Distributed and decentralized state estimation of Fractional order systems

Kupper, Martin; Gil, Iñigo; Hohmann, Sören

doi:10.1109/acc.2016.7525337

Cited by 12 publications

(18 citation statements)

References 12 publications

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“…(ii) Their ability to predict more accurately the dynamics of the system that is being modelled. (iii) They make it possible to obtain simplified system models with just a few physically motivated parameters [14] and [15]. The main contribution of this paper is on the use of the continuous-time fractional-order MOESP system identification algorithm to help identify massive MIMO frequency-selective wireless channels.…”

Section: Related Work and Paper Contributionmentioning

confidence: 99%

Fractional-Order System Identification and Equalization in Massive MIMO Systems

Lupupa

Hadjiloucas

2020

IEEE Access

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In this paper, a new methodology for the identification and equalization of massive Multiple-Input Multiple-Output (MIMO) channels in fifth generation (5G) wireless communications is proposed. Channel equalization is performed in state-space, having estimated the channel using the fractional-order Multivariable Output Error State Space (MOESP) system identification algorithm. The proposed fractional-order algorithm is an improvement over its integer-order counterpart that is currently used for state-space channel identification/ estimation and state-space channel equalization. When dealing with fractional-order calculus our work adopts the Riemann-Liouville definition. Our numerical results of channel identification having used a chirp signal to excite our massive MIMO system show a lower mean square error (MSE) in channel estimation using the proposed fractional-order MOESP identification algorithm when compared to integer-order MOESP identification algorithm of increased order. Furthermore, following equalization, transmission using Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK) and 256-Quadrature amplitude modulation (256-QAM) signals show that the symbol error rate (SER) as a function of signal to noise ratio (SNR) performance of the fractional-order equalization algorithm compares to that of the integer-order equalization algorithm. The proposed channel identification algorithm also provides a more parsimonious solution for modelling multipath fading, thus enabling the design of fractional-order equalizers. In addition, they may be used in other applications where high order filtering and more complex control algorithms which are difficult to tune would be needed. Finally, the work has other technological applications where there is a requirement for modelling and control of propagation processes in dispersive media.

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Section: Related Work and Paper Contributionmentioning

confidence: 99%

Fractional-Order System Identification and Equalization in Massive MIMO Systems

Lupupa

Hadjiloucas

2020

IEEE Access

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“…Therefore, we consider a maximum number l of past values of x which is called short memory principle (SMP) [25,27]. The fractional, time-variant, and discrete-time state-space representation can be obtained from (2) following [23,28] whereby index k denotes the current time step t k in…”

Section: Fractional Calculusmentioning

confidence: 99%

“…Such distributed filters have been introduced, e.g. in [21,22] and have been extended for fractional models in [23]. A special case of a distributed system, using a hierarchical arrangement of subsystems, is a cascaded system.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

Kupper

Brenneisen

Stark

et al. 2018

2018 Chinese Control and Decision Conference (CCDC)

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In this paper, a cascaded fractional Kalman filter for state of charge and branch current estimation of largescale battery systems is proposed. As a centralized approach for the estimation of a large-scale system is costly in terms of effort and time, a partition into smaller and, therefore, simpler subsystems is applied. Since the overall system is divided into smaller units, a local computation is allowed and complexity reduced. In these distributed systems, usually, the subsystems communicate with each other to exchange relevant data. Using a model based on mesh currents, we receive a cascaded system structure which results in a hierarchical arrangement of all subsystems. This concludes in a one-directional information flow and, therefore, reduces the overall communication effort. Using this proposed approach, it is not only possible to estimate the states of each branch locally but also to calculate the branch currents when the total current is known. Finally, a practical test with real measurement data is presented.

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“…The decentralization and distribution of the Kalman filter has been presented, for example in [9]- [11] for integer order systems and in [12] for fractional order systems. However, the distribution scheme can be complicated and laborious for some system classes, because the choice of appropriate transformation matrices and the implementation of an additional fusion step can be ambiguous and complex.…”

Section: B Distributed and Cascaded Systemsmentioning

confidence: 99%

“…It can be seen in the equations that past estimatesx j will not be updated when new data u k or y k with j < k are obtained. Therefore, the FKF shown in (12) to (19) is a suboptimal state estimation algorithm. An algorithm which also updates and estimates past states is presented in [19].…”

Section: Fractional Kalman Filtermentioning

confidence: 99%

Distributed and decentralized Kalman filtering for Cascaded Fractional order systems

Kupper

Gil

Hohmann

2017

2017 American Control Conference (ACC)

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This paper presents a distributed Kalman filter algorithm for cascaded systems of fractional order. Certain conditions are introduced under which a division of a fractional system into cascaded subsystems is possible. A functional distribution of a large scale system and of the state estimation algorithm leads to smaller and scalable nodes with reduced memory and computational effort. Since each subsystem performs its calculations locally, a central processing node is not needed. All data which are required by subsequent nodes are communicated to them unidirectionally. Also a comparison between the Fractional Kalman Filter (FKF) and the Cascaded Fractional Kalman Filter (CFKF) is given by an example.

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Distributed and decentralized state estimation of Fractional order systems

Cited by 12 publications

References 12 publications

Fractional-Order System Identification and Equalization in Massive MIMO Systems

Fractional-Order System Identification and Equalization in Massive MIMO Systems

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

Distributed and decentralized Kalman filtering for Cascaded Fractional order systems

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