A Model-Free ON-OFF Iterative Adaptive Controller Based on Stochastic Approximation

A model-free on-off iterative adaptive controller is described for application to microscale servo systems performing repeated motions under extremely strict power constraints. The approach is motivated by the needs of piezoelectric actuators in autonomous microrobots, where power consumption in ana...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2012-01, Vol.20 (1), p.196-204
Main Authors: Hahn, B., Oldham, K. R.
Format: Article
Language:English
Subjects:
Actuators
Adaptation model
Adaptative systems
Adaptive control
Adaptive control systems
Applied sciences
Approximation
Approximation methods
Computer science
control theory
systems
Control algorithms
Control systems
Control theory. Systems
Convergence
Cost function
Exact sciences and technology
gradient approximation
Instruments, apparatus, components and techniques common to several branches of physics and astronomy
iterative control
Iterative methods
Mathematical analysis
Mathematical models
Mechanical engineering. Machine design
Mechanical instruments, equipment and techniques
Microactuators
microelectromechanical systems (MEMS)
Micromechanical devices and systems
microrobotics
model free
Noise measurement
on-off control
Physics
piezoelectric actuation
piezoelectric actuators
Precision engineering, watch making
Robotics
Studies
Switches
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Description
Summary:A model-free on-off iterative adaptive controller is described for application to microscale servo systems performing repeated motions under extremely strict power constraints. The approach is motivated by the needs of piezoelectric actuators in autonomous microrobots, where power consumption in analog circuitry and/or for position sensing may be much larger than that of the actuators themselves. The control algorithm adjusts switching instances between "on" and "off" inputs to the actuator to minimize an objective function using simultaneously perturbed stochastic approximation of the gradient with just a single sensor measurement in each iteration. Convergence conditions for the gradient approximation are shown to apply when the possibility for a range of possible switching times minimizing the objective function is accounted for, while a method is proposed for avoiding local minima for plants with bounded nonlinearities. The algorithm is tested on a prototype piezoelectric microactuator.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2011.2104360