$H_{\infty}$ Filtering for Discrete-Time Systems With Stochastic Incomplete Measurement and Mixed Delays

Shi, Peng; Luan, Xiaoli; Liu, Fei

doi:10.1109/tie.2011.2167894

Cited by 248 publications

(124 citation statements)

References 29 publications

Supporting

Mentioning

124

Contrasting

Order By: Relevance

“…Even when the sensor has computational abilities,x s k cannot be computed by the smart sensor, since its computation requires u a k , which can be seen from (4). What the smart sensor can send to the estimator is the raw measurement y k .…”

Section: Further Discussion On Conditions 1 Andmentioning

confidence: 99%

“…The introduction of network does bring numerous advantages, but in the meantime, it also causes some network-induced constraints, such as limited bandwidth, quantization errors, which may result in transmission packet losses [1][2][3][4][5]. For NCSs with packet losses, there are two fundamental protocols in network communication, which are the transmission control protocol (TCP) and the user datagram protocol (UDP).…”

Section: A Background and Motivationsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Lin

Shi

et al. 2017

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper, we study the problems of optimal estimation and control, i.e., the linear quadratic Gaussian (LQG) control, for systems with packet losses but without acknowledgment. Such acknowledgment is a signal sent by the actuator to inform the estimator of the incidence of control packet losses. For such system, which is usually called as a User Datagram Protocol (UDP)-like system, the optimal estimation is nonlinear and its calculation is time-consuming, making its corresponding optimal LQG problem complicated. We first propose two conditions: 1) the sensor has some computation abilities; and 2) the control command, exerted to the plant, is known to the sensor. For a UDP-like system satisfying these two conditions, we derive the optimal estimation. By constructing the finite and infinite product probability measure spaces for the estimation error covariances (EEC), we give the stability condition for the expected EEC, and show the existence of a measurable function to which the EEC converges in distribution, and propose some practical methods to evaluate the estimation performance. Finally, the LQG controllers are derived, and the conditions for the mean square stability of the closed-loop system are established.

show abstract

Section: Further Discussion On Conditions 1 Andmentioning

confidence: 99%

Section: A Background and Motivationsmentioning

confidence: 99%

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Lin

Shi

et al. 2017

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, the simultaneous presence of time delay and packet loss has been addressed in [89][90][91]; both quantization and packet dropout have been taken into consideration in [92]; a filtering scheme has been recently proposed in [93] which is robust against both channel fadings and gain variations. For more related results, the readers are referred to [94][95][96][97][98][99][100][101].…”

Section: Treatments Of Network-induced Phenomenamentioning

confidence: 99%

Particle filtering with applications in networked systems: a survey

Wang

Yuan

2016

Complex Intell. Syst.

View full text Add to dashboard Cite

The particle filtering algorithm was introduced in the 1990s as a numerical solution to the Bayesian estimation problem for nonlinear and non-Gaussian systems and has been successfully applied in various fields including physics, economics, engineering, etc. As is widely recognized, the particle filter has broad application prospects in networked systems, but network-induced phenomena and limited computing resources have led to new challenges to the design and implementation of particle filtering algorithms. In this survey paper, we aim to review the particle filtering method and its applications in networked systems. We first provide an overview of the particle filtering methods as well as networked systems, and then investigate the recent progress inThe work of W. Li, Y. Yuan and L.

show abstract

“…This is why different from the traditional transmission-delaybased and packet-loss-based sampling period scheduling schemes which have been more extensively studied (e.g. [12][13][14][15][16][17]), we view packet disordering as a key index of QoS of a network and propose a novel packet-disordering-based sampling and control strategy for the LQR problem of an NCS.…”

Section: Main Contributionsmentioning

confidence: 99%

Sampling and control strategy: networked control systems subject to packet disordering

Huang

2016

IET Control Theory & Applications

View full text Add to dashboard Cite

This study proposes a novel sampling and control strategy to find a suboptimal sampling period sequence and control input sequence, such that a quadratic cost function of state and control input of a networked control system (NCS) with packet disordering is minimised. First, a discrete-time system model of the NCS with packet disordering, transmission delay and packet loss in terms of displacement values of packets is put forward. Second, a linear quadratic regulation (LQR) problem of the NCS is formulated, showing that the optimal controller depends on sampling period and quality of services (QoS) of networks. Interactive effects between sampling period and QoS of networks pose a challenge in solving the LQR problem of the NCS. To overcome this difficulty, different from traditional transmission-delay-based or packet-loss-based sampling scheme, a novel packet-disordering-based sampling period selection scheme is proposed. Furthermore, an algorithm is presented to find a suboptimal solution to the LQR problem in this study. Finally, simulation results demonstrate the effectiveness of the proposed approach. IntroductionFacing the increasing demand of control networks in industrial processes, commercial systems and traffic systems, a rapidly increasing focus on networked control systems (NCSs) has been witnessed [1][2][3]. NCSs have been hotspots of research in the past decades. The introduction of control networks can improve the efficiency, flexibility and reliability of these applications, reducing installation, maintenance, reconfiguration and costs. However, some unexpected phenomena potentially occur in network channels including transmission delay, packet loss and packet disordering and so on [4,5]. Therefore, different from traditional control systems, optimal control problems of NCSs require not only control system design, but also network system design. In this paper, we investigate the effects of sampling rate and packet disordering on solution of linear quadratic regulation (LQR) problem. We first formulate the LQR problem for the following continuous linear systemẋwhere x ∈ R n and u ∈ R m , respectively, are state and control input of the plant, and A ∈ R n×n and B ∈ R n×m are matrices with appropriate dimensions. The term t 0 is the initial time instant and x 0 is the initial state. Consider the following quadratic performance index for system (1)where Q ∈ R n×n and R ∈ R m×m are positive semi-definite matrix and positive definite matrix, respectively, and the term t f is the terminal time instant.The control input is assumed to be piecewise constantwhere u k = u(t k ), t k is the kth sampling time instant with 0 ≤ t 0 < t 1 < · · · < t k < · · · ≤ t f , and h k = t k+1 − t k is called sampling period. We assume h min ≤ h k ≤ h max , where h max and h min are the maximum and minimum values of the sampling period, respectively. For given sampling periods, the optimal control input u k that minimises the cost function (2) for system (1) can be analytically determined through solving the disc...

show abstract

$H_{\infty}$ Filtering for Discrete-Time Systems With Stochastic Incomplete Measurement and Mixed Delays

Cited by 248 publications

References 29 publications

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

Particle filtering with applications in networked systems: a survey

Sampling and control strategy: networked control systems subject to packet disordering

Contact Info

Product

Resources

About