Time-stamped predictive functional control for networked control systems with random delays

The random delays in a networked control system (NCS) degrade control performance and can even destabilize the control system.To deal with this problem,the time-stamped predictive functional control (PFC) algorithm is proposed,which generalizes the standard PFC algorithm to networked control systems with random delays.The algorithm uses the time-stamp method to estimate the control delay,predicts the future outputs based on a discrete time delay state space model,and drives the control law that applies to an NCS from the idea of a PFC algorithm.A networked control system was constructed based on TrueTime simulator,with which the time-stamped PFC algorithm was compared with the standard PFC algorithm.The response curves show that the proposed algorithm has better control performance.

Fault Tolerant Synchronization for a General Complex DynamicalNetwork with Random Delay

A fault tolerant synchronization strategy is proposed to synchronize a complex network with random time delays and sensor faults. Random time delays over the network transmission are described by using Markov chains. Based on the Lyapunov stability theory and stochastic analysis, several passive fault tolerant synchronization criteria are derived,which can be described in the form of linear matrix inequalities. Finally,a numerical simulation example is carried out and the results show the validity of the proposed fault tolerant synchronization controller.

Mean square stability of recurrent neural networks with random delay and Markovian switching

To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.

Asymptotical mean square stability of cellular neural networks with random delay

In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.

Design and performance analysis of networked control systems with random delay

The design and performance analysis of networked control systems with random network delay in the forward channel is proposed, which are described in a state-space form. A new control scheme is used to overcome the effects of network transmission delay, which is termed networked predictive control （NPC）. Furthermore, three different ways to choose control input are discussed and the performances are analyzed, respectively. Both real-time simulations and practical experiments show the effectiveness of the control scheme.

Optimal full-order filtering for discrete-time systems with random measurement delays and multiple packet dropouts

This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, an optimal full-order filter for the state of the system is presented, which is shown to be of the form of employing the received outputs at the current and last time instants. The solution to the optimal filter is given in terms of a Riccati difference equation governed by two binary random variables. The optimal filter is reduced to the standard Kalman filter when there are no random delays and packet dropouts. The steady-state filter is also investigated. A sufficient condition for the existence of the steady-state filter is given. The asymptotic stability of the optimal filter is analyzed.

Optimal linear estimator for discrete-time systems with random delays

In this paper, optimal estimation for discrete-time linear time-varying systems with randomly state and measurement delays is considered. By introducing a set of binary random variables, the system is converted into the one with both multiplicative noises and constant delays. Then, an estimator which includes the cases of smoothing and filter- ing, is derived via the projection formula, and the solution is given in terms of a partial difference Riccati equation with boundary conditions. A predictor for such systems is also presented based on the proposed filter and smoother. The ob- tained estimators have the same dimension as the original state. Conditions for existence, uniqueness, and stability of the steady-state optimal estimators are studied for time-invariant cases. In this case, the obtained estimators are very easy to implement and all calculations can be performed off line, leading to a linear time-invariant estimator.

Reliable Control for Nonlinear Systems with Stochastic Actuators Fault and Random Delays Through a T-S Fuzzy Model Approach

This paper considers the reliable control design for T-S fuzzy systems with probabilistic actuators faults and random time-varying delays. The faults of each actuator occurs randomly and its failure rates are governed by a set of unrelated random variables satisfying certain probabilistic distribution. In terms of the probabilistic failures of each actuator and time-varying random delays, new fault model is proposed. Based on the new fuzzy model, reliable controller is designed and sufficient conditions for the exponentially mean square stability （EMSS） of T-S fuzzy systems are derived by using Lyapunov functional method and linear matrix inequality （LMI） technique. It should be noted that the obtained criteria depend on not only the size of the delay, but also the probability distribution of it. Finally, a numerical example is given to show the effectiveness of the proposed method.

WHITE NOISE ESTIMATION FOR DISCRETE-TIME SYSTEMS WITH RANDOM DELAY AND PACKET DROPOUT

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal input white noise estimator is presented based on the stochastic Kahnan filtering theory. However, tb_e optimal white-noise estimator is timevarying, stochastic, and doesn＇t converge to a steady state in general. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.

Guaranteed cost control for discrete-time networked control systems with random Markov delays

The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality （LMI） approach. A simulation example illustrates the effectiveness of the proposed method.

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features.In addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem.The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample time.In the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion method.Furthermore,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is bounded.Finally,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

MEC Enabled Cooperative Sensing and Resource Allocation for Industrial IoT Systems

In industrial Internet of Things systems,state estimation plays an important role in multisensor cooperative sensing.However,the state information received by remote control center experiences random delay,which inevitably affects the state estimation performance.Moreover,the computation and storage burden of remote control center is very huge,due to the large amount of state information from all sensors.To address this issue,we propose a layered network architecture and design the mobile edge computing(MEC)enabled cooperative sensing scheme.In particular,we first characterize the impact of random delay on the error of state estimation.Based on this,the cooperative sensing and resource allocation are optimized to minimize the state estimation error.The formulated constrained minimization problem is a mixed integer programming problem,which is effectively solved with problem decomposition based on the information content of delivered data packets.The improved marine predators algorithm(MPA)is designed to choose the best edge estimator for each sensor to pretreat the sensory information.Finally,the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.

Optimal linear estimators for systems with random measurement delays

This paper is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with random measurement delays. A new model that describes the random delays is constructed where possible the largest delay is bounded. Based on this new model, the optimal linear estimators including filter, predictor and smoother are developed 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. The steady-state estimators are also investigated. A sufficient condition for the convergence of the optimal linear estimators is given. A simulation example shows the effectiveness of the proposed algorithms.

Balanced energy-efficient relay selection scheme in wireless mobile relay networks

In order to resolve the relay selection problem in wireless mobile relay networks （WMRNs）, a novel balanced energy-efficient mobile relay selection scheme is proposed in this paper. Compared with traditional counter-based algorithm, distance and energy consumption are considered from network respect to provide a better network lifetime performance in the proposed scheme. Also, it performs well when nodes move freely at high speed. A random assessment delay （RAD） mechanism is added to avoid collisions and improve transmission efficiency. Simulation results reveal that, the proposed scheme has advantages in prolonging network lifetime, balancing energy compared with existing counter-based scheme. consumption and reducing the total energy consumption

Real-Time nonlinear predictive controller design for drive-by-wire vehicle lateral stability with dynamic boundary conditions

Due to flexible drive-by-wire technology,vehicle stability control can improve handling and lateral stability under extreme conditions.However,this technology can also increase the probability of random transmission delay.This paper proposes a nonlinear model predictive control(NMPC)strategy to improve vehicle stability and compensate for the random time delay.First,by combining the nonlinear dynamic characteristics and driver behavior,we obtain a stable region of the yaw rate and the sideslip angle under complex driving conditions.Second,an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral stability.Finally,the actuator receives the optimized control sequence and compensates for the random time delay of the transmission channel.CarSim/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.