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Discrete time learning control in nonlinear systemsIn this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.
Document ID
19920069480
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Longman, Richard W.
(NASA Langley Research Center Hampton, VA, United States)
Chang, Chi-Kuang
(Columbia University New York, United States)
Phan, Minh
(Lockheed Engineering and Sciences Co. Hampton, VA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1992
Subject Category
Cybernetics
Report/Patent Number
AIAA PAPER 92-4592
Meeting Information
Meeting: 1992 AIAA/AAS Astrodynamics Conference
Location: Hilton Head Island, SC
Country: United States
Start Date: August 10, 1992
End Date: August 12, 1992
Accession Number
92A52104
Funding Number(s)
CONTRACT_GRANT: NAG1-649
Distribution Limits
Public
Copyright
Other

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