Regularised estimation for ARMAX process with measurements subject to outliers

Yin, Li; Liu, Shuo; Gao, Hui

doi:10.1049/iet-cta.2017.1204

Cited by 14 publications

(6 citation statements)

References 61 publications

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“…In addition, an MHE strategy for minimizing the risk suffered by selecting a subset of the most recent measurements for the case of discrete-time linear systems suffering from measurement contamination was proposed by Aghapour and Farrell (2020) and was later extended to nonlinear systems (Aghapour et al, 2021). In Yin and Gao (2019) and Yin et al (2018), by modeling the underlying plant as the Auto-Regressive-Moving-Average with eXogenous input (ARMAX) process, a moving horizon estimator that not only obtains robust estimates but also has the ability to detect outliers was proposed. The active detection-based schemes described above can diagnose anomalies and eliminate their effects through a specialized filter structure design.…”

Section: Introductionmentioning

confidence: 99%

Design and stability of moving horizon estimator for discrete-time linear systems subject to multiple measurement outliers

Liu,

Wang,

Yuan

et al. 2023

Transactions of the Institute of Measurement and Control

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This paper considers the state estimation problem for discrete-time linear systems suffering from dense measurement anomalies. Conventional moving horizon estimation algorithms can be used to solve the case containing sparse measurement anomalies, but their performance degrades dramatically as the number of outliers increases. To address this problem, we propose two outliers exclusion-moving horizon estimation strategies. That is, at each sampling instant, solving a set of least-squares cost functions aims to exclude all possible outliers. The state estimates corresponding to the optimal cost are retained and propagated to the next instant, and the procedure is repeated when new information arrives. The stability of the estimation error of the estimators is proved under moderate conditions, namely the observability of the noise-free state equation and the choice of the tuning parameters in the cost function. The simulation results demonstrate the robustness of the proposed approaches in the presence of dense outliers.

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Section: Introductionmentioning

confidence: 99%

Design and stability of moving horizon estimator for discrete-time linear systems subject to multiple measurement outliers

Liu,

Wang,

Yuan

et al. 2023

Transactions of the Institute of Measurement and Control

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“…Over the past few decades, the MHE method has been widely investigated to support applications in several research areas. For example, it has been used to successfully address the estimation problem for the auto-regressive-moving-average with outliers contaminating the output ( Yin, Liu & Gao, 2018 ; Su et al, 2012 ). The author uses the combination of MPC and T-S fuzzy system to design a predictive control method to solve the vehicle trajectory tracking problem, and uses the MHE to obtain the estimation of the vehicle state ( Alcala et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A filter design for T-S fuzzy systems based on moving horizon estimator with measurement noise

Gao

Wang

Hu³

2023

PeerJ Computer Science

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In this article, a filter based on moving horizon estimator is proposed with Takagi-Sugeno (T-S) fuzzy controllers for a kind of unknown discrete-time system. The T-S fuzzy control algorithm is employed to handle the unknown system dynamics, thus ensuring the property of input-to-state stability (ISS) of the system, which guarantees the boundedness of all states. Besides, the proposed filter and controller can significantly improve the robustness of the system with external disturbance, even if the disturbance has non-Gaussian characteristics. Finally, the effectiveness of the presented algorithm is demonstrated by simulation examples under two kind of noise situations.

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“…In Huang and Guo, 49 the convergence analysis is based on limit theorem for both double array martingale and non-negative supermartingales and on techniques of stochastic Lyapunov function and Shi et al 50 established the consistency of LS-based and gradient-based algorithm without assuming that the noise is stationary or ergodic. Although the consistency and convergence properties of RLS estimators for ARMAX models as well for general classes of linear time series models have been proved, [51][52][53][54][55][56] the consistency of ARMAX timedelayed model is still under study. This work deals with the convergence analysis of the extended LS under the persistent excitation condition and the strictly positive real (SPR) condition.…”

Section: Introductionmentioning

confidence: 99%

On consistency of least squares algorithm for ARMAX time-delay models

Bedoui

Abderrahim

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Jointly identification of the sample delay and the parameters of time-delay discrete-time ARMAX models are addressed in this article. Like any identified problem, the proposed approach is based on the solution of an optimization problem. It consists then of two main steps: the first is the problem formulation and the second is the problem resolution. The problem formulation defines the criterion to be minimized according to an unknown parameter vector consisting of the sample delay and the parameters with their corresponding observation vector. Since the sample delay is of integer type, the obtained optimization problem cannot be efficiently solved using a real optimization algorithm. To overcome this difficulty, the rounding properties are used to transform the obtained problem into a real optimization problem. The solution is then done using the least square approach. Consistency of the estimates with their convergence rates is established under the persistent excitation condition. Finally, some numerical simulation and experimental results are presented to illustrate the performance of the proposed method.

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Regularised estimation for ARMAX process with measurements subject to outliers

Cited by 14 publications

References 61 publications

Design and stability of moving horizon estimator for discrete-time linear systems subject to multiple measurement outliers

Design and stability of moving horizon estimator for discrete-time linear systems subject to multiple measurement outliers

A filter design for T-S fuzzy systems based on moving horizon estimator with measurement noise

On consistency of least squares algorithm for ARMAX time-delay models

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