Estimating the fates of the control packets for Networked Control Systems with loss of control and measurement packets

Blind, Rainer; Allgöwer, Frank

doi:10.1109/cdc.2009.5400030

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…Hence, it cannot be used in practice. To address the computational complexity, various linear [2,15,17,18] and nonlinear [19,20] sub-optimal estimators have been proposed to approximate the optimal estimator, but they are not good approximations in terms of the estimation criterion, since the criterion for these linear estimators (see Definition 5) differs from the criterion for the optimal estimator (see Definition 1). From [19,20], it is clear that the nonlinear estimators are not obtained according to either one of these two criteria above.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…To address the computational complexity, various linear [2,15,17,18] and nonlinear [19,20] sub-optimal estimators have been proposed to approximate the optimal estimator, but they are not good approximations in terms of the estimation criterion, since the criterion for these linear estimators (see Definition 5) differs from the criterion for the optimal estimator (see Definition 1). From [19,20], it is clear that the nonlinear estimators are not obtained according to either one of these two criteria above. As one of the most costefficient estimation schemes, the interacting multiple model (IMM) estimator is proven to be a good approximation for the optimal estimator, since it is able to obtain the estimate fairly close to the optimal one [21][22][23], which motivates us to apply the IMM estimator to UDP-like systems, and then explore its properties, especially the three aforementioned key aspects: stability, convergence, and performance.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…From (20), it follows that the conditional average estimation becomes worse, as the uncertainty of θ k increases.…”

Section: Definition 4 a Matrix Function F (θ) Is Said To Have The Samentioning

confidence: 99%

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Interacting Multiple Model Estimator for Networked Control Systems: Stability, Convergence, and Performance

Lin

Lam

Chen

et al. 2019

IEEE Trans. Automat. Contr.

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Abstract-In this paper, we study the interacting multiple model (IMM) estimator for networked control systems with packet loss but without packet acknowledgment (ACK). The ACK is a signal sent by the actuator to inform the estimator of whether control packets are lost or not. A system with ACK is usually called a TCP-like system; otherwise, it is called a UDPlike system. We show that the stability of the IMM estimator for UDP-like systems is determined by the observation packet arrival rate (p.a.r.) and is independent of the control p.a.r. and control inputs. The IMM estimator is stable if the observation p.a.r. is greater than a critical value. We show that this critical value is the same as the critical value for the stability of the optimal estimator for its corresponding TCP-like system. If control inputs eventually tend to zero, the error covariance of the IMM estimator converges to that of the optimal estimator for TCP-like systems. We characterize the impact of the control/observation p.a.r. and the control input on estimation performance. Finally, we prove that the average estimation performance of the IMM estimator approximates that of the optimal estimator within a finite bound, and is superior to that of the linear minimum mean square error estimator.

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Section: A Background and Motivationmentioning

confidence: 99%

Section: A Background and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Interacting Multiple Model Estimator for Networked Control Systems: Stability, Convergence, and Performance

Lin

Lam

Chen

et al. 2019

IEEE Trans. Automat. Contr.

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“…For the systems with random losses of ACK signals, we show that the optimal estimation is quite different from the TVKF for the systems with ACK in [11], and from those for the systems without ACK in [17,18]. In the optimal estimation, the number of terms increases exponentially as the ACK signals drop, which makes its computation timeconsuming.…”

Section: Introductionmentioning

confidence: 96%

Fast filtering algorithm for state estimation of lossy networks

Lin

et al. 2014

IET Control Theory & Applications

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The authors study the state estimation issue of networked control systems in which observations, control commands and acknowledgement (ACK) signals are randomly dropped. They show that unlike the transmission control protocol-like or user datagram protocol-like case, the probability density function of system state turns out to be Gaussian mixture with exponentially increasing terms because of the random losses of ACK signals, which makes the optimal filtering time-consuming. They develop a sub-optimal but computationally efficient filtering algorithm, named as fast filtering (FF), and establish a packet-loss-rate-based sufficient condition for the boundedness of the expected estimation error covariance matrices. By a numerical example, the effectiveness of the proposed FF algorithm is illustrated, and the mutual influences of the packet losses of observations, control commands and ACK signals on the estimation performance are analysed.

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“…The stability of the OE only depends on the observation packet loss rate; however, the OE consists of an exponentially growing number of components, making its computation time-consuming [13]. To address the computational problem, two approximate optimal estimators (AOEs) [3,12] were developed to compute the optimal estimates for S i.i.d…”

mentioning

confidence: 99%

State estimation over non-acknowledgment networks with Markovian packet dropouts

et al. 2019

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Estimating the fates of the control packets for Networked Control Systems with loss of control and measurement packets

Cited by 8 publications

References 12 publications

Interacting Multiple Model Estimator for Networked Control Systems: Stability, Convergence, and Performance

Interacting Multiple Model Estimator for Networked Control Systems: Stability, Convergence, and Performance

Fast filtering algorithm for state estimation of lossy networks

State estimation over non-acknowledgment networks with Markovian packet dropouts

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