Improved event-triggered control for networked control systems subject to deception attacks

This paper investigates the event-triggered control problem for networked control systems subject to deception attacks. An improved event-triggered scheme is proposed to reduce transmission rate by using both the information of the relative error and the past released signals. Under the proposed event-triggered scheme, a new switched time-delay system model is proposed for the event-triggered control systems. Based on the new model, the exponential mean-square stability criteria are derived by using the constructed Lyapunov function. Then, a co-design method is developed to obtain both trigger parameters and mode-dependent controller gains. Finally, the proposed scheme is verified by an unmanned aerial vehicle system.     

Dual-Condition event-Triggered distributed adaptive neural control for multi-Agent systems with nonlinear fault

This paper studies the cooperative adaptive dual-condition event-triggered tracking control problem for the uncertain nonlinear nonstrict feedback multi-agent systems with nonlinear faults and unknown disturbances. Under the framework of backstepping technology, a new threshold update method is designed for the state event-triggered mechanism. At the same time, we develop a novel distributed dual-condition event-triggered strategy that combined the fixed threshold triggered mechanism acted on the controller with the new event-triggered mechanism, which can better reduce the waste of communication bandwidth. To deal with the algebraic loop problem caused by the non-affine nonlinear fault, the Butterworth low-pass filter is introduced. At the same time, the unknown function problems are solved by the neural network technology. All signals of the system are semiglobally uniformly ultimately bounded and the tracking performance is achieved, which proved by the Lyapunov stability theorem. Finally, the results of the simulation test the efficiency of the proposed control scheme.     

Memory-based event-triggered leader-following consensus for T-S fuzzy multi-agent systems subject to deception attacks

In this paper, the problem of fuzzy model-based leader-following consensus control for multi-agent systems (MASs) under deception attacks is investigated. For the sake of alleviating the communication burden, a novel memory-based event-triggered scheme (METS) is first proposed for the considered MASs to reduce redundant data transmission, and the leader-following consensus can be achieved faster with a smaller adjustment error by applying the historical released packets. Considering the designed METS and upper-bounded attacks synthetically, the closed-loop fuzzy system model is well established. Furthermore, with the help of Lyapunov-Krasovskii technique, some sufficient conditions are derived to ensure the consensus of MASs subject to deception attacks. Finally, a simulation example is introduced to manifest the effectiveness of the proposed method.     

Event-triggered finite-time fault detection for delayed networked systems with conic-type nonlinearity and deception attacks

The paper is concerned with the finite-time fault detection (FTFD) problem for a class of delayed networked systems subject to conic-type nonlinearity and randomly occurring deception attacks (RODAs) via dynamic event-triggered mechanism (DETM). The nonlinear function with the conic-type constraint is limited to a known hypersphere with uncertain center. Moreover, a variable governed by Bernoulli distribution is introduced to characterize the RODAs phenomenon. In order to reduce unnecessary communication transmissions, a DETM is considered in the design of finite-time fault detection filter (FTFDF) for the addressed networked systems with time-delays. This paper focuses on the design of an FTFDF via the DETM to ensure the finite-time stochastic stability of error dynamics system with satisfactory the prescribed $H_{\infty }$ performance. Moreover, the desired FTFDF parameter matrices are obtained by solving linear matrix inequalities. In the end, a simulation example is employed to illustrate the validity of the proposed FTFD method.     

Security control of networked systems with deception attacks and packet dropouts: A discrete-time approach

This paper is concerned with the security control problem for a class of networked systems subject to deception attacks and packet dropouts. First, by taking into account the deception attacks and packet dropouts in an unified framework, a discrete-time stochastic system is presented. In virtue of matrix exponential computation, an equivalent discrete-time stochastic model is established. Based on this, the security analysis is given by the law of total expectation and some sufficient conditions are provided. Subsequently, a controller is designed. Finally, the effectiveness of the proposed method is illustrated by two examples including a practical power grid system.     

Nonstationary quantized control for discrete-time Markov jump singularly perturbed systems against deception attacks

This paper addresses the nonstationary quantized control problem for the discrete-time Markov jump singularly perturbed systems (MJSPSs) subject to deception attacks (DAs). The control inputs are characterized by randomly occurring DAs and nonstationary quantization simultaneously, where the DAs are depicted by means of a Bernoulli distributed sequence. By applying a multi-layer structure methodology (MLSM), the nonstationary controllers are devised for MJSPSs. Meanwhile, the correlation among system mode, controller mode, and quantizer mode are portrayed via the nonstationary Markov process. Based on a mode-dependent Lyapunov functional, sufficient criteria are established such that the resulting closed-loop system (CLS) is stochastic mean square exponential ultimately bounded (SMSEUB), and the desired controller is designed. Ultimately, two simulation examples are offered to elaborate on the validity and superiority of the proposed theoretical results.     

Security control for networked control systems with randomly occurring integrity check protection subject to randomly occurring zero-value attacks

This paper investigates the problem of secure control for networked control systems (NCSs) under randomly occurring zero-value attacks (ROZVAs). Specifically, ROZVAs only offset the true signal without injecting obfuscated information or noises, and possess the minimum energy of the added malicious information. To protect system stability against ROZVA, randomly occurring integrity check protection (ROICP) is introduced which prevents malicious data injection with less energy cost than persistently occurring protection. Besides the random phenomena of ROZVA and ROICP, which are characterized by two mutually independent random variables obeying the Bernoulli distribution, the randomly occurring time delays caused by ROICP are also considered in system modelling. According to the built stochastic linear system model, security analysis of the NCS with ROICP subject to ROZVA is carried out and sufficient condition for stochastic stability is derived via a linear matrix inequality (LMI) approach. Based on the proposed condition, a compensation feedback controller is designed to facilitate system stability. Finally, simulation results show the effectiveness of the proposed method.     

Probabilistic-constrained tracking control for stochastic time-varying systems under deception attacks: A Round-Robin protocol

The probabilistic-constrained tracking control issue is investigated for a class of time-varying nonlinear stochastic systems with sensor saturation, deception attacks and limited bandwidth in an unified framework. The saturation of sensors is quantified by a sector-bound-based function satisfying certain conditions, and the random deception attacks are considered and modeled by a random indicator variable. To gain more efficient utilization of communication channels, a Round-Robin (RR) protocol is utilized to orchestrate the transmission order of measurements. The main purposes of this study aim to plan an observer-based tracking controller to achieve the following goals: (1) the related performance indicators of the estimation error is less than given bound at each time step; and (2) the violation probability of the tracking error confined in a predefined scope is supposed to be higher than a prescribed scalar and the area is minimized at each instant. In order to reach these requirements, a group of recursive linear matrix inequalities (RLMIs) are developed to estimate the state and design the tracking controller at the same time. Finally, two simulation examples are exploited to illustrate the availability and flexibility of the proposed scheme.     

Dynamic event-triggered control for nonlinear NCSs subject to DoS attacks

In this study, a dynamic event-triggered control problem is addressed for nonlinear networked control systems (NCSs) subject to denial-of-service (DoS) attacks. Assume that data from the plant to the controller is transmitted via a wireless transmission channel under malicious DoS attacks characterized by frequency and duration properties. On the premise of ensuring the stability and minimum inter-event time (MIET) of the systems, dynamic event-triggered mechanisms (DETMs) are proposed for the hybrid dynamic system to withstand a certain degree of DoS attacks. Three event-triggered schemes are designed for the most existing state-based control systems which further enlarge the inter-event times, and the stabilization conditions of hybrid dynamic system are given. Finally, illustrative examples are provided to verify the effectiveness of the presented theoretical results.     

Synchronization of second-order chaotic systems via adaptive terminal sliding mode control with input nonlinearity

In the presence of system uncertainties, external disturbances and input nonlinearity, this paper is concerned with the adaptive terminal sliding mode controller to achieve synchronization between two identical attractors which belong to a class of second-order chaotic system. The proposed controller with adaptive feedback gains can compensate nonlinear dynamics of the synchronous error system without calculating the magnitudes of them. Meanwhile, these feedback gains are updated by the novel adaptive rules without required that the bounds of system uncertainties and external disturbances have to be known in advance. Some sufficient conditions for stability are provided based on the Lyapunov theorem and numerical studies are performed to verify the effectiveness of presented scheme.     

Consensus control via iterative learning for distributed parameter models multi-agent systems with time-delay

Most of the available results of iterative learning control (ILC) are that solve the consensus problem of lumped parameter models multi-agent systems. This paper considers the consensus control problem of distributed parameter models multi-agent systems with time-delay. By using the knowledge between neighboring agents, considering time-delay problem in the multi-agent systems, a distributed P-type iterative learning control protocol is proposed. The consensus error between any two agents in the sense of L² norm can converge to zero after enough iterations based on proposed ILC law. And then we extend these conclusions to Lipschitz nonlinear case. Finally, the simulation result shows the effectiveness of the control method.     

Consensus control for multi-agent systems with distributed parameter models via iterative learning algorithm

This paper deals with the problem of iterative learning control algorithm for a class of multi-agent systems with distributed parameter models. And the considered distributed parameter models are governed by the parabolic or hyperbolic partial differential equations. Based on the framework of network topologies, a consensus-based iterative learning control protocol is proposed by using the nearest neighbor knowledge. When the iterative learning control law is applied to the systems, the consensus errors between any two agents on L² space are bounded, and furthermore, the consensus errors on L² space can converge to zero as the iteration index tends to infinity in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method.     

Resilient control co-design for cyber-Physical systems with DoS attacks via a successive convex optimization approach

This paper addresses the issue of resilient control in the presence of denial-of-service (DoS) attacks for a class of cyber-physical systems. The primary objective is to design a static output feedback controller and event-triggered condition simultaneously such that the globally exponential stability of the closed-loop system is ensured. Compared with stepwise techniques, the co-design achieves the trade-off between control performance and communication cost. The control co-design process is formulated as a bilinear matrix inequality (BMI) problem, which involves nonlinear terms. A successive convex optimization approach is proposed to solve the BMI problem. Further, we develop a self-triggered communication scheme to reduce the cost caused by continuous event detection. It is shown that the proposed event/self-triggered strategy is Zeno-free and excludes singular triggering. Finally, a numerical example is presented to demonstrate the validity of the proposed method.     

Cluster synchronization in community networks with nonidentical nodes via edge-based adaptive pinning control

This paper investigates cluster synchronization in community networks with nonidentical nodes. Several effective strategies to enhance the coupling weights are designed. For the first time, adaptive enhancing factor method combined with edge-based pinning control is adopted to achieve synchronization. Furthermore, distributed adaptive pinning control scheme is adopted based on the local information of node dynamics. Noticeably, only the coupling weights of spanning trees in each community are tuned, which are low-cost and more practicable. Based on Lyapunov stability theory, some sufficient conditions for cluster synchronization are derived. Numerical simulations are provided to verify the effectiveness of the theoretical results.     

Consensus of networked multi-agent systems via the networked predictive control and relative outputs

This paper investigates the consensus problem of discrete-time networked multi-agent systems (DNMASs) with a directed topology and communication delay, where exact state and output of each agent are not measured, and yet output differences between agent and its neighboring ones (relative outputs for short) are available. Based on the networked predictive control scheme and relative output data, a novel protocol is proposed to overcome the effect of delay on the consensus actively. Moreover, for the DNMASs with a fixed topology and constant communication delay, delay-independent necessary and/or sufficient conditions of achieving consensus are obtained, which reveal that the essence of dominating the consensus is agents' dynamics and communication topology. Simulation results further demonstrate the effectiveness of theoretical results.     

Adaptive neural finite-time control of nonlinear systems subject to sensor hysteresis

In this paper, an adaptive neural control scheme is proposed for a class of unknown nonlinear systems with unknown sensor hysteresis. The radial basis function neural networks are employed to approximate the unknown nonlinearities and the backstepping technique is implemented to construct controllers. The difficulty of the control design lies in that the genuine states of the system are not available for feedback, which is caused by sensor hysteresis. The proposed control scheme eventually ensures the practical finite-time stability of the closed-loop system, which is proved by the Lyapunov theory. A numerical simulation example is included to verify the effectiveness of the developed approach.