Protocol-Based Unscented Kalman Filtering in the Presence of Stochastic Uncertainties

Liu, Shuai; Wang, Zidong; Chen, Yun; Wei, Guoliang

doi:10.1109/tac.2019.2929817

Cited by 96 publications

(38 citation statements)

References 47 publications

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“…Finally, a simulation example has been given to demonstrate the effectiveness of the proposed control method. The further topic motivated by the main results can be listed as: (1) the extension of the method to observer-based SMC for networked systems with different communication transmission protocols as in Shen, Wang, Shen, Alsaadi, and Alsaadi (2020), Liu, Wang, Chen, and Wei (2019), Wang, Wang, Chen, and Sheng (2019), Zou, Wang, Hu, and Gao (2017), Zou, Wang, Hu, and Zhou (2020) and quantized observation in Zou, Wang, Hu, and Han (2020), Liu, Wang, Han, and Jiang (2020); (2) the discussion of the less conservatism of proposed SMC technique handling the delays.…”

Section: Discussionmentioning

confidence: 99%

Observer-based sliding mode control for discrete nonlinear systems with packet losses: an event-triggered method

Guan

Cui

et al. 2020

Systems Science & Control Engineering

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In this paper, the observer-based output feedback sliding mode control (SMC) problem is investigated for discrete delayed nonlinear systems subject to packet losses under the event-triggered strategy. It is assumed that the packet losses may occur in the control channel from the sensor to the observer. A suitable compensation strategy via the Bernoulli distributed random variable is used to reduce the effects of packet losses. In order to avoid the phenomenon of network congestion during the networked transmission, an event-triggered mechanism is introduced to determine if the last released measurement needs to be updated. Based on the zero-order-hold (ZOH) measurement, an output feedback observer is designed to reconstruct the system state. This method can facilitate the design of the discrete-time sliding surface. A sufficient condition is proposed to guarantee the stochastic stability of sliding mode dynamics systems by using linear matrix inequality (LMI) method, and a novel observer-based sliding mode controller is synthesized to force the trajectories of the error systems onto the pre-designed sliding mode surface within finite time. Finally, an example is given to illustrate the validity of the proposed theoretical result.

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Section: Discussionmentioning

confidence: 99%

Observer-based sliding mode control for discrete nonlinear systems with packet losses: an event-triggered method

Guan

Cui

et al. 2020

Systems Science & Control Engineering

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“…Step 2. At time instant k + 1, solve the Riccati-like difference equation (28) to obtain Ξ k+1|k , and then substituting it into (32) and (29) derives the filter gain and the upper bound of the filtering error covariance, respectively. Moreover, the updated statex k+1|k+1 is obtained by using (8a) and (8b).…”

Section: Theorem 1 Let the Initial Conditionmentioning

confidence: 99%

“…For networked control systems with limited communication capacities, in order to avoid the data collisions and reduce the occurrence of the resulted network‐induced phenomena, 22‐24 a practical yet efficient way is to introduce certain data scheduling protocols to allocate the network resource for multiple data packet transmissions. In industry applications, the broadly employed communication protocols include the Round‐Robin (RR) protocol, the try‐once‐discard protocol and the stochastic communication protocol, based on which the corresponding control/filtering problems have stirred some initial attention in the past few years, see, for example, References 25‐34. For those protocol‐based control/filtering problems, there are generally two approaches for dealing with challenges resulting from the protocols, namely, the time‐delay system approach 35,36 and the switched system approach 27,28 …”

Section: Introductionmentioning

confidence: 99%

Protocol‐based extended Kalman filtering with quantization effects: The Round‐Robin case

Liu

Wang

et al. 2020

Intl J Robust & Nonlinear

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This article investigates the extended Kalman filtering problem for a class of stochastic nonlinear systems with quantization effects and Round-Robin (RR) communication protocols. The uniform quantization is considered and the resulting quantization error is characterized as an additive white noise sequence obeying the uniform distribution over certain intervals. For the sake of reducing communication traffic of the network as well as alleviating data collisions, the RR mechanism is introduced to schedule the data transmission from the sensors to the filter. By combining the periodic property of the RR protocol and the zero-order holder strategy, the input signal of the filter is modeled by a sequence of delayed quantized measurements. The main purpose of this article is to design an extended Kalman filter for the stochastic nonlinear systems, in the simultaneous presence of quantization errors, stochastic nonlinearities, and stochastic noises, such that an optimized upper bound for the filtering error covariance is derived. By solving two coupled Riccati-like difference equations, the filter gain matrix is explicitly formulated. An RR protocol-based recursive filtering algorithm is developed for the online implementation. Furthermore, a sufficient condition is established to ensure the uniform boundedness of the filtering error in the mean-square sense. Finally, a simulation example is given to demonstrate the practical validity of the designed filter algorithm.

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“…Typical state estimation schemes include, but are not limited to, the recursive state estimation,  ∞ state estimation, set-membership estimation and moving horizon estimation (MHE) approaches. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Among others, the MHE (also called receding-horizon estimation) is one of the mostly applied algorithms dealing with complex nonlinear and/or constrained systems in industrial applications. 31 In a large number of real-world systems, it is quite common that different measurement signals are sampled with different rates, and the resulting multirate measurements are justified by the following two main reasons.…”

Section: Introductionmentioning

confidence: 99%

Moving horizon estimation with multirate measurements and correlated noises

Zou

Wang

Dong

et al. 2020

Intl J Robust & Nonlinear

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This article is concerned with the moving horizon estimation (MHE) problem for a class of linear discrete-time systems subject to multirate measurements and correlated noises. The sensors of the plant are grouped into two nodes with different sampling periods, which give rise to the multirate measurements. Under the effects of multirate measurements, the underlying system is then described by a periodic system with correlated noises. A MHE strategy is formulated and the corresponding analytical solution of the state estimation is derived by using the "completing the square" technique. Based on the lifting approach, the dynamics of the estimation error covariance is analyzed and the desired estimator parameters are obtained by solving a set of linear matrix inequalities. An illustrative example is given to show the effectiveness of our proposed MHE method.

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Protocol-Based Unscented Kalman Filtering in the Presence of Stochastic Uncertainties

Cited by 96 publications

References 47 publications

Observer-based sliding mode control for discrete nonlinear systems with packet losses: an event-triggered method

Observer-based sliding mode control for discrete nonlinear systems with packet losses: an event-triggered method

Protocol‐based extended Kalman filtering with quantization effects: The Round‐Robin case

Moving horizon estimation with multirate measurements and correlated noises

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