Automated estimator parameter selection for an IBM head/disk assembly.
This paper presents the application of a discrete adaptive observer (DAO) to an IBM head/disk assembly system. Because of the difficulties in tuning, a genetic algorithm is implemented off-line to obtain optimal observer parameters for the DAO. Simulations show that the genetic algorithm is successful in choosing appropriate observer gains. Furthermore, as a result of these optimal gains, the observer state and parameter estimates converge accurately and quickly.
Thein
May-Win L
MW
Department of Mechanical Engineering, University of New Hampshire, Durham, New Hampshire 03824-3591, USA. mthein@cisunix.unh.edu
Rendon
Thomas
T
Misawa
Eduardo A
EA