Random Transmission Delays Research Articles

Modeling and filtering for a stochastic uncertain system in a complex scenario

This paper presents a new approach to filter signals for discrete-time physical problems with stochastic uncertain in the presence of random data transmission delays, out-of-order packets and correlated noise. To deal with the packet disorder, the system model synthesizing the transmission delays and out-of-order packets from the plant to the filter is established by utilizing signal reconstruction schemes based on the zero-order-holder and logic zero-order-holder. A robust finite horizon Kalman filter is proposed by augmenting the state-space model and minimizing the error covariance. To further improve the filtering performance, a linear estimation-based delay compensation strategy is proposed by employing the reorganized time-stamped measurements. Moreover, for solving the missing measurement problem whilst reducing the computational costs, an artificial delay compensation approach is established using an one-step prediction approach. Simulation results show the effectiveness of the proposed method.

A Comparative Simulation Study of Deterministic and Stochastic Strategies for Reduction of Packet Reordering in Multipath Data Streaming

Multipath routing gains clear network performance advantages for data streaming in networked systems with high path diversity. The level of packet reordering, however, becomes higher: distant packets are reordered, the application performance is reduced due to head-of-line blocking at the destination, and a large resequencing buffer is needed for sorting incoming packets. In this paper, we study by simulation the stochastic compensation effect to reduce packet reordering. If a source randomizes packet scheduling into multiple paths of random transmission delays, then these two sides of randomness “quench” each other. We perform comparison experiments to test this hypothesis in various multipath configurations, focusing on deterministic vs. randomized strategies of packet scheduling. The experiments confirm the existence of the stochastic compensation effect and its considerable influence on the application performance. © 2016, UK Simulation Society. All rights reserved.

Robust measurement fusion steady-state estimator design for multisensor networked systems with random two-step transmission delays and missing measurements

In this paper, we study the centralized fusion (CF) and weighted measurement fusion (WMF) robust steady-state Kalman filtering problem for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, two-step random delays, missing measurements, and uncertain noise variances. By using a model transformation approach consisting of augmented approach, de-randomization approach and fictitious noise approach, the original multisensor system under study is converted into a multi-model multisensor system with only uncertain noise variances. By introducing an augmented state vector, and applying the weighted least squares (WLS) algorithm, the CF and WMF systems are obtained. According to the minimax robust estimation principle, based on the worst-case fusion systems with conservative upper bounds of uncertain noise variances, the CF and WMF robust steady-state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Their robustness is proved by using a combination method consisting of augmented noise approach, matrix representation approach of quadratic form, and Lyapunov equation approach, the so-called robustness is concerned with the design of a filter such that for all admissible uncertainties, the actual fused steady-state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady-state Kalman estimators are proved. An example with application to autoregressive (AR) signal processing is proposed, which shows that the robust fusion signal estimation problems can be solved by the robust fusion state estimation method. Simulation example shows the effectiveness and correctness of the proposed method.

Clock Synchronization in Wireless Networks Using Matrix Completion-Based Maximum Likelihood Estimation

Clock synchronization has been a challenging task in the design of wireless networks because the synchronization accuracy suffers from uncertain transmission delay and/or packet loss due to poor wireless channel conditions. To address this problem, this paper develops robust clock synchronization schemes based on the matrix completion theory to improve the accuracy in presence of random transmission delay and packet loss. We propose a matrix completion-based maximum likelihood estimator (MC-MLE) to estimate the clock offset and clock skew under Gaussian transmission delay model. Thanks to the denoise feature of matrix completion, the proposed schemes outperform the maximum likelihood estimator (MLE) in the presence of packet loss and random transmission delay. That is, the proposed schemes are closer to the Cramer-Rao lower bound (CRLB) than the MLE when the timestamp packets are randomly corrupted. The robustness and accuracy of the proposed schemes are validated by numerical results.

Decentralized Networked Load Frequency Control in Interconnected Power Systems Based on Stochastic Jump System Theory

Random transmission delay and packet loss may cause load frequency control (LFC) performance degradation or even instability in interconnected power systems. Traditional robust methods mainly focus on guaranteeing the asymptotic stability with pre-estimating a maximum delay case. As a result, the designed controller cannot satisfy the actual operational requirements completely since the improper network performance estimation. In this paper, based on jump system theory, the state-space model for LFC system with the different transmission delay is established as the jumping decision variable, which describes the possible dynamics of the networked LFC system with the bounded time delay. Secondly, in order to avoid the defect of conservative or optimistic design caused by imprecise pre-set delay, the upper boundary of the transmission delay in the power communication system is calculated by deterministic network theory. Furthermore, the asymptotic stability constraints in bilinear matrix inequality forms are deduced via construction of a Lyapunov function. Moreover, to reduce the peak value, peak time and setting time of frequency deviations caused by power mismatches, an iterative optimization algorithm is proposed to obtain the optimal feedback matrix. Simulation studies with two-area and five-area interconnected power systems show that the proposed method can avoid optimistic or conservative design effectively while the stability and dynamic performance can be ensured simultaneously.

Extended Kalman filtering subject to random transmission delays: Dealing with packet disorders

This paper studies the extended Kalman filtering problem for a class of nonlinear discrete-time systems with random transmission delays (RTDs) and RTD-induced packet disorders. The relationship between the RTDs and the resulting packet disorders is discussed. The bounded RTDs, which take place in the sensor-to-filter channel, are modeled as independent and identically distributed random variables obeying a certain probability distribution. A novel filter structure is proposed that utilizes an integer-valued function of the mathematical expectation of the RTDs so as to compensate the RTD-induced effects. Under the proposed extended Kalman filter, an upper bound for the filtering error covariance is derived by solving two Riccati-like difference equations, and subsequently minimized (in the sense of trace) by appropriately designing the filter gains. A numerical simulation is provided to verify the validity of the developed filter design scheme.

Recursive resilient filtering for nonlinear stochastic systems with packet disorders

In this paper, a new resilient filtering problem is studied for a class of nonlinear stochastic systems with packet disorders. The underlying system is quite comprehensive that considers both deterministic and stochastic nonlinearities. The phenomenon of packet disorders takes place in the sensor-to-filter channel as a result of the limited capability of the communication network. The random transmission delay, which is the main cause for the packet disorders, is modeled as a bounded random variable obeying a known probability distribution and its influence on the filter performance is examined. Furthermore, the resilient issue of the proposed recursive filter against random fluctuations of the filter gain is thoroughly studied. The purpose of this paper is to develop a resilient filter for the addressed nonlinear stochastic systems such that, in the presence of both stochastic nonlinearities and packet disorders, an upper bound of the filtering error covariance is ensured and then locally minimized through adequate design of the filter gains. Finally, a simulation example is given to illustrate the usefulness of the theoretical results.

Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing

Abstract This paper investigates the distributed fusion estimation problem for networked systems whose multisensor measured outputs involve uncertainties modelled by random parameter matrices. Each sensor transmits its measured outputs to a local processor over different communication channels and random failures –one-step delays and packet dropouts– are assumed to occur during the transmission. White sequences of Bernoulli random variables with different probabilities are introduced to describe the observations that are used to update the estimators at each sampling time. Due to the transmission failures, each local processor may receive either one or two data packets, or even nothing and, when the current measurement does not arrive on time, its predictor is used in the design of the estimators to compensate the lack of updated information. By using an innovation approach, local least-squares linear estimators (filter and fixed-point smoother) are obtained at the individual local processors, without requiring the signal evolution model. From these local estimators, distributed fusion filtering and smoothing estimators weighted by matrices are obtained in a unified way, by applying the least-squares criterion. A simulation study is presented to examine the performance of the estimators and the influence that both sensor uncertainties and transmission failures have on the estimation accuracy.

Earliest deadline first scheduling technique for different networks in network control system

The idea of this research is to determine the best network providing the reduced network time delay in network controlled system (NCS). NCS tackles robust and nonlinear processes that are time-varying or the requirements for the control change with the operating conditions using earliest deadline first (EDF) scheduling technique. It is very useful for minimizing the effects of parameter variations with respect to the process dynamics. The main advantage of applying EDF is that the parameters of the controller can be changed according to deadline along with the process changes. The main limiting factor is that auxiliary measurement should respond quickly to the process change. By applying the EDF algorithm in NCS schemes, the control of the two-tank water-level system is simulated using PID controllers and the control via Internet is simulated with the constant time delays and time-varying delay conditions. The drawbacks of NCS are network-induced delay, packet losses, bit errors, time-varying random transmission delay, lack of synchronization between devices, bandwidth limitations and integration of the modules. As the EDF scheduling approach is based on the mean time-delay parameters, they shift from one set to another when the mean time delay changes from one set to another. Therefore, time-delay issues are reduced by this algorithm. In this research work, the main challenge network-induced delay is reduced by scheduling technique and suitable network for PID control for EDF scheduling is determined among CAN, Ethernet, Switched Ethernet, TDMA and FDMA. CAN and Ethernet have the minimum of 1 ms network-induced time delay when compared with Switched Ethernet, TDMA and FDMA under overload condition. The integral of absolute error is employed to measure the quality of control of each control loop value for EDF scheduling 0.00354 and processor utilization is estimated as U = 0.12.

Event-based finite horizon state estimation for stochastic systems with network-induced phenomena

This paper investigates a finite horizon state estimation problem for a class of discrete-time stochastic systems with random transmission delays and out-of-order packets of data. Employing an event-driven signal-choosing scheme of logic zero-order-holder (LZOH), a system model is established synthetically in a unified form considering the network-induced phenomena, to drop out-of-order packets and improve system performance. By virtue of the established system model, a novel minimum error covariance matrix for the augmented state-space is obtained from the estimated variance constraint. With the aid of a finite horizon, the upper boundary of estimation error covariance is introduced during the information transmission from sensor to estimator, and the appropriate filter parameters are probed. To improve the estimation performance and alleviate the computation burden, an estimation-based compensation approach for random transmission delays is proposed using the received valid signals. Finally, the effectiveness and applicability of the proposed state estimation method are illustrated by a numerical simulation.

Event-triggered distributed fusion estimation with random transmission delays

Recently distributed fusion estimation problem has been widely studied because of better estimation accuracy, reliability and robustness. In this paper, an event-triggered distributed fusion estimation problem is investigated for a multi-sensor nonlinear networked system with random transmission delays. For each communication channel, an event-triggered scheduling mechanism is introduced to reduce excessive measurement transmission, and a D-length buffer is used to retrieve partly delayed measurements. Based on a sequential covariance intersection fusion technique, a distributed fusion estimation algorithm is designed utilizing local estimations calculated by modified unscented Kalman filter (UKF). Sufficient conditions are established to ensure boudedness of fusion estimation error covariance. Finally, comparative simulations indicate that measurement transmission is reduced for each communication channel while still maintaining satisfactory estimation performance by the proposed technique.

Power Peak Shaving With Data Transmission Delays for Thermal Management in Smart Buildings

This paper presents a scheme aimed at mitigating the influence of random data transmission delays in networked thermal appliance control systems in smart buildings. The impact of this type of delays is first analyzed, and it is proposed to utilize loose timing synchronization and add blank gaps between the consecutive appliance operations to avoid the possible violation of the given power budget. A cooperative control of thermal appliance operation is developed using a networked model predictive control-based controller to deal with delays. It is also shown that the schedulability of such a control scheme can be assessed online. The performance of the proposed control scheme is assessed by a simulation study based on the thermal dynamics of an eight-room office building. The obtained results show that the proposed solution can achieve an efficient power peaks shaving in the presence of random network delays.

A Novel Predictive Control Scheme with an Enhanced Smith Predictor for Networked Control System

This paper presents a novel predictive control scheme with an enhanced Smith predictor for a networked control system with random time delays and system uncertainties. In the scheme, time axis is partitioned into equidistant small intervals to limit the continuous time varying delays into several discrete values. The stability of the networked control system is achieved by establishing an offline database and an online update strategy for Smith predictor, which reduces the reliance on the determined model of delay and system uncertainties in comparison with conventional Smith predictor. In this way, a sequence of finite forward predictive control signals of all possible time delays can be generated in advance and the actual delays will be compensated in real time when achieving the real delay information. Illustrative examples are given to demonstrate the effectiveness and robustness of the proposed predictive methods towards the random transmission delays and system uncertainties integrated in the networked control system.

Covariance-based fusion filtering for networked systems with random transmission delays and non-consecutive losses

The distributed and centralized fusion filtering problems for multi-sensor networked systems with transmission random one-step delays and non-consecutive packet losses are addressed. The signal evolution model is not required, as only covariance information is used. The measurements of individual sensors, subject to uncertainties modeled by random matrices and correlated noises, are transmitted to local processors through different communication channels and, due to random transmission failures, some of the data packets may be delayed or even definitely lost. The random transmission delays and non-consecutive packet losses are modeled by sequences of Bernoulli variables with different probabilities. By an innovation approach, local least squares linear filtering estimators are obtained by recursive algorithms; the distributed fusion framework is then used to obtain the optimal matrix-weighted combination of the local filters, using the mean squared error as optimality criterion. Also, a recursive least squares linear estimation algorithm is designed within the centralized fusion framework.

Multi-sensor distributed fusion estimation with applications in networked systems: A review paper

In this paper, the advances of distributed fusion estimation (DFE) algorithms for multi-sensor networked systems (NSs) are reviewed. First, several general DFE algorithms are reviewed. Then, some random phenomena in NSs are discussed, including data quantization, random transmission delays, packet dropouts, fading measurements and communication disturbances. Finally, the advances of DFE algorithms for NSs are summarized.

Quantized fuzzy passification for nonlinear systems with Markov-based transmission delays

In this paper, the quantized fuzzy passification issue for nonlinear systems subject to random transmission delays is studied. A discrete-time homogeneous Markov chain, whose state transition probability matrix is partially unknown, is employed to model the random transmission delays. And a logarithmic quantizer is introduced to overcome the channel capacity constraint. A sufficient condition is derived by constructing a Lyapunov function, which depends both on the fuzzy basis and the transmission delay, to guarantee that the augmented closed-loop system is mean-square exponentially stable and passive in the sense of expectation. Then, the controller is designed with the linearization of this condition. Finally, the effectiveness of the proposed design technique for nonlinear systems is demonstrated through a numerical example.

Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays.

This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices.

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.

Event-Triggered Faults Tolerant Control for Stochastic Systems with Time Delays

This paper is concerned with the state-feedback controller design for stochastic networked control systems (NCSs) with random actuator failures and transmission delays. Firstly, an event-triggered scheme is introduced to optimize the performance of the stochastic NCSs. Secondly, stochastic NCSs under event-triggered scheme are modeled as stochastic time-delay systems. Thirdly, some less conservative delay-dependent stability criteria in terms of linear matrix inequalities for the codesign of both the controller gain and the trigger parameters are obtained by using delay-decomposition technique and convex combination approach. Finally, a numerical example is provided to show the less sampled data transmission and less conservatism of the proposed theory.

H∞output tracking control for uncertain networked control systems via a switched system approach

SummaryIn this paper, the problem ofH∞output tracking control for networked control systems with random time delays and system uncertainties is investigated. Effective sampling instant that is tightly related with transmission delay from sensor to actuator is proposed to ensure that the random variable time delay is always shorter than one effective sampling period. By using both active time‐varying sampling period strategy and hybrid node‐driven mechanism, the switching instant is coincided with the effective sampling instant. An augmented time‐varying networked tracking system model is provided by including the output tracking error as an additional state. However, random transmission delay causes indeterminate sampling period, which induces infinite subsystems. Gridding approach is introduced to transform the continuous time axis into discrete‐time sequences, which guarantees the finite number of switching rules. By employing multiple Lyapunov–Krasovskii functions, linear matrix inequality (LMI)‐based output trackingH∞performance analysis is presented, and robust switchingH∞model reference tracking controller for networked control systems with communication constraints and system uncertainties is designed to guarantee asymptotic tracking of prescribed reference outputs while rejecting disturbances. Finally, simulation results illustrate the correctness and effectiveness of the proposed approaches. Copyright © 2015 John Wiley & Sons, Ltd.