Parameters Optimization and Experiment of A Planar Parallel 3-DOF Nanopositioning System

Planar parallel three-degrees-of-freedom (3-DOF) nanopositioning systems have been widely applied in scanning probe microscopy, micro-/nanomanipulation, nanoimprint lithography, and precision machining. Two effective optimization approaches are proposed to enhance the system performance in this pape...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2018-03, Vol.65 (3), p.2388-2397
Main Authors: Wang, Ruizhou, Zhang, Xianmin
Format: Article
Language:English
Subjects:
Computer simulation
Decision analysis
Decision making
End effectors
Indexes
Machining
Mechanism performance
Model accuracy
Modelling
Multiple objective analysis
Nanopositioning
nanopositioning platform
nanopositioning system
Nanotechnology
optimization
Parallel degrees of freedom
Pareto optimization
Particle swarm optimization
Payloads
Performance indices
Precision machining
Prototype tests
Resonant frequency
Scanning probe microscopy
Servocontrol
System performance
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Summary:Planar parallel three-degrees-of-freedom (3-DOF) nanopositioning systems have been widely applied in scanning probe microscopy, micro-/nanomanipulation, nanoimprint lithography, and precision machining. Two effective optimization approaches are proposed to enhance the system performance in this paper. First, seven indexes are involved in modeling a nanopositioning platform to evaluate the mechanism performance. Four constraints are also considered. A general analysis, optimization, and decision-making method to select better structure parameters is presented. Prototype test results demonstrate high accuracy of the proposed modeling approach and efficiency of the optimization method. Second, precision, accuracy, and velocity are three main indexes to evaluate a nanopositioning system. Different simulated loads are added to test the system performance. Servocontrol parameters of both three input ports and the end effector are optimized using a multiobjective particle swarm optimization algorithm. Experimental results validate the performance of the optimization module and selected parameters. Two parameters optimization approaches are helpful to enhance the performance of a planar parallel 3-DOF nanopositioning system.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2736502