Discrete time adaptive control of linear systems with preload nonlinearity

Kung, Min-Chio; Womack, Baxter F.

doi:10.1016/0005-1098(84)90106-7

Cited by 32 publications

(19 citation statements)

References 5 publications

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“…The Hammerstein model has been introduced to system identification by Narendra and Gallman (1966) and further studied by Chang and Luus (1971), Chung and Sun (1988), Gallman (1975Gallman ( , 1976, Hsia (1977) and Billings and Fakhouri (1979). It has been applied to adaptive control by Kung and Womack (1984), to adaptive noise cancellation by Stapleton and Baas (1985), to the design of nonlinear predictors by McCannon et al (1982), and to the identification of biological systems by Hunter and Korenberg (1986). In all the papers mentioned above, the authors consider single-input single-output (SISO) systems in which the nonlinearity is of the polynomial type of fixed degree.…”

Section: Introductionmentioning

confidence: 99%

On identification of block orientated systems by non-parametric techniques

Krzyżak

Partyka²

1993

International Journal of Systems Science

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The identification of block orientated systems is discussed. Particular attention is devoted to memoryless and dynamic systems with cascade structure. An optimal model of a memoryless cascade system is derived and estimated by kernel regression. Nonlinear dynamic systems of the Hammerstein and Wiener type are identified by means of non-parametric techniques. Convergence of the identification procedures is investigated.

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Section: Introductionmentioning

confidence: 99%

On identification of block orientated systems by non-parametric techniques

Krzyżak

Partyka²

1993

International Journal of Systems Science

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“…Motivated by [12][13], the strategy we will employ is to choose 1 ( ) u t that makes the estimated output ˆ( 1) …”

Section: Problem Descriptionmentioning

confidence: 99%

“…In [10][11], the authors derive identification algorithms for deterministic H models with discontinuous nonlinearities and design dead-zone compensated control law for the estimated systems. In [12][13], adaptive controllers are proposed for systems with two-segment piecewise-linear nonlinearity and preload nonlinearity. Most of the contributions assume the disturbance is white Gaussian noise [3][4][5][6][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…In [12][13], adaptive controllers are proposed for systems with two-segment piecewise-linear nonlinearity and preload nonlinearity. Most of the contributions assume the disturbance is white Gaussian noise [3][4][5][6][12][13]. This note focuses on robust adaptive controller design for H systems with dead-zone nonlinearity and bounded noise.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive control for Hammerstein systems with dead-zone nonlinearity and bounded noise

Zhang

Mao

2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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This note focuses on robust adaptive controller design for Hammerstein systems with symmetric dead-zone nonlinearity and bounded noise. A modified control law is proposed to compensate dead-zone nonlinearity and a new recursive estimator with a nonnegative data weighting is presented to deal with bounded noise. From convergence properties and global stability analysis, we show that parameter estimation is convergent and the closed-loop system is globally stable. What is more, the theoretical results also show that tracking error is convergent with a specific residue which is related to parameter estimation and the upper bound of noise.

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“…Hammerstein models with piecewise nonlinearities are frequently used in nonlinear systems control [10]. We find such nonlinearities in some actuator families such as dead zone nonlinearity, saturation nonlinearity, preload nonlinearity [11] etc…. In the literature, many methods of modelling and identification of Hammerstein systems have been proposed [12,13].…”

Section: Introductionmentioning

confidence: 99%

Identification of Hammerstein Systems using Triangular basis Functions

Elleuch¹,

Châari²

2011

IJCA

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A new identification method is proposed for Hammerstein systems in presence of dead zone input nonlinearities. To describe and identify the nonlinear system, a new decomposition technique using the triangular basis functions is employed. Then a parameterized model is derived to represent the entire system. The approximation by Triangular basis functions for the description of the static nonlinear block conducts to a linear regressive model, so parameter matrices characterizing the considered model can be estimated. After this stage, Singular Values Decomposition (SVD) technique has been applied to separate the coupled parameters of matrixes. The numerical simulation results illustrate that the proposed approach can be a promising tool for identifying Hammerstein systems with dead zone nonlinearities. General TermsModeling of nonlinear systems, Hammerstein systems, Triangular basis functions.

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Discrete time adaptive control of linear systems with preload nonlinearity

Cited by 32 publications

References 5 publications

On identification of block orientated systems by non-parametric techniques

On identification of block orientated systems by non-parametric techniques

Adaptive control for Hammerstein systems with dead-zone nonlinearity and bounded noise

Identification of Hammerstein Systems using Triangular basis Functions

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