title: Automated estimator parameter selection for an IBM head/disk assembly.
abstract: 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.
authors.affiliation: Department of Mechanical Engineering, University of New Hampshire, Durham, New Hampshire 03824-3591, USA. mthein@cisunix.unh.edu
authors.forename: May-Win L
authors.initials: MW
authors.lastname: Thein
authors.forename: Thomas
authors.initials: T
authors.lastname: Rendon
authors.forename: Eduardo A
authors.initials: EA
authors.lastname: Misawa