Linear optimal estimator for system subject to random delay and packet dropout without time stamping

SummaryThis note is concerned with the linear estimation problems for discrete‐time systems where the measurements are subject to random time delay and packet dropout. Different from most of previous works, the time‐stamping is assumed to be unavailable in this paper. In this case, the estimation problems for such systems are very difficult because the information of the received measurements is not exactly known in most cases. To overcome the difficulty caused by the random delay and unavailability of time‐stamping, a new observation is introduced by the summation of all the measurements received in the same time. Then, the random time delay measurement system is converted into a constant‐delay measurement system with multiplicative noises where the noise is binary distributed random variables with known distributions. Finally, the linear optimal estimator is derived by using the transformation of auto‐regressive moving average model to state‐space model and the standard Kalman filtering. The convergence and stability of the filter are also analyzed. A simulation example is given to show the effectiveness of the proposed estimator. Copyright © 2016 John Wiley & Sons, Ltd.

This article is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with possible multiple random measurement delays and packet dropouts, where the largest random delay is limited within a known bound and packet dropouts can be infinite. A new model is constructed to describe the phenomena of multiple random delays and packet dropouts by employing some random variables of Bernoulli distribution. By state augmentation, the system with random delays and packet dropouts is transferred to a system with random parameters. Based on the new model, the least mean square optimal linear estimators including filter, predictor and smoother are easily obtained via an innovation analysis approach. The estimators are recursively computed in terms of the solutions of a Riccati difference equation and a Lyapunov difference equation. A sufficient condition for the existence of the steady-state estimators is given. An example shows the effectiveness of the proposed algorithms.

This paper is concerned with the control problem for a class of systems with random delays and packet dropouts which unavoidably occur during the data transmission in the network. A novel model is developed to describe the random one-step delays and multiple packet dropouts from sensors to controllers and from controllers to actuators by applying four Bernoulli distributed stochastic variables. An observer-based feedback controller is designed via a linear matrix inequality (LMI) approach which guarantees the closed-loop networked system to be exponentially stable in the sense of mean square and also achieves the prescribed H ∞ disturbance-rejection-attenuation level. A simulation example shows the effectiveness of the proposed algorithm.

Linear estimation for networked control systems with random transmission delays and packet dropouts

This article is concerned with the optimal sequential fusion filter design issue for a class of sampled‐data systems subject to random transmission delays and multiplicative noise. The considered delay is modeled as a multistate Markov chain, while the multiplicative noise corrupting the observation measurements is assumed be a white noise sequence with known statistical properties. First, by reorganizing the original measurements, the solvability of the addressed problem is cast into the feasibility of an optimal estimation issue for the hybrid Markovian jump system without delays. Then, an optimal sequential fusion filter is constructed by utilizing a dynamic model with finite jumps on basis of the arrival order of measurements from different sensors, guaranteeing estimation performances both at sampling instants and within the sampling intervals. Moreover, the desired estimator gains can be computed via solving a series of coupled differential Riccati equations with jumps and coupled differential Lyapunov equations. Further, a stationary sequential fusion filter is achieved with the guarantee of the exponentially mean‐square stability. Finally, two simulation examples are provided to verify the validity of the proposed algorithms.

A probability‐weighted optimal control strategy for nonlinear stochastic vibrating systems with random time delay is proposed. First, by modeling the random delay as a finite state Markov process, the optimal control problem is converted into the one of Markov jump systems with finite mode. Then, upon limiting averaging principle, the optimal control force is approximately expressed as probability‐weighted summation of the control force associated with different modes of the system. Then, by using the stochastic averaging method and the dynamical programming principle, the control force for each mode can be readily obtained. To illustrate the effectiveness of the proposed control, the stochastic optimal control of a two degree‐of‐freedom nonlinear stochastic system with random time delay is worked out as an example.

In this research, the optimal estimation for networked control system with multiplicative noises (MN), one-step random delays and multiple packet dropouts is studied. Both the sensor-to-estimator and controller-to-actuator channels are affected by MN. The one-step random delays and packet dropouts are characterised by a series of Bernoulli variables. By state augmentation, the random circumstances in channels are transformed into random parameters, and the optimal estimators are derived utilising projection theory. For systems without MN, random delays or packet dropouts, the corresponding estimators can be derived as particular cases of the proposed algorithm. Then a sufficient existence condition of steady-state estimators is derived. Some numerical examples are given to validate the proposed estimation algorithms.

The random time delay brings great challenges to the controller design for the Internet-based teleoperation system, the worst case is that it can destabilize the Internet-based teleoperation system. This paper first gives a brief historical preview of bilateral passive control theory, and then a direct passive scattering transform method, extending the passive bilateral control method to the Internet-based random time delay teleoperation system, is advanced to guarantee the teleoperation system's stability with any non-symmetric random network time delay. Finally, a virtual master-slave manipulator bilateral control scheme is designed for mobile robot teleoperation based on the passive scattering transformation method, and simulations are carried out to verify the results at the end of this paper. © 2012 IEEE.

This paper concentrates on the nonlinear filtering for systems with event-triggered data transmission and random time delays. A stochastic event-triggered sensor schedule is introduced to reduce the excessive measurements, which achieves a lower communication rate with a good estimation performance. The time delay is modeled by a Poisson distribution, and a D-length buffer is used to retrieve partly delayed measurements. Subsequently, a nonlinear filter is designed for the state estimation with event-triggered data transmission and random time delays. An example is provided to verify the performance of the proposed algorithm.

Communication-based train control (CBTC) systems use wireless local area networks (WLANs) to transmit train status and control commands. Since WLANs are not originally designed for applications with high mobility, random transmission delays and packet drops are inevitable, which could result in unnecessary traction, brakes or even emergency brakes of trains, loss of line capacity, and passenger satisfaction. In this paper, we study the packet drops introduced by random transmission errors and handovers in CBTC systems, analyze the impact of random packet drops on the stability and performances of CBTC systems, and propose two novel schemes to improve the performances of CBTC systems. Unlike the existing works that only consider a single train and study the communication issues and train control issues separately, we model the system to control a group of trains as a networked control system (NCS) with packet drops in transmissions. Extensive field test and simulation results are presented. We show that our proposed schemes can provide less energy consumption, better riding comfortability, and higher line capacity compared with the existing scheme.

This study is concerned with the linear estimation problem for networked systems with one‐step random delay and multiple packet dropouts. At each moment, the estimator may receive one or two data packets or nothing. The predictor of sensor measurement at the current instant is used as a compensator if the current measurement does not arrive at the estimator. Based on the developed compensation model, the optimal linear estimators including filter, predictor and smoother are proposed by the innovation analysis approach. Compared with the estimators based on the compensation of using the latest measurement previously received in the existing literatures, the proposed estimators have higher estimation accuracy and smaller computational burden. Simulation results show the effectiveness of the proposed algorithms.

For Internet based real-time teleoperation systems, random time delay can cause instability in the closed loop control system and hence hinder task accomplishment. Event based control systems have been proposed to overcome the instability caused by the random time delay. High latency at the transport layer can still impede effective and reliable execution of tasks with high dexterity requirements. Network QoS based dynamic resource allocation has been proposed to increase the efficiency and reliability of task execution. However, these approaches only try to mitigate or overcome the effects of random time delay and do not address the cause of latency issues in the communication channel. This paper addresses the efficiency and reliability requirements for supermedia enhanced teleoperated systems by reducing the end-to-end transmission latency through the use of overlay networks. The proposed system reduces the transmission latency by using multiple, disjoint paths in overlay networks. The proposed system facilitates reliable and efficient task completion for tasks with high dexterity requirements. Experimental validation of the proposed teleoperated system using the PlanetLab Network is provided for the task of teleoperating a mobile manipulator system.

For networked multisensor systems with mixed uncertainties, including random one-step sensor delays, multiplicative noises and uncertain noise variances, a new augmented state approach with the fictitious noises is presented, by which the original system model is transformed into one without sensor delays and only with white noises. An extended Lyapunov equation approach with two Lyapunov equations is presented, which is applied to prove the robustness of the estimators. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, the four weighted fusion robust time-varying and steady-state Kalman estimators (predictor, filter and smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness and accuracy relations are proved, and their convergence in a realization is proved by the dynamic error system analysis (DESA) method. Specially, the presented modified robust covariance intersection (CI) fuser has higher robust accuracy than the original one. A simulation example applied to the uninterruptible power system (UPS) shows the effectiveness of the proposed approaches and results.

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example.

In this paper, we study stabilization of discrete-time systems in which control inputs are transmitted over a random delay and packet drop channel. A predictor-like linear feedback controller is proposed by a reduction technique in which the reduced system has the same dimension as that of the original system. A necessary and sufficient condition to stabilize the original system in the mean-square sense is obtained based on an algebraic Riccati equation. Some necessary and sufficient stabilizability conditions based on the Lyapunov criterion are also presented. Interestingly, it is shown that the stabilization problem considered in this paper is equivalent to the stabilization problem of a particular delay-free system with control-dependent correlated noises. Simulation examples are provided to illustrate the effectiveness and advantages of the proposed stabilizing controller.