workspace, stiffness, etc.
• Based on established analytical models
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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
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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
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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
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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%
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Kinematic and Dynamic model uncertainty
• Inverse kinematic model based open loop 3D trajectory control
• The model is rate dependent
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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
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SMCPE With PID Sliding Surface and Adaptive Gains
• System model
• Perturbation
• Perturbation estimation strategy
• The sliding surface
• The control law
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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
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SMCPE With PID Sliding Surface and Adaptive Gains
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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
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Experimental test• 2D decoupled parallel micro-positioning stage
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Experimental test• Less freedom 3D- pure translational
parallel micro-positioning and active vibration isolation manipulator
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SummarySummary
Uncertainty in Nanomanipulation
1.Mechanism and mechanical structure2. Actuators and sensors3. Control method
