Active Defense-Based Resilient Sliding Mode Control Under Denial-of-Service Attacks

This article investigates the sampled-data containment control issue for nonlinear multiagent systems (MASs) with packet losses in the framework of Takagi-Sugeno fuzzy model. In the process of designing fuzzy sampled-data containment controller, a crucial factor, that is, asynchronous premise variables, is tackled via the proposed novel asynchronous premise rebuilding approach. Based on the fact that there may be packet losses in communication networks, the fuzzy sampled-data MAS is concerned as a switched version with two subsystems. Then, in the light of average dwell-time method and Lyapunov stability theory, sufficient conditions of designing fuzzy sampled-data containment controller are derived such that the states containment can be achieved for considered systems in spite of packet losses. Finally, both a numerical simulation and a Caltech multivehicle wireless test bed simulation are presented to confirm the effectiveness of the proposed control scheme.

Section: Proof It Is Effortless To Derive Thatmentioning

confidence: 99%

“…▪ Remark 5. Conditions (48)(49)(50)(51)(52)(53) of Theorem 3 are sufficient criteria for designing containment controller in the form of LMIs. Notice that when we solve these LMIs of Theorem 3 by MATLAB, the computational complexity will not increase with the raise of the number of followers.…”

Section: Proof It Is Effortless To Derive Thatmentioning

confidence: 99%

Sampled‐data containment control for Takagi‐Sugeno fuzzy multiagent systems with packet losses

Zhang

Dong

2020

“…The DoS switch sequences represent a transition from zero corresponding to attack occurrence, to one corresponding to no attacks. Vivid examples with reference to this process involving the active and silent subintervals of DoS attacks were properly depicted in ( Figure 2 in the work of De Persis and Tesi 7 and Figure 2 in the work of Wu et al 25 ). In that case, for the time interval [1,19), we can see n (1,19) = 3 while the number of total switchings is 5, which in turn verifies that the number of DoS attacks is not equal to the total switching times obviously.…”

Section: Assumptionmentioning

confidence: 99%

Resilient filtering for cyber‐physical systems under denial‐of‐service attacks

Shi

Wang

et al. 2019

Self Cite

Summary Owing to the deep integration of control, computation, and communication, cyber‐physical systems (CPSs) play an important role in wide real‐world applications. In this paper, we investigate the problem of resilient filter design of CPSs under malicious denial‐of‐service (DoS) attacks launched by adversaries. Firstly, based on two standard assumptions concerning with the frequency and duration of DoS attacks, we state the H∞ filtering problem for the CPSs under DoS attacks. Then, sufficient conditions are developed to ensure that, when there are DoS attacks, the filtering error dynamics of the underlying CPSs is mean square exponential stable with a prescribed H∞ disturbance attenuation performance. Furthermore, a switched filter is designed for CPSs under DoS attacks. Examples are given to illustrate the effectiveness and potential of the proposed new design techniques.

“…On account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, sliding mode control, robust control, fault tolerant control, and so on. In addition, backstepping‐based adaptive neural control and fuzzy control for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

Pan

Chadli

et al. 2020

This paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict-feedback form.Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict-feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised.Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme. K E Y W O R D Sadaptive fuzzy control, asymptotic tracking control, nonstrict-feedback system, state constraints, unknown control direction INTRODUCTIONOn account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, 1-3 sliding mode control, 4-6 robust control, 7-9 fault tolerant control, 10,11 and so on. In addition, backstepping-based adaptive neural control 12-18 and fuzzy control [19][20][21][22][23][24][25][26][27][28][29][30] for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique. Cite several examples, based on FLS's online approximation capability, a new adaptive fuzzy control scheme was presented in Reference 20. Tong et al citebib21 established a fuzzy state observer and proposed a decentralized adaptive fuzzy optimal control approach for nonlinear large-scale systems. In Reference 22, the fuzzy adaptive output feedback controller was constructed for strict-feedback nonlinear systems, where the problem of virtual control coefficients was well handled by using a convex combination technique. Int J Robust Nonlinear Control. 2020;30:3365-3381. wileyonlinelibrary.com/journal/rnc