Adaptive Charge Estimation of Piezoelectric Actuators, a Radial Basis Function Approach

Experiments have shown charge of a piezoelectric actuator is proportional to its displacement for an extensive area of operating. As a result, accurate estimation of charge can be an equivalent of displacement/position estimation for piezoelectric actuators, a key advancement towards precise sensorless nanopositioning. As a drawback, existing charge estimation methods take a significant portion of the excitation voltage, i.e. voltage drop. Digital charge estimation have been shown, in the literature, to have the least voltage drop compared to other methods. Digital charge estimators have only one analogue element, a sensing resistor. This paper initially investigates digital charge estimators of piezoelectric actuators to extract an aptness criterion to (i) maximise the accuracy and (ii) minimise the voltage drop. Experiments show that estimators with a constant sensing resistance cannot satisfy the aptness criterion at different operating conditions; while, all existing digital charge estimators use one or, exceptionally, a few intuitive uncalculated sensing resistances. That is, existing estimators witness evitable inaccuracy and/or unnecessarily high voltage drop. This research tackles this defect through development of adaptive charge estimators with varying resistors, which fulfil the aptness criterion in the entire operating area.

[1]  Michael Mason The ABCs of ADCs , 2009 .

[2]  Ben S. Cazzolato,et al.  An innovative digital charge amplifier to reduce hysteresis in piezoelectric actuators , 2010, ICRA 2010.

[3]  Morteza Mohammadzaheri,et al.  Nanopositioning systems with piezoelectric actuators, current state and future perspective , 2017 .

[4]  Tien-Fu Lu,et al.  A novel digital charge-based displacement estimator for sensorless control of a grounded-load piezoelectric tube actuator , 2013 .

[5]  Robert J. Veillette,et al.  A charge controller for linear operation of a piezoelectric stack actuator , 2005, IEEE Transactions on Control Systems Technology.

[6]  Ben S. Cazzolato,et al.  A review, supported by experimental results, of voltage, charge and capacitor insertion method for driving piezoelectric actuators , 2010 .

[8]  Lei Chen,et al.  A critical review of the most popular types of neuro control , 2012 .

[9]  Xiang Li,et al.  Robotic Cell Manipulation Using Optical Tweezers With Unknown Trapping Stiffness and Limited FOV , 2015, IEEE/ASME Transactions on Mechatronics.

[10]  S O Reza Moheimani,et al.  Invited review article: accurate and fast nanopositioning with piezoelectric tube scanners: emerging trends and future challenges. , 2008, The Review of scientific instruments.

[11]  S. Grainger,et al.  A review of charge methods for driving piezoelectric actuators , 2017 .

[12]  Steven Grainger,et al.  A system identification approach to the characterization and control of a piezoelectric tube actuator , 2013 .

[13]  Vladimir Protopopov Beam Alignment and Positioning Techniques , 2014 .

[14]  Nasser Hosseinzadeh,et al.  Development of Gasoline Direct Injector Using Giant Magnetostrictive Materials , 2017, IEEE Transactions on Industry Applications.

[15]  Ali Ghaffari,et al.  Neuro-Fuzzy Modeling of Superheating System of a Steam Power Plant , 2006, Artificial Intelligence and Applications.

[16]  Morteza Mohammad zaheri,et al.  Estimate of the Head Produced by Electrical Submersible Pumps on Gaseous Petroleum Fluids, a Radial Basis Function Network Approach , 2018 .

[17]  Hui Tang,et al.  A flexible parallel nanopositioner for large-stroke micro/nano machining , 2015, 2015 International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO).

[18]  Tien-Fu Lu,et al.  A new hybrid method for sensorless control of piezoelectric actuators , 2013 .

[19]  Alireza Mirbagheri,et al.  A local hybrid actuator for robotic surgery instruments , 2014 .

[20]  Tien-Fu Lu,et al.  A digital charge amplifier for hysteresis elimination in piezoelectric actuators , 2013 .

[21]  Gavin C. Cawley,et al.  On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..

[22]  Qingze Zou,et al.  A review of feedforward control approaches in nanopositioning for high-speed spm , 2009 .