Non-iterative identification and model following control of Hammerstein systems with asymmetric dead-zone non-linearities

Lv, Xiaohua; Ren, Xuemei

doi:10.1049/iet-cta.2010.0454

Cited by 24 publications

(22 citation statements)

References 11 publications

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“…The Hammerstein model represents a class of input nonlinear systems, where the nonlinear block is prior to the linear one. It can flexibly approximate various input nonlinearities, such as saturation, dead zone, backlash and hysteresis, thus having been extensively employed in realistic applications [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Xie

Yang

2017

Algorithms

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Abstract:Due to the lack of powerful model description methods, the identification of Hammerstein systems based on the non-uniform input-output dataset remains a challenging problem. This paper introduces a time-varying backward shift operator to describe periodically non-uniformly sampled-data Hammerstein systems, which can simplify the structure of the lifted models using the traditional lifting technique. Furthermore, an auxiliary model-based multi-innovation stochastic gradient algorithm is presented to estimate the parameters involved in the linear and nonlinear blocks. The simulation results confirm that the proposed algorithm is effective and can achieve a high estimation performance.

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Section: Introductionmentioning

confidence: 99%

Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Xie

Yang

2017

Algorithms

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“…Email: maozhizhong@ise.neu.edu.cn In contrast to the contributions for block-oriented nonlinear systems identification, much less efforts have been done to design controllers for block-oriented models, which is another key challenge omitted by many researchers. In the previous work, Dolanc and Strmcnik (2008) and Lv and Ren (2012) have proposed nonlinear controllers based on the classic linear pole placement principle. The model parameters are obtained by the 'off-line' identification, conducted prior to the control experiment.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of Hammerstein–Wiener nonlinear systems

Zhang

Hong

Mao

2014

International Journal of Systems Science

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The Hammerstein-Wiener model is a block-oriented model, having a linear dynamic block sandwiched by two static nonlinear blocks. This note develops an adaptive controller for a special form of Hammerstein-Wiener nonlinear systems which are parameterized by the key-term separation principle. The adaptive control law and recursive parameter estimation are updated by the use of internal variable estimations. By modeling the errors due to the estimation of internal variables, we establish convergence and stability properties. Theoretical results show that parameter estimation convergence and closed-loop system stability can be guaranteed under sufficient condition. From a qualitative analysis of the sufficient condition, we introduce an adaptive weighted factor to improve the performance of the adaptive controller. Numerical examples are given to confirm the results in this paper.

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“…As a result, uncertain input nonlinearity inevitably exists in dynamic control systems [9]. More recently, attention has been focused on various uncertain input nonlinearities common in uncertain systems, such as deadzones, relays, saturation, hysteresis, and others; see, for instance [6,[13][14][15][16][17] and the references cited therein.…”

Section: Introductionmentioning

confidence: 99%

Robust Stabilization for a Class of Uncertain Nonlinear Systems via a Novel Hybrid Control Applicable to Mechanical Systems

Sun

2014

Advances in Mechanical Engineering

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An important consideration in control system design is that of model uncertainty. Besides, systems with mixed uncertainties, chaotic vibrations, and input nonlinearities are not easily stabilized and traditional control schemes for linear systems are not always effective. Therefore, in this paper, we will solve two problems, first searching a novel hybrid control methodology to achieve the practical stabilization for uncertain systems with mixed uncertainties and second calculating the guaranteed exponential convergence rate with the convergence radius. The applicability of the main results is demonstrated by a tracking controller design for a class of uncertain nonlinear mass-damper-spring systems with mixed uncertainties, chaotic vibrations, and input nonlinearities.

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Non-iterative identification and model following control of Hammerstein systems with asymmetric dead-zone non-linearities

Cited by 24 publications

References 11 publications

Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Adaptive control of Hammerstein–Wiener nonlinear systems

Robust Stabilization for a Class of Uncertain Nonlinear Systems via a Novel Hybrid Control Applicable to Mechanical Systems

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