Security control of Markovian jump neural networks with stochastic sampling subject to false data injection attacks

The security control of Markovian jumping neural networks(MJNNs)is investigated under false data injection attacks that take place in the shared communication network.Stochastic sampleddata control is employed to research the exponential synchronization of MJNNs under false data injection attacks(FDIAs)since it can alleviate the impact of the FDIAs on the performance of the system by adjusting the sampling periods.A multi-delay error system model is established through the input-delay approach.To reduce the conservatism of the results,a sampling-periodprobability-dependent looped Lyapunov functional is constructed.In light of some less conservative integral inequalities,a synchronization criterion is derived,and an algorithm is provided that can be solved for determining the controller gain.Finally,a numerical simulation is presented to confirm the efficiency of the proposed method.

Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

Formation-containment control of multi-robot systems under a stochastic sampling mechanism

This paper studies the problem of formation-containment for multi-robot systems with stochastic sampling.First,a stochastic sampling control protocol is proposed,in which information exchanging among robots only occurred at the sampling time and two different sampling periods randomly switch.Thus,both energy and controller updating frequencies can be reduced.Also,the protocol can be applied to the situation where the sampling period varies stochastically.Second,sufficient conditions guaranteeing mean square formation-containment are derived.Under stochastic sampling mechanism,the leaders reach a geometric formation shape and the followers are in the geometric formation shape formed by the leaders.Finally,an example is shown to demonstrate the results.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Goodness of Fit Test Based on BLUS Residuals for Error Distribution of Regression Model

The error distribution testing plays an important role in linear regression as distribution misspecification seriously affects the validity and efficiency of regression analysis. The least squares (OLS) residuals are often used to construct test statistics;in order to overcome the non-independent and identically residuals, the best linear unbiased scale (BLUS) residuals are applied in this paper, which, unlike OLS residuals, the residuals vector is identically and independently distributed. Based on the BLUS residuals, a new test statistic is constructed by using the sample random distance between sample quantile and quasi sample quantile derived from the null distribution, and the goodness-of-fit test of error distribution in the linear model is studied. The powers of the new tests under certain alternatives are examined. They are more powerful tests for the hypotheses concerned.

A novel cooperative mid-course guidance scheme for multiple intercepting missiles

In the interception engagement,if the target movement information is not accurate enough for the mid-course guidance of intercepting missiles,the interception mission may fail as a result of large handover errors.This paper proposes a novel cooperative mid-course guidance scheme for multiple missiles to intercept a target under the condition of large detection errors.Under this scheme,the launch and interception moments are staggered for different missiles.The earlier launched missiles can obtain a relatively accurate detection to the target during their terminal guidance,based on which the latter missiles are permitted to eliminate the handover error in the mid-course guidance.A significant merit of this scheme is that the available resources are fully exploited and less missiles are needed to achieve the interception mission.To this end,first,the design of cooperative handover parameters is formulated as an optimization problem.Then,an algorithm based on Monte Carlo sampling and stochastic approximation is proposed to solve this optimization problem,and the convergence of the algorithm is proved as well.Finally,simulation experiments are carried out to validate the effectiveness of the proposed cooperative scheme and algorithm.