Dynamic event-based sliding mode security control for singular Semi-Markov jump LPV systems against deception attacks.

In this paper, we preliminarily propose the dissipative sliding mode control (SMC) scheme for polyhedral singular semi-Markov jump linear parameter varying (SS-MJLPV) systems considering deception attacks between the sensor and controller. The main feature of this scheme is that a novel developed parameter dependent integral-type SMC law follows the changes of the system. Note that the mode of the sliding mode controller is not synchronized with the system mode, and the transition rates (TRs) of the system are assumed to be unknown. Moreover, we extend the previous work concerning the static event-triggered transmission protocol (ETP) to the dynamic one, in which the triggering threshold is dynamically updated via the internal-dynamic-variable. Finally, a DC-motor model is presented to illustrate the correctness of the developed results.

Dynamic-Memory Event-Based Asynchronous Attack Detection Filtering for a Class Of Nonlinear Cyber-Physical Systems.

In this article, we endeavor to address the problem of asynchronous attack detection for a class of nonlinear cyber-physical system (CPS) under the dynamic-memory event-triggered transmission protocol (DMETP). Malicious attacks under consideration are composed of the parameter-dependent false data-injection (FDI) attacks and the multichannel deception attacks with the time delay. Meanwhile, we attempt to employ the T-S fuzzy singular Semi-Markov jump linear parameter-varying (SS-MJLPV) systems to describe the stochastic jump characteristics and nonlinear time-varying characteristics of CPSs. Then, the DMETP is proposed for the first time, which can not only relieve the bandwidth pressure but also obtain the key released packets at the crest or trough of the curve. By augmenting the states of the original system and the asynchronous attacks detection filter, the issue of attacks detection is converted into an auxiliary dissipative filtering for the CPS. Moreover, sufficient conditions for the existence of the attacks detection filter are obtained. Finally, the effectiveness of the proposed scheme is verified via the networked truck-trailer reversing system.

H∞ master-slave synchronization for delayed impulsive implicit hybrid neural networks based on memory-state feedback control.

This paper investigates the H∞ master-slave synchronization problem for delayed impulsive implicit hybrid neural networks based on memory-state feedback control. By developing a more holistic stochastic impulse-time-dependent Lyapunov-Krasovskii functional and dealing with the nonlinear neuron activation function, the stochastic admissibility and prescribed H∞ performance index for the synchronization error closed-loop system are achieved. In addition, the desired mode-dependent memory-state feedback synchronization controller is acquired in the form of linear matrix inequalities. The free-weighting matrix technique is adopted to remove the inherent limitation of time-varying delay derivative for the implicit delayed systems, and the derivative of time-varying delay is relaxed enough to be greater than 1. The simulation of genetic regulatory network in bio-economic system is given to verify validity of the derived results.

Output feedback H∞ control for singular hybrid systems with partly unknown transition rates and input saturation.

This paper investigates dynamic output feedback H∞ control for singular Markovian jump systems with partly unknown transition rates and input saturation. Necessary and sufficient conditions that singular Markovian jump system satisfies stochastic admissibility and H∞ performance index are successfully deduced in terms of linear matrix inequalities under the two different conditions of completely known transition rates and partly unknown transition rates. Mode-dependent dynamic output feedback controller is designed to ensure that the closed-loop singular Markovian jump system satisfies stochastic admissibility and H∞ performance index. Novel set invariant condition is proposed, and it not only provides an estimate of the attractive domain of the closed-loop system but also allows the analysis of performance outside the stability region within this invariant set. Furthermore, the estimation of the attraction domain comes down to the determination of the largest contractively invariant ellipsoid satisfying the necessary and sufficient conditions and the novel set invariant condition, and it is solved as an optimization problem with linear matrix inequality constraints. Finally, the effectiveness and utility of the proposed method are verified by a numerical example and an oil catalytic cracking process.

Robust Sampled-Data Control for Switched Complex Dynamical Networks With Actuators Saturation.

In this article, an aperiodic sampled-data control problem is investigated for polytopic uncertain switched complex dynamical networks subject to actuator saturation. Due to the constraint on the upper bound of the sampling interval being no greater than the dwell time, the issue concerning the asynchronization between the sampled-data controller mode and the system mode is hence considered to be caused by subsystems that may switch in a sampling interval. By considering the sampling interval without switching and the sampling interval with switching, the parameters-dependent loop-based Lyapunov functionals are constructed, respectively. With the help of the constructed functional, mean-square exponential stability criteria for the error polytopic uncertain switched complex dynamical networks are presented under the definition of average dwell time. Furthermore, based on the stability criteria, the asynchronous aperiodic sampled-data controller is designed for polytopic uncertain switched complex dynamical networks subject to actuator saturation. The polytopic uncertain switched complex dynamical networks can be guaranteed to exponentially synchronize with the target node based on the proposed stability conditions and aperiodic sampled-data controller design method. Finally, by transforming the proposed theoretical conditions into the LMI-based objective optimization problem, the domain of attraction of polytopic uncertain switched complex dynamical networks is estimated. An example based on switched Chua's circuit is applied to verify the effectiveness of the proposed method.

Sampled-Data Synchronization of Stochastic Markovian Jump Neural Networks With Time-Varying Delay.

In this article, sampled-data synchronization problem for stochastic Markovian jump neural networks (SMJNNs) with time-varying delay under aperiodic sampled-data control is considered. By constructing mode-dependent one-sided loop-based Lyapunov functional and mode-dependent two-sided loop-based Lyapunov functional and using the Itô formula, two different stochastic stability criteria are proposed for error SMJNNs with aperiodic sampled data. The slave system can be guaranteed to synchronize with the master system based on the proposed stochastic stability conditions. Furthermore, two corresponding mode-dependent aperiodic sampled-data controllers design methods are presented for error SMJNNs based on these two different stochastic stability criteria, respectively. Finally, two numerical simulation examples are provided to illustrate that the design method of aperiodic sampled-data controller given in this article can effectively stabilize unstable SMJNNs. It is also shown that the mode-dependent two-sided looped-functional method gives less conservative results than the mode-dependent one-sided looped-functional method.

Event-based asynchronous and resilient filtering for Markov jump singularly perturbed systems against deception attacks.

The dynamic event-based asynchronous and resilient dissipative filter design for Markov jump singularly perturbed systems (MJSPSs) against stochastic deception attacks is discussed in this paper. Firstly, a novel dynamic event-based transmission protocol is provided to further decrease the proportion of sampled data into network. The effect of deception attacks is formulated as a random variable satisfying the Bernoulli distribution. And an asynchronous filter is delicately constructed. Based on the technique of linear matrix inequality (LMI), efficient criteria of stochastically stable for the filtering error systems with a predetermined dissipative performance are obtained. An effective method of jointly design the proposed dynamic event-triggered transmission protocol and the non-synchronous filter is offered. Lastly, a numerical instance and a resistance-capacitance (RC) circuit system are provided to display the effectiveness and the benefit of the developed method.

HMM-Based Asynchronous H∞ Filtering for Fuzzy Singular Markovian Switching Systems With Retarded Time-Varying Delays.

This article reports our study on asynchronous H∞ filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi-Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the H∞ performance level. Simulation examples including a single-link robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.

Reliable mixed H∞ and passive control for networked control systems under adaptive event-triggered scheme with actuator faults and randomly occurring nonlinear perturbations.

In this paper, the problem of reliable mixed H∞ and passive control for a class of nonlinear networked control systems under the adaptive event-triggered scheme with actuator faults and randomly occurring nonlinear perturbations is studied. Firstly, a series of independent stochastic variables with certain probabilistic distribution are presented to formulate the phenomena of actuator faults. Then, different from the traditional event-triggered scheme, the threshold of adaptive event-triggered scheme is determined by an online adaptive control law instead of the preset constant value. Further, based on a novel Lyapunov functional by taking the adaptive control law into account, sufficient conditions for asymptotically stable with mixed H∞ and passive performance are obtained in terms of a set of LMIs. If these LMIs are feasible, the reliable mixed H∞ and passive control gain and the weight parameter of adaptive event-triggered scheme can be gained simultaneously. Finally, two numerical examples are provided to show the effectiveness of the developed technique.

H∞ mode-dependent fault detection filter design for stochastic Markovian jump systems with time-varying delays and parameter uncertainties.

This paper deals with the problem of robust H∞ fault detection for a class of stochastic Markovian jump systems (SMJSs) The aim is to design a linear mode-dependent fault detection filter such that the fault detection system is not only stochastically asymptotically stable in the large, but also satisfies a prescribed H∞-norm level for all admissible uncertainties. By using Lyapunov stability theory and generalized Itô formula, some novel mode-dependent and delay-dependent sufficient conditions in terms of linear matrix inequality (LMI) are proposed to insure the existence of the desired fault detection filter. A simulation example and an industrial nonisothermal continuous stirred tank reactor (CSTR) system are employed to show the effectiveness of the proposed method.