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.

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.

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.

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.

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.