Title:
Concurrent learning for convergence in adaptive control without persistency of excitation

dc.contributor.advisor Johnson, Eric N.
dc.contributor.author Chowdhary, Girish en_US
dc.contributor.committeeMember Calise, Anthony J.
dc.contributor.committeeMember Egerstedt, Magnus
dc.contributor.committeeMember Haddad, Wassim M.
dc.contributor.committeeMember Tsiotras, Panagiotis
dc.contributor.committeeMember Vela, Patricio A.
dc.contributor.department Aerospace Engineering en_US
dc.date.accessioned 2011-03-04T21:00:31Z
dc.date.available 2011-03-04T21:00:31Z
dc.date.issued 2010-11-11 en_US
dc.description.abstract Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command. This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/37243
dc.publisher Georgia Institute of Technology en_US
dc.subject Gradient descent en_US
dc.subject Flight control systems en_US
dc.subject Flight test en_US
dc.subject Unmanned aerial systems en_US
dc.subject Adaptive control en_US
dc.subject Parameter identification en_US
dc.subject.lcsh Adaptive control systems
dc.subject.lcsh Artificial intelligence
dc.subject.lcsh Robust control
dc.title Concurrent learning for convergence in adaptive control without persistency of excitation en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Johnson, Eric N.
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
relation.isAdvisorOfPublication 175a1f2b-c14e-4c43-a9e5-136fb7f8e5d0
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication f6a932db-1cde-43b5-bcab-bf573da55ed6
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
chowdhary_girish_v_201012_phd.pdf
Size:
6.24 MB
Format:
Adobe Portable Document Format
Description: