Adaptive nonlinear attitude control of spacecraft

dc.contributor.advisor Ping Lu
dc.contributor.author Tang, Cheng
dc.contributor.department Aerospace Engineering
dc.date 2018-08-23T10:34:01.000
dc.date.accessioned 2020-06-30T07:10:06Z
dc.date.available 2020-06-30T07:10:06Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 1995
dc.date.issued 1995
dc.description.abstract <p>Three adaptive nonlinear control approaches are proposed for attitude control of a Space Station. These control algorithms avoid the need for linearization, either by truncating a Taylor series or by a feedback linearization approach, which poses some difficulties in the presence of both uncertain mass properties and external disturbances. Global stability of the Space Station is guaranteed;A conventional linear-quadratic regulator synthesis technique is investigated. This linear control scheme provides a natural way to select control gains for the known spacecraft system parameters. Simulation results reveal that the uncertain mass property causes the nonlinear Space Station to be uncontrollable;On the basis of two nonlinear adaptive control approaches originally developed for robot manipulators, Two controllers are developed to overcome the problems introduced by uncertain inertias. The improvement on transient state responses is obtained by optimizing control parameters. A compensation term which adaptively adds periodic signals to the input is appended to both controllers to cancel the effect of the cyclic disturbances. The convergence properties of the adaptive algorithms are proved. The application to the Space Station attitude control of both nonlinear control schemes shows good performance in the presence of uncertain inertias and disturbances. Robustness with the unmodeled dynamics for both algorithms are also tested;Another new approach that utilizes the neural network with a radial basis function technique to compensate for the effect of the disturbances is also presented. The neural networks provide an estimate of the cyclic disturbances and act as an open-loop control so that the overall attitude oscillation caused by the disturbances is minimized. This neural network control law is applied to the Space Station in conjunction with the nonlinear controllers which are used to shape the system responses. The simulation results show that, compared to the case where only nonlinear controllers are used, the required control torques are significantly smaller. It has also been shown that the trained networks are capable of extrapolating;Finally, when the attitude control is achieved by control momentum gyros (CMGs), an approximate feedback linearization method is applied to simultaneous attitude control and momentum management with unknown constant disturbances. A direct adaptive controller is developed to stabilize the system with large disturbances. Simulations are carried out to show that the Space Station can be stabilized within a reasonable range, whereas ignoring the disturbances in the controller can lead to destruction of the Space Station and saturation of the CMGs.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/11093/
dc.identifier.articleid 12092
dc.identifier.contextkey 6435840
dc.identifier.doi https://doi.org/10.31274/rtd-180813-10208
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/11093
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/64312
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/11093/r_9610994.pdf|||Fri Jan 14 18:41:56 UTC 2022
dc.subject.disciplines Aerospace Engineering
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.disciplines Systems Engineering
dc.subject.keywords Aerospace engineering and engineering mechanics
dc.subject.keywords Aerospace engineering
dc.title Adaptive nonlinear attitude control of spacecraft
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 047b23ca-7bd7-4194-b084-c4181d33d95d
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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