(Georgia Institute of Technology, 2007-07)
Yang, Bong-Jun; Calise, Anthony J.; Craig, James I.
This paper considers augmentation of an existing inertial damping mechanism by neural network-based adaptive
control, for controlling a micromanipulator that is serially attached to a macromanipulator. The approach is
demonstrated using an experimental test bed in which the micromanipulator is mounted at the tip of a cantilevered
beam that resembles a macromanipulator with its joint locked. The inertial damping control combines acceleration
feedback with position control for the micromanipulator so as to simultaneously suppress vibrations caused by the
flexible beam while achieving precise tip positioning. Neural network-based adaptive elements are employed to
augment the inertial damping controller when the existing control system becomes deficient due to modeling errors
and uncertain operating conditions. There were several design challenges that had to be faced from an adaptive
control perspective. One challenge was the presence of a nonminimum phase zero in an output feedback adaptive
control design setting in which the regulated output variable has zero relative degree. Other challenges included
flexibility in the actuation devices, lack of control degrees of freedom, and high dimensionality of the system
dynamics. In this paper we describe how we overcame these difficulties by modifying a previous augmenting adaptive
approach to make it suitable for this application. Experimental results are provided to illustrate the effectiveness of
the augmenting approach to adaptive output feedback control design.
(Georgia Institute of Technology, 2004)
Calise, Anthony J.; Yang, Bong-Jun; Craig, James I.
This paper describes an approach for augmenting a linear controller design with a neural-network-based adaptive
element. The basic approach involves formulating an architecture for which the associated error equations
have a form suitable for applying existing results for adaptive output feedback control of nonlinear systems. The
approach is applicable to non-affine, nonlinear systems with both parametric uncertainties and unmodelled dynamics.
The effect of actuator limits are treated using control hedging. The approach is particularly well suited for
control of flexible systems subject to limits in control authority. Its effectiveness is tested on a laboratory experiment
consisting of a three-disk torsional pendulum system, including control voltage saturation and stiction.