Estimation of Nonlinear Systems Parameters

In this paper, an identification method is proposed to determine the nonlinear systems parameters. The proposed nonlinear systems can be described by Wiener systems. This structure of models consists of series of linear dynamic element and a nonlinearity block. Both the linear and nonlinear parts are nonparametric. In particular, the linear subsystem of structure entirely unknown. The considered nonlinearity function is of hard type. This latter can have a dead zone or with preload. These nonlinear systems have been confirmed by several practical applications. The suggested approach involves easily generated excitation signals.

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Identification of Wiener Systems With Known Noninvertible Nonlinearities1

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.1409256 ◽

2001 ◽

Vol 123 (4) ◽

pp. 566-571 ◽

Cited By ~ 4

Author(s):

Seth L. Lacy ◽

R. Scott Erwin ◽

Dennis S. Bernstein

Keyword(s):

System Identification ◽

Nonlinear Systems ◽

Dynamic System ◽

Linear Dynamic System ◽

Wiener System ◽

Linear Dynamic ◽

Static Nonlinearity ◽

Wiener Systems ◽

Wiener Type

In this paper we develop a method for identifying SISO Wiener-type nonlinear systems, that is, systems consisting of a linear dynamic system followed by a static nonlinearity. Unlike previous techniques developed for Wiener system identification, our approach allows the identification of systems with nonlinearities that are known but not necessarily invertible, continuous, differentiable, or analytic.

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Identification of Nonparametric Nonlinear Systems

ITM Web of Conferences ◽

10.1051/itmconf/20192402006 ◽

2019 ◽

Vol 24 ◽

pp. 02006

Author(s):

Mohamed Benyassi ◽

Adil Brouri

Keyword(s):

Nonlinear Systems ◽

Infinite Order ◽

Nonlinear Function ◽

Identification Problem ◽

Spectral Approach ◽

Linear Dynamics ◽

Hammerstein Models ◽

Linear Subsystem ◽

Linear And Nonlinear

Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be done using a unique stage.

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Nonlinear systems identification with discontinuous nonlinearity

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v9i1.pp34-41 ◽

2019 ◽

Vol 9 (1) ◽

pp. 34

Author(s):

Mohamed Benyassi ◽

Adil Brouri ◽

Smail Slassi

Keyword(s):

Nonlinear Systems ◽

Hammerstein Model ◽

Linear Dynamic ◽

Input Signals ◽

Systems Identification ◽

System Nonlinearity ◽

Periodic Signals ◽

Two Stages ◽

Nonlinear Systems Identification ◽

Hard Type

<span>In this paper, nonparametric nonlinear systems identification is proposed. The considered system nonlinearity is nonparametric and is of hard type. This latter can be discontinuous and noninvertible. The entire nonlinear system is structured by Hammerstein model. Furthermore, the linear dynamic block is of any order and can be nonparametric. The problem identification method is done within two stages. In the first stage, the system nonlinearity is identified using simple input signals. In the first stage, the linear dynamic block parameters are estimated using periodic signals. The proposed algorithm can be used of large class of nonlinear systems.</span>

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Mathematical modeling of conversion of non-Gaussian random processes, signals and noise in linear and nonlinear systems

Informacionno-technologicheskij vestnik ◽

10.21499/2409-1650-2018-3-66-78 ◽

2018 ◽

pp. 66-78

Author(s):

V. M. Artyushenko ◽

V. I. Volovach

Keyword(s):

Mathematical Modeling ◽

Nonlinear Systems ◽

Random Processes ◽

Mathematical Transformation ◽

Positive And Negative Feedback ◽

Non Linear Systems ◽

Linear And Nonlinear ◽

Non Gaussian ◽

Linear And Nonlinear Systems ◽

Inertial Systems

The questions connected with mathematical modeling of transformation of non-Gaussian random processes, signals and noise in linear and nonlinear systems are considered and analyzed. The mathematical transformation of random processes in linear inertial systems consisting of both series and parallel connected links, as well as positive and negative feedback is analyzed. The mathematical transformation of random processes with polygamous density of probability distribution during their passage through such systems is considered. Nonlinear inertial and non-linear systems are analyzed.

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Adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220985637 ◽

2021 ◽

pp. 014233122098563

Author(s):

Fei Shen ◽

Xinjun Wang ◽

Xinghui Yin

Keyword(s):

Adaptive Control ◽

Nonlinear Systems ◽

Lyapunov Function ◽

Dead Zone ◽

Small Neighborhood ◽

Unmodeled Dynamics ◽

Stochastic Nonlinear Systems ◽

Dynamic Surface ◽

Barrier Lyapunov Function ◽

Full State

This paper investigates the problem of adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics. To stabilize such a system, a dynamic signal is introduced to dominate unmodeled dynamics and an assistant signal is constructed to compensate for the effect of the dead zone. Dynamic surface control is used to solve the “complexity explosion” problem in traditional backstepping design. Two cases of symmetric and asymmetric Barrier Lyapunov functions are discussed respectively in this paper. The proposed Barrier Lyapunov function based on backstepping method can ensure that the output tracking error converges in the small neighborhood of the origin. This control scheme can ensure that semi-globally uniformly ultimately boundedness of all signals in the closed-loop system. Two simulation cases are proposed to verify the effectiveness of the theoretical method.

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