JLS-PPC: A Jump Linear System Framework for Networked Control

Reed, Brooks L.; Hover, Franz S.

doi:10.1109/tcst.2016.2589822

Cited by 7 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. Finally, we can compute p(γ k , Γ k−1 |Y k ), and thus obtain the MAP estimate γk by ( 8) and (9). The estimate xk|k is then updated from (4) by replacing γ k with γk .…”

Section: A Bayesian Kalman Filter Imentioning

confidence: 99%

“…T HE Kalman filter (KF) has a simple structure in optimally correcting propagated state estimates with the sensor measurement of the observed system, and has been successfully applied in countless guidance, navigation and control (GNC) related applications. While in some practical applications, the estimator may not always access the true sensor measurement [1]- [9]. For example, a sensor fault results in that the estimator only receives a pure noise [4]- [6], which does not contain any information of the estimated system.…”

Section: Introductionmentioning

confidence: 99%

“…• In a target tracking system, the position, distance, and velocity of the target to the base station is measured by a radar or Global Positioning System (GPS). The sensor information is sent to a missile or an unmanned aircraft via resource limited wireless channels where communications between devices are power constrained and therefore limited in range and reliability [7]- [9]. If the true sensor measurement is lost, the base station only receives error data, e.g., channel noises.…”

Section: Introductionmentioning

confidence: 99%

“…compute(9) and approximately obtain the MAP estimate of γ k by(8).3) Measurement update: Given y k andγ k , update the state estimate and its estimation error covariance matrix via (4) by replacing γ k withγ k . 4) Time update: Update the state prediction via(5).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Bayesian Filtering With Unknown Sensor Measurement Losses

Zhang

You

Xie

2019

IEEE Trans. Control Netw. Syst.

View full text Add to dashboard Cite

This work studies the state estimation problem of a stochastic nonlinear system with unknown sensor measurement losses. If the estimator knows the sensor measurement losses of a linear Gaussian system, the minimum variance estimate is easily computed by the celebrated intermittent Kalman filter (IKF). However, this will no longer be the case when the measurement losses are unknown and/or the system is nonlinear or non-Gaussian. By exploiting the binary property of the measurement loss process and the IKF, we design three suboptimal filters for the state estimation, i.e., BKF-I, BKF-II and RBPF. The BKF-I is based on the MAP estimator of the measurement loss process and the BKF-II is derived by estimating the conditional loss probability. The RBPF is a particle filter based algorithm which marginalizes out the loss process to increase the efficiency of particles. All the proposed filters can be easily implemented in recursive forms. Finally, a linear system, a target tracking system and a quadrotor's path control problem are included to illustrate their effectiveness, and show the tradeoff between computational complexity and estimation accuracy of the proposed filters.Index Terms-Stochastic systems, networked estimation, intermittent Kalman filter, sensor measurement losses, particle filter. PLACE PHOTO HEREJiaqi Zhang received the B.S. degree from the

show abstract

“…. Finally, we can compute p(γ k , Γ k−1 |Y k ), and thus obtain the MAP estimate γk by ( 8) and (9). The estimate xk|k is then updated from (4) by replacing γ k with γk .…”

Section: A Bayesian Kalman Filter Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Filtering With Unknown Sensor Measurement Losses

Zhang

You

Xie

2019

IEEE Trans. Control Netw. Syst.

View full text Add to dashboard Cite

show abstract

“…In this work, we consider predictive control to design controllers for dynamic systems where the communication between the controller and the actuators is affected by correlated packet dropouts. Traditionally, the packet dropouts are modeled as an independent and identically distributed ( i.i.d ) random process . This model is also extended to include transmission delays .…”

Section: Introductionmentioning

confidence: 99%

Predictive control for networked systems affected by correlated packet loss

Peters

Marelli

Quevedo

et al. 2017

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this paper, we consider the predictive control problem of designing receding horizon controllers for networked linear systems subject to random packet loss in the controller to actuator link. The packet dropouts are temporarily correlated in the sense that they obey a Markovian transition model. Our design task is to solve the optimal controller that minimizes a given receding horizon cost function, using the available packet loss history. Due to the correlated nature of the packet loss, standard linear quadratic regulator methods do not apply. We first present the optimal control law by considering the correlations. This controller turns out to depend on the packet loss history and would typically require a large lookup table for implementation when the Markovian order is high. To address this issue, we present and compare several suboptimal design approaches to reduce the number of control laws. KEYWORDSjump linear systems, networked control, optimal control, packet dropouts, predictive control, stochastic systems 5078

show abstract