State and fault estimation of sandwich systems with hysteresis

Zhou, Zhaowei; Liu, Xufeng

doi:10.1002/rnc.4116

Cited by 41 publications

(28 citation statements)

References 22 publications

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“…If rl  ，then the limited invariant zeros are the zeros of the Eigenpolynomial as shown in eq. (20). det( ( )) certain zeros of the transfer function of * () dk to () rk .…”

Section:  mentioning

confidence: 99%

“…Note that the work on a state-estimation of the sandwich system with dead zone, backlash, and hysteresis, has been conducted respectively [18][19][20]. Recently, an observer to realize the more accurate fault detection of a mechanical system with inherent backlash without considering the model uncertainties and disturbances is designed in [21].…”

Section: Introductionmentioning

confidence: 99%

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Fault detection of a sandwich system with dead-zone based on robust observer

Zhou

Tan

Shi

2016

Systems & Control Letters

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Non-smooth sandwich systems with dead-zone widely exist in the real engineering applications. For accurately detecting its faults, a novel robust observer has been proposed in this paper. With the consideration of the model uncertainties, disturbances, and switching error which specially belongs to the system, the so-called general disturbance is defined. After that, the conventional dynamic robust observer design method can be applied to the system. Then, for saving the computing time and effectively reducing the effect of the disturbances to the residual, the main frequencies of the disturbances have been identified by the spectrum analysis of the residual created by the conventional observer and the zeros assignment methodology has been applied to get one feedback matrix of the robust observer. Finally, the rest of the feedback matrices of the robust observer can be obtained by solving an optimal problem with ,, / FF HH  as the minimizing object. For verifying the effectiveness of this novel robust observer, the comparison between the proposed robust non-smooth scheme and the conventional method has been made. The final results show that the proposed robust fault detection approach can detect the actuator and sensor faults of the system more accurately and timely with a lower missing and false alarm rates comparing with the conventional one.

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“…If rl  ，then the limited invariant zeros are the zeros of the Eigenpolynomial as shown in eq. (20). det( ( )) certain zeros of the transfer function of * () dk to () rk .…”

Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault detection of a sandwich system with dead-zone based on robust observer

Zhou

Tan

Shi

2016

Systems & Control Letters

Self Cite

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show abstract

“…System identification methods can obtain the mathematical models of practical systems from the experimental data [1][2][3] and play an important role in fault detection 4,5 and system control. 6,7 Multivariate systems have acquired widespread applications for their wide existence in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

The innovation algorithms for multivariable state‐space models

Ding

Xiao

2019

Adaptive Control & Signal

115

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This paper derives the input-output representation of the dynamical system described by a linear multivariable state-space model and the corresponding multivariate linear regressive model (ie, multivariate equation-error model). A projection identification algorithm, a multivariate stochastic gradient identification algorithm, and a multi-innovation stochastic gradient (MISG) identification algorithm are proposed for multivariate equation-error systems by using the negative gradient search and the multi-innovation identification theory. The convergence analysis of the MISG algorithm indicates that the parameter estimation errors converge to zero under the persistent excitation condition. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.

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“…Mathematical models can describe the dynamic behavior of a system as a function of time and exist in various fields, such as fault diagnosis [1,2], telecommunication transmission [3][4][5], control system [6,7] and signal processing [8,9]. In most cases, it is not easy to analyse the whole system and to construct the mathematical model based on the analytic approach.…”

Section: Introductionmentioning

confidence: 99%

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Liu

Ding

et al. 2019

IET Control Theory & Applications

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System identification provides many convenient and useful methods for engineering modeling. This paper targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the colored noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then we decompose the filtered system into several subsystems and develop a filtering based partially-coupled generalized extended stochastic gradient algorithm via the coupling concept. In contrast to the multivariable generalized extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.

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State and fault estimation of sandwich systems with hysteresis

Cited by 41 publications

References 22 publications

Fault detection of a sandwich system with dead-zone based on robust observer

Fault detection of a sandwich system with dead-zone based on robust observer

The innovation algorithms for multivariable state‐space models

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

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