Design of a Robust State Estimator for a Discrete-Time Nonlinear Fractional-Order System With Incomplete Measurements and Stochastic Nonlinearities

Zheng, Xiujuan; Wu, Huaiyu

doi:10.1109/access.2020.2965252

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Kalman filtering via limited capacity or fading communication channels (networks) is a relevant problem raised by [ 154 , 155 , 156 ]. Reference [ 89 ], a very recent paper dealing with the issue of incomplete measurements and stochastic nonlinearities, addresses this topic through a state estimator based on robust fractional-order unscented Kalman filters. The results are compared with other types of filtering methods and the advantages of the proposed approach are highlighted.…”

Section: Discussionmentioning

confidence: 99%

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A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

Muresan

Birs

Dulf

et al. 2021

Sensors

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The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields.

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Section: Discussionmentioning

confidence: 99%

“…Comparisons with the conventional FEKF show that the proposed method achieves better estimation performance. A robust state estimator for discrete-time nonlinear fractional-order systems is developed in [ 89 ]. The same issue regarding incomplete measurement data is tackled.…”

Section: Fractional-order Filtersmentioning

confidence: 99%

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

Muresan

Birs

Dulf

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

A survey on state estimation of complex dynamical networks

Jia

Liu

et al. 2021

International Journal of Systems Science

127

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Fractional-Order Predictive PI Controller for Dead-Time Processes With Set-Point and Noise Filtering

et al. 2020

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In most of the industrial process plants, PI/PID controllers have been widely used because of its simple design, easy tuning, and operational advantages. However, the performance of these controllers degrades for the processes with long dead-time and variation in set-point. Up next, a PPI controller is designed based on the Smith predictor to handle dead-time processes by compensation technique, but it failed to achieve adequate performance in the presence of external noise, large disturbances, and higher-order systems. Furthermore, the model-based controllers structure is complex in nature and requires the exact model of the process with more tunable parameters. Therefore, in this research, a fractional-order predictive PI controller has been proposed for dead-time processes with added filtering abilities. The controller uses the dead-time compensation characteristics of the Smith predictor and the fractional-order controller's robustness nature. For the high peak overshoot, external noise, and disturbance problems, a new set-point and noise filtering technique is proposed, and later it is compared with different conventional methods. In servo and regulatory operations, the proposed controller and filtering techniques produced optimal performance. Multiple real-time industrial process models are simulated with long dead-time to evaluate the proposed technique's flexibility, set-point tracking, disturbance rejection, signal smoothing, and deadtime compensation capabilities.

show abstract

Design of a Robust State Estimator for a Discrete-Time Nonlinear Fractional-Order System With Incomplete Measurements and Stochastic Nonlinearities

Cited by 3 publications

References 29 publications

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

A survey on state estimation of complex dynamical networks

Fractional-Order Predictive PI Controller for Dead-Time Processes With Set-Point and Noise Filtering

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