An LMI approach to control oriented identification of LPV systems

Sznaier, Mario; Mazzaro, C.; Inanc, Tamer

doi:10.1109/acc.2000.879257

Cited by 24 publications

(24 citation statements)

References 14 publications

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“…For this property, infinitely many basis functions, (n e + 1)n b = ∞ are required in the general case, which implies that using a finite number of basis functions will restrict the class of realizable LPV systems. However it can be showed that in 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 ThB11.6…”

Section: ) Wiener Lpv Obf Model (W-lpv Obf)mentioning

confidence: 99%

“…However, selection of the basis set has a major impact on the outcome of the identification process as the distance 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 ThB11.6…”

Section: Orthonormal Basis Functionsmentioning

confidence: 99%

“…This comprises methods based on subspace techniques [8], [9], [10], basis functions [11], Linear Matrix Inequalities (LMI's) based optimization [12], simple Least Means Square (LMS) approaches [13], [14], stochastic framework based methods [15], and on parameter estimation based gradient searches [16], [17]. However, in practical use of these methods commonly unaddressed problems occur, highly compromising the development of efficient controllers.…”

Section: Introductionmentioning

confidence: 99%

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LPV system identification with globally fixed orthonormal basis functions

Tóth

Heuberger

Hof

2007

2007 46th IEEE Conference on Decision and Control

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Section: ) Wiener Lpv Obf Model (W-lpv Obf)mentioning

confidence: 99%

“…However, selection of the basis set has a major impact on the outcome of the identification process as the distance 46th IEEE CDC, New Orleans, USA, Dec. [12][13][14]2007 ThB11.6…”

Section: Orthonormal Basis Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LPV system identification with globally fixed orthonormal basis functions

Tóth

Heuberger

Hof

2007

2007 46th IEEE Conference on Decision and Control

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“…In addition we have extended these techniques to (he case where the plant is not necessarily open loop £2 stable. This is of particular importance for applications such as visual tracking and activity recognition (see [11,12,39] for details). Finally, we have obtained necessary and sufficient convex conditions for model invalidation in die presence of arbitrarily slowly time-varying structured imcertainty.…”

Section: U=-b'p{xt)xmentioning

confidence: 99%

“…• Development of Control Oriented Identification and Model (In)validation mefliods for parameter-varying, not necessarily £2 stable systems [6,11,12,16]. …”

Section: Theoreticalmentioning

confidence: 99%

Multiobject Robust Control of Nonlinear Systems via State Dependent Coefficient Representations and Applications

Sznaier¹

2000

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The public nponing burden for iMs collection of information is estimated to average 1 hour per response, including i gnlMririg and maintaining the data needed, and completing and reviewing the collection of information. Send comment of infonrvation, including suggestions for reducing the burden, to Department of Defense, Washington Headquar 10704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware subject to any penalty for failing to comply with a collection of Information if it does not display a currently valid 0MB cc PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. * • 'AFRL-SR-AR-TR-03- 0^0■or rts nBPOfKT DAVL (DD-MM-YYYY) REPORT TYPE Final Report DATES COVERED (From -To)Dec 1.99-May31, 03 TITLE AND SUBTITLE Multi-objective Robust Control of Nonlinear Systems via State Dependent Coefficient Representations and Applications 5a. CONTRACT NUMBER5b. GRANT NUMBER F49620-00-1-0020 5c. PROGRAM ELEMENT NUMBER AUTHOR(S)Dr. Mario Sznaier 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER reRFORMING ORGANIZATION NAME(S) AND ADORESS(ES) Department of Electrical EngineeringThe Pennsylvania State University University Park, PA 16802 PERFORMING ORGANIZATION REPORT NUMBER SPONSORING/MONITORING AGENCY NAME{S) AND ADDRESS(ES) Etepartment of the Air Force 4015 Wilson Blvd. Air Force Office of Scientific ResearchArlington, VA 22203-1954 SPONSOR/MONITOR'S ACRONYM(S) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release, distribution unlimited. The past few years have seen a significant change in the roles and requirements of the U.S. Air Force. On the one hand, the well defiiKd global threat posed by a few conventional and nuclear forces has been replaced by multiple, localized potential threats that in addition to the usual conventional and nuclear ones, also potentially involve chemical and biological weapons. In addition, die USAF role has expaiuled to include operations other than warfare, such as peacekeeping and humanitarian aid. Finally, limited resources mandate that these roles must he achieved in a cost-effective way. This scenario translates into a requirement for highly versatile systems, capable of delivering near optimal performance under a wide range of conditions. In tills research we have developed a framework, based on the combination of Receding Horizon, Contro Ly^unov Functions and Operator Interpolation Theory techniques, for systematically designing controllers capable of meeting thesi challenges. These controllers offer the following advantages over hitherto available design methods: oThe ability to deliver near optimal performance while keeping the computational complexity compatible with an on-line implementation SUPPLEMENTARY NOTESDODAAD DISTRIBUTION STATEMENT A Approved for Public Release Distribution UnlimitedAbstract The past few years have seen a significant change in the roles and requirements of the U.S. Air Force. On one hand, the well defined global threat posed by a few conventional and nuc...

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Robust identification/invalidation in an LPV framework

Bianchi¹,

Peña²

2009

Intl J Robust & Nonlinear

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SUMMARYA robust linear parameter varying (LPV) identification/invalidation method is presented. Starting from a given initial model, the proposed method modifies it and produces an LPV model consistent with the assumed uncertainty/noise bounds and the experimental information. This procedure may complement existing nominal LPV identification algorithms, by adding the uncertainty and noise bounds which produces a set of models consistent with the experimental evidence. Unlike standard invalidation results, the proposed method allows the computation of the necessary changes to the initial model in order to place it within the consistency set. Similar to previous LPV identification procedures, the initial parameter dependency is fixed in advance, but here a methodology to modify this dependency is presented. In addition, all calculations are made on state-space matrices which simplifies further controller design computations. The application of the proposed method to the identification of nonlinear systems is also discussed.

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An LMI approach to control oriented identification of LPV systems

Cited by 24 publications

References 14 publications

LPV system identification with globally fixed orthonormal basis functions

LPV system identification with globally fixed orthonormal basis functions

Multiobject Robust Control of Nonlinear Systems via State Dependent Coefficient Representations and Applications

Robust identification/invalidation in an LPV framework

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