This paper investigates the event-triggered secure control problem for fuzzy Markov jump systems (MJSs) with time-varying delays. In order for higher resource efficiency, a multi-level probabilistic event-triggered mechanism (MLPETM) is newly established, which reduces the data transmission frequency by investigating possible variations of triggering thresholds. Given the network-induced phenomena, the MLPETM’s output may undergo cyber-attacks, and the controller may not switch synchronously along with the system. Asynchronous secure controllers are thus developed to perform secure control of fuzzy MJSs. Sufficient conditions dependent on fuzzy basis functions, state delays and jumping modes are established to guarantee the dissipative finite-time boundedness of the resultant closed-loop system. Afterward, an easy-to-check fuzzy secure control algorithm is developed to compute the controller gains. Finally, the one-link robotic arm system and the truck-trailer system are borrowed to validate the proposed results. Note to Practitioners—Networked control systems (NCSs) play a key role in real applications like automobile industry, power systems, and autonomous mobile robots. As a key part, the communication network such as Controller Area Network is embedded into the NCSs to perform the communication tasks between different control units. In spite of great benefits from the communication network, both the security and the efficiency issues of the data transmission are challenging for the engineering designer. Besides, the target physical system is usually nonlinear, and is very likely to undergo abrupt changes in the networked environment. In terms of these challenges, this paper studies the asynchronous security control of networked Markov jump systems with time-varying delays and randomly occurring cyber-attacks. A multi-level probabilistic event-triggered mechanism (MLPETM) is newly established, which studies the effect of the stochastic variation regarding the triggering threshold on the triggering performance. Potential cyber-attacks are modelled in a randomly occurring fashion and then absorbed into to the control law which allows operation modes asynchronous to the system modes. Finally, the one-link robotic arm system and the truck-trailer system are borrowed to check the proposed findings. Note that both the transmission security and communication traffic regulation are well concerned, which renders the developed method applicable to practical systems.

This paper studies the dynamic event-triggered output feedback control problem for networked switched systems (NSSs) under communications consisting of network congestion and data collisions. A dynamic event-triggered mechanism (ETM) is constructed in the sensor-controller network channel, which reduces unnecessary communications and saves network resources. To prevent data collisions in the controller-actuator network channel, the Round-Robin protocol (RRP) is used for communication scheduling, where a periodic function is used to select nodes gaining access to the network. Under the dynamic ETM and RRP, a dynamic output feedback controller is designed, and sufficient conditions are derived to ensure the mean square exponential stability (MSES) of the underlying NSSs using the mode-dependent average dwell time (MDADT) approach and multiple Lyapunov function techniques. Switching-mode-dependent controllers are further derived to achieve the desired stability. Finally, an application oriented example of F-18 aircraft model is provided to verify the usefulness of the proposed theoretical results.

This paper deals with the event-triggeredH∞asynchronous secure control problem for piecewise homogeneous Markov jump systems (PHMJSs) with denial-of-service (DoS) attacks and mode-dependent deception attacks. With the aid of acknowledgement character technique and probabilistic threshold division method, a mode-dependent switching-like probabilistic event-triggered mechanism is established to save communication resources while remedying the adverse effects of both DoS attack-induced packet losses and stochastic fluctuation of the triggering threshold on PHMJSs. Sufficient criteria are developed to ensure the stochastic stability andH∞performance of PHMJSs with hybrid cyber-attacks. It analytically builds the relationship between the maximum number of consecutive packet losses induced by DoS attacks and the triggering parameter. A co-design scheme is provided to calculate the event-triggering weight matrices and controller gains. Ultimately, an application-oriented example of the F-404 aircraft engine system is offered to demonstrate the validity of the proposed approach.

Robust resilient control is promising and effective since it can combat gain perturbations in control system design. In this article, finite-time resilient control is investigated for the networked Markov switched nonlinear systems (NMSNSs). Both additive and multiplicative feedback gain perturbations with randomly occurring manners are considered during the controller design to improve its tolerance of inaccurate controller implementation. The mode information that is partially available to the controller is incorporated into the design. With the help of the fuzzy-logic method, fuzzy resilient output-feedback controllers are established to cope with the gain perturbations and immeasurable system states. Then, by developing a novel switching model, relaxed conditions compared with the existing results are obtained which guarantee the finite-time boundedness (FTB) of NMSNSs. Based on the FTB analysis, a fuzzy design algorithm is proposed via a new separation approach to obtain the controller gains. Eventually, simulations are conducted via a multimode robotic arm system to validate the achieved results.

This paper investigates the attack-compensated output control problem for a class of Markov jump cyber-physical systems (MJCPSs) subject to mismatched modes. A sub-domain-awareness adaptive probabilistic event-triggered mechanism (APETM) is innovatively developed to fully enhance the control performance of the networked control system. Here, sub-domain-awareness means the network’s communication ability, which may be affected by time delay and network resource. To defend against the cyber-attacks imposed on the communication channel, a predictor-based compensator is constructed to mitigate the impact of attacks on the control performance. Different from the existing works, the mismatch degree between the controller and the system, governed by a random variable obeying any discrete-time distribution over$[0, 1]$, is changing dynamically. A mismatched output feedback controller is designed based on the APETM and attack-compensator, and an augmented closed-loop system is obtained. Both time delay-and triggering threshold-dependent Lyapunov functionals are introduced to perform the asymptotical stability analysis. With a separation technique, a solving algorithm is provided which converts the non-convex analysis conditions into traceable ones. Finally, simulations are conducted to validate the proposed results through a mass-spring-damping system model.Note to Practitioners—Cyber-physical systems (CPSs) have found many applications in the fields such as smart grid, gas distribution systems, and automated manufacturing systems. The open communication network deployed in CPSs face two main challenges including the security of data transmission and the efficiency of resource utilization. Owing to the practical environment and other factors, system parameters may change abruptly, which can be well defined by a Markov model. In the resultant Markov jump system, it is typically assumed that the mode mismatch degree between the controller and the system remains the same, which is unrealistic in a time-varying network environment. Motivated by these observations, this paper investigates the secure output control of Markov jump CPSs (MJCPSs) subject to mismatched modes and unknown cyber-attacks. An adaptive probabilistic event-triggered mechanism is innovatively proposed, which compared with the existing mechanisms strikes a higher level balance between the communication burden and the control performance. And attack-compensated mismatched output controllers are developed to stabilize MJCPSs and meanwhile preserve the security level. Finally, a mass-spring-damping system is adopted to verify the application effectiveness of the proposed results. Note that communication traffic control and secure control are finely involved based on CPSs, making our proposed results more applicable to practical systems.

This article investigates the extended dissipative finite-time boundedness (ED-FTB) problem for fuzzy switched systems under deception attacks. To improve the network resource efficiency, a multidomain probabilistic event-triggered mechanism (MDPETM) is proposed. The mode mismatched phenomenon is modeled based on the switching delay information between the controller mode and the system mode. To extract the true signal generated by the MDPETM, a virtual delay concept is developed. The constraint that the controller and the system must have the same premise variables is removed. Based on the MDPETM, mismatched fuzzy state feedback controllers are first devised which may not share the same modes with the system. Then, by establishing fuzzy basis and controller mode-dependent Lyapunov functionals, sufficient criteria free of nonlinear terms existing in the literature are derived, which ensure the ED-FTB of the closed-loop system under admissible delays and deception attacks. Finally, an application-oriented one-link robotic arm system is utilized to validate the theoretical results.

This article addresses the finite-time dissipative fuzzy state estimation for Markov jump systems under mixed cyber attacks. A probabilistic event-triggered mechanism (PETM) is proposed to reduce the unwanted network traffic by using the statistic information of network-induced delays. The dual asynchronizations characterized by asynchronous modes and mismatched premise variables are tackled simultaneously. Under the PETM, Takagi-Sugeno (T-S) fuzzy state estimators are first constructed based on the imperfect measurements subject to mixed cyber attacks and exogenous disturbances. Less conservative criteria relying on both fuzzy rules and jumping modes are established to achieve the strictly (Q,S,R) - ϑ -dissipative finite-time state estimation performance. Furthermore, a synthesis algorithm is derived to calculate the fuzzy state estimator gains by virtue of an improved matrix decoupling technique. Finally, two examples are utilized to validate the effectiveness and advantage of the proposed results.

This article investigates the finite-time secure filter design of fuzzy switched cyber-physical systems equipped with a resource-constraint network that may undergo false data injection attacks (FDIAs). To strike a higher level balance between the resource consumption and filtering performance, a multidomain probabilistic event-triggered mechanism (MDPETM) is initially developed. And the mode mismatched phenomenon between the filter and the system is characterized through a delayed switching signal. Based on the MDPETM and a virtual delay partitioning approach, fuzzy mismatched secure filters are first devised whose modes could differ from the system. Then, filter-mode-dependent Lyapunov functionals are created to obtain new sufficient criteria such that the filtering error achieves finite-time boundedness with extended dissipativity subject to admissible FDIAs. The filter gains are obtained by solving a set of convex optimization problems. Finally, an application-oriented example is employed to test the effectiveness and advantages of the proposed results.

This paper investigates the H ∞ trajectory tracking control for a class of nonlinear systems with time-varying delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) technique. A unified model consisting of a linear delayed dynamic system and a bounded static nonlinear operator is introduced, which covers most of the nonlinear systems with bounded nonlinear terms, such as the one-link robotic manipulator, chaotic systems, complex networks, the continuous stirred tank reactor (CSTR), and the standard genetic regulatory network (SGRN). First, the definition of the tracking control is given. Second, the H ∞ performance analysis of the closed-loop system including this unified model, reference model, and state feedback controller is presented. Then criteria on the tracking controller design are derived in terms of LMIs such that the output of the closed-loop system tracks the given reference signal in the H ∞ sense. The reference model adopted here is modified to be more flexible. A scaling factor is introduced to deal with the disturbance such that the control precision is improved. Finally, a CSTR system is provided to demonstrate the effectiveness of the established control laws.

This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the H∞ performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the H∞ estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical systems, but also obtain less conservative results. Finally, three illustrative examples are provided to validate the proposed control laws.

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.