Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances

Qin, Yuemei; Liang, Yan; Yang, Yanbo; Yang, Feng

doi:10.1016/j.automatica.2016.07.013

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…In the electronic countermeasure environment, when an aircraft target flies in the atmosphere, its dynamic characteristics are very complex due to the uncertain manoeuvring characteristics, which cannot be accurately described by only one possible target trajectory pattern [30,31]. Besides, the UI and systematic biases always exist in the process of manoeuvring target tracking due to the atmospheric turbulence, sensor drifts, faults, and deception jamming [32]. Therefore, a class of hybrid target tracking systems with the UI and systematic biases is expressed as follows:…”

Section: Problem Formulationmentioning

confidence: 99%

“…In the electronic countermeasure environment, when an aircraft target flies in the atmosphere, its dynamic characteristics are very complex due to the uncertain manoeuvring characteristics, which cannot be accurately described by only one possible target trajectory pattern [30, 31]. Besides, the UI and systematic biases always exist in the process of manoeuvring target tracking due to the atmospheric turbulence, sensor drifts, faults, and deception jamming [32]. Therefore, a class of hybrid target tracking systems with the UI and systematic biases is expressed as follows:

x_{k + 1} = {bold-italicA}_{k, m_{k}} {bold-italicx}_{k} + {bold-italicB}_{k, m_{k}} {bold-italicμ}_{k, m_{k}} + {bold-italicζ}_{k, m_{k}}

{bold-italicy}_{i, k + 1} = {bold-italicC}_{i, k + 1} x_{k + 1} + {bold-italicD}_{i, k + 1} {bold-italicb}_{i, k + 1} + {bold-italicη}_{i, k + 1}, i = 1, 2, \dots, N

where

x_{k + 1} \in R^{Ω}

represents the target tracking system state at time

k + 1

normalΩ

is the dimension of the state

{bold-italicx}_{k}

, and

{bold-italicμ}_{k, m_{k}} \in R^{r}

is the UI.…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

Feng

Deng

et al. 2020

IET signal process.

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In the multisensor target tracking system, the key of the target tracking performance depends on the state estimation accuracy to a great extent. However, the system uncertainties will seriously affect the performance of the state estimation. Up to now, little research focuses on the state estimation for the multi-sensor hybrid target tracking systems with multiple uncertainties including the multiple models, the unknown inputs and the systematic biases. In this study, the minimum error entropy based on the multiple model estimation for the multisensor hybrid uncertain target tracking systems with the multiple system uncertainties is presented. The minimum variance unbiased filter based on the general systematic bias evolution model decoupled with the unknown state is designed to estimate the optimal systematic biases and compensate the system measurements. Taking full advantage of the compensated measurement information in time and space, the multiple model observer based on the minimum error entropy is designed to obtain the optimal state estimation. The simulation results of the target tracking scenario illustrate the effectiveness of the proposed method, and the indoor target tracking and positioning experiment based on the ultrawideband further verifies that the proposed method is satisfying.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

“…In the electronic countermeasure environment, when an aircraft target flies in the atmosphere, its dynamic characteristics are very complex due to the uncertain manoeuvring characteristics, which cannot be accurately described by only one possible target trajectory pattern [30, 31]. Besides, the UI and systematic biases always exist in the process of manoeuvring target tracking due to the atmospheric turbulence, sensor drifts, faults, and deception jamming [32]. Therefore, a class of hybrid target tracking systems with the UI and systematic biases is expressed as follows:

x_{k + 1} = {bold-italicA}_{k, m_{k}} {bold-italicx}_{k} + {bold-italicB}_{k, m_{k}} {bold-italicμ}_{k, m_{k}} + {bold-italicζ}_{k, m_{k}}

{bold-italicy}_{i, k + 1} = {bold-italicC}_{i, k + 1} x_{k + 1} + {bold-italicD}_{i, k + 1} {bold-italicb}_{i, k + 1} + {bold-italicη}_{i, k + 1}, i = 1, 2, \dots, N

where

x_{k + 1} \in R^{Ω}

represents the target tracking system state at time

k + 1

normalΩ

is the dimension of the state

{bold-italicx}_{k}

, and

{bold-italicμ}_{k, m_{k}} \in R^{r}

is the UI.…”

Section: Problem Formulationmentioning

confidence: 99%

Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

Feng

Deng

et al. 2020

IET signal process.

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“…As a kind of stochastic system with several modes jumping according to the Markov chain, Markov jumping systems (MJSs) can simulate many practical systems, such as power systems, communication systems, manufacturing systems, and so on. [1][2][3][4][5][6] Therefore, many control schemes have been proposed for MJSs, such as H ∞ control, [7][8][9] sliding mode control, [10][11][12][13] and so on. It should be noticed that the systems discussed in the above literatures are linear MJSs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite‐time output feedback control for Markov jumping nonlinear systems

Zhao

2021

Adaptive Control & Signal

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In this article, the problem of output feedback tracking control for uncertain Markov jumping nonlinear systems is studied. A finite-time control scheme based on command filtered backstepping and adaptive neural network (NN) technique is given. The finite-time command filter solves the problem of differential explosions for virtual control signals, the NN is utilized to approximate the uncertain nonlinear dynamics and the adaptive NN observer is applied to restructure the state of system. The finite-time error compensation mechanism is established to compensate the errors brought by filtering process. The proposed finite-time tracking control algorithm can ensure that the solution of the closed-loop system is practically finite-time stable in mean square. Two simulation examples are employed to demonstrate the effectiveness of the proposed control algorithm.

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“…Further, in [28], a new compensation scheme is proposed to attenuate the effects from both the randomly occurring faults and the sensor saturations onto the estimation performance. Recently, the optimal control, antiwindup control, and model predictive control have become the most basic methods to deal with saturation problems [33,34].…”

Section: Introductionmentioning

confidence: 99%

Disturbance Attenuation and Rejection for a Class of Switched Nonlinear Systems Subject to Input and Sensor Saturations

Wei

Sun

Jia

et al. 2019

Mathematical Problems in Engineering

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This paper investigates the problem of disturbance attenuation and rejection for a class of switched nonlinear systems subject to input and sensor saturations, in which exosystem generated disturbances and H2-norm bounded disturbances are considered. The full-order and reduced-order observers are designed according to whether the system states are available or not. Based on the estimating values of the system states and exosystem generated disturbances, the design schemes for the composite controllers are put forward based on the full-order and reduced-order observers, respectively. For a switched system, the input and sensor saturations would influence the effective synthesis of observer and controller. By sector nonlinearity technology, the stability of the augmented closed-loop systems under the proposed composite controllers are analyzed, and the conditions of synthesis of the observers and controllers are further presented to ensure the augmented systems to be robustly asymptotically stable with a weighted H∞ performance level. An example is given to guarantee the effectiveness of the proposed control schemes.

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Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances

Cited by 18 publications

References 20 publications

Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

Adaptive finite‐time output feedback control for Markov jumping nonlinear systems

Disturbance Attenuation and Rejection for a Class of Switched Nonlinear Systems Subject to Input and Sensor Saturations

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