Parameter estimation and control of nonlinearly parameterized systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.

Bibliographic Details
Main Author: Cao, Chengyu, 1973-
Other Authors: A.M. Annaswamy.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2006
Subjects:
Online Access:http://hdl.handle.net/1721.1/30326
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author Cao, Chengyu, 1973-
author2 A.M. Annaswamy.
author_facet A.M. Annaswamy.
Cao, Chengyu, 1973-
author_sort Cao, Chengyu, 1973-
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description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.
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spelling mit-1721.1/303262019-04-12T23:37:50Z Parameter estimation and control of nonlinearly parameterized systems Cao, Chengyu, 1973- A.M. Annaswamy. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004. Includes bibliographical references. Parameter estimation in nonlinear systems is an important issue in measurement, diagnosis and modeling. The goal is to find a differentiator free on-line adaptive estimation algorithm which can estimate the internal unknown parameters of dynamic systems using its inputs and outputs. This thesis provides new algorithms for adaptive estimation and control of nonlinearly parameterized (NLP) systems. First, a Hierarchical Min-max algorithm is invented to estimate unknown parameters in NLP systems. To relax the strong condition needed for the convergence in Hierarchical Min-max algorithm, a new Polynomial Adaptive Estimator (PAE) is invented and the Nonlinearly Persistent Excitation Condition for NLP systems, which is no more restrictive than LPE for linear systems, is established for the first time. To reduce computation complexity of PAE, a Hierarchical PAE is proposed. Its performance in the presence of noise is evaluated and is shown to lead to bounded errors. A dead-zone based adaptive filter is also proposed and is shown to accurately estimate the unknown parameters under some conditions. Based on the adaptive estimation algorithms above, a Continuous Polynomial Adaptive Controller (CPAC) is developed and is shown to control systems with nonlinearities that have piece-wise linear parameterizations. Since large classes of nonlinear systems can be approximated by piece-wise linear functions through local linearization, it opens the door for adaptive control of general NLP systems. The robustness of CPAC under bounded output noise and disturbances is also established. by Chengyu Cao. Ph.D. 2006-03-24T18:40:23Z 2006-03-24T18:40:23Z 2004 2004 Thesis http://hdl.handle.net/1721.1/30326 61104698 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 233 p. 8424054 bytes 8454237 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology
spellingShingle Mechanical Engineering.
Cao, Chengyu, 1973-
Parameter estimation and control of nonlinearly parameterized systems
title Parameter estimation and control of nonlinearly parameterized systems
title_full Parameter estimation and control of nonlinearly parameterized systems
title_fullStr Parameter estimation and control of nonlinearly parameterized systems
title_full_unstemmed Parameter estimation and control of nonlinearly parameterized systems
title_short Parameter estimation and control of nonlinearly parameterized systems
title_sort parameter estimation and control of nonlinearly parameterized systems
topic Mechanical Engineering.
url http://hdl.handle.net/1721.1/30326
work_keys_str_mv AT caochengyu1973 parameterestimationandcontrolofnonlinearlyparameterizedsystems