Learning control algorithms for tracking "slowly" varying trajectories

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dc.contributor.author Saab, Samer S.
dc.contributor.author Vogt, William G.
dc.contributor.author Mickle, Marlin H.
dc.date.accessioned 2019-07-26T11:24:07Z
dc.date.available 2019-07-26T11:24:07Z
dc.date.copyright 1997 en_US
dc.date.issued 2019-07-26
dc.identifier.issn 1083-4419 en_US
dc.identifier.uri http://hdl.handle.net/10725/11147
dc.description.abstract To date, most of the available results in learning control have been utilized in applications where a robot is required to execute the same motion over and over again, with a certain periodicity. This is due to the requirement that all learning algorithms assume that a desired output is given a priori over the time duration t /spl isin/ ~0,T\. For applications where the desired outputs are assumed to change "slowly", we present a D-type, PD-type, and PID-type learning algorithms. At each iteration we assume that the system outputs and desired trajectories are contaminated with measurement noise, the system state contains disturbances, and errors are present during reinitialization. These algorithms are shown to be robust and convergent under certain conditions. In theory, the uniform convergence of learning algorithms is achieved as the number of iterations tends to infinity. However, in practice we desire to stop the process after a minimum number of iterations such that the trajectory errors are less than a desired tolerance bound. We present a methodology which is devoted to alleviate the difficulty of determining a priori the controller parameters such that the speed of convergence is improved. In particular, for systems with the property that the product matrix of the input and output coupling matrices, CB, is not full rank. Numerical examples are given to illustrate the results. en_US
dc.language.iso en en_US
dc.title Learning control algorithms for tracking "slowly" varying trajectories en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SOE en_US
dc.author.idnumber 199690250 en_US
dc.author.department Electrical And Computer Engineering en_US
dc.description.embargo N/A en_US
dc.relation.journal IEEE transactions on systems, man, and cybernetics, Part B: Cybernetics en_US
dc.journal.volume 27 en_US
dc.journal.issue 4 en_US
dc.article.pages 657-670 en_US
dc.identifier.doi http://dx.doi.org/10.1109/3477.604109 en_US
dc.identifier.ctation Saab, S. S., Vogt, W. G., & Mickle, M. H. (1997). Learning control algorithms for tracking" slowly" varying trajectories. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 27(4), 657-670. en_US
dc.author.email ssaab@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://ieeexplore.ieee.org/abstract/document/604109 en_US
dc.orcid.id https://orcid.org/0000-0003-0124-8457 en_US
dc.author.affiliation Lebanese American University en_US

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