Security control of interval type-2 fuzzy system with two-terminal deception attacks under premise mismatch
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ORIGINAL PAPER
Security control of interval type-2 fuzzy system with two-terminal deception attacks under premise mismatch Tao Zhao · Kunpeng Zhang · Songyi Dian
Received: 31 May 2020 / Accepted: 31 August 2020 © Springer Nature B.V. 2020
Abstract This paper focuses on security control of nonlinear networked control systems with parameter uncertainties. Interval type-2 T–S fuzzy model is used to represent the original nonlinear system, so as to capture the parameter uncertainties. Stochastic deception attacks are considered in the sensor to controller channel and the controller to actuator channel. By constructing Lyapunov–Krasovskii functional with the information of time-varying delays in the deception attacks, new stabilization conditions are derived. Furthermore, considering the information of upper and lower bounds of membership functions, less conservative membership-function-dependent conditions are developed in terms of linear matrix inequality. Finally, simulation results show that the proposed method is effective under the influence of deception attacks and parameter uncertainties. Keywords Deception attacks · Security control · Type-2 fuzzy · Premise mismatch 1 Introduction With the promotion of automation technology, industry is developing toward the direction of digitalization, intelligence, networking and comprehensive integraT. Zhao · K. Zhang · S. Dian (B) College of Electrical Engineering, Sichuan University, Chengdu 610065, China e-mail: [email protected]
tion [1]. With the communication network embedded in the control system as a part of the system, it has greatly enriched the industrial control technology and means. The control system has changed greatly in the aspects of architecture, control method and man-machine cooperation method, and also brought some new problems, such as the coupling of control and communication, distributed control method, etc. [2]. With the emergence of these new problems, the control methods and algorithms of automatic control theory in the network environment need to be continuously expanded and innovated [3]. Networked control system (NCS) has the characteristics of strong reliability, low cost, easy maintenance, easy to expand, so it has been widely used in the field of industrial control. However, the introduction of network also brings some problems, such as delay and packet loss, which leads to system performance degradation and even instability [4]. At present, a lot of valuable results have been obtained in stability analysis, control synthesis, robust filtering and fault diagnosis of NCS [5–9]. However, how to deal with the nonlinearity, uncertainty and security of networked control system is still a challenge. T–S fuzzy model can be regarded as approximate piecewise linear model. The model is equivalent to dividing the input space into several fuzzy subspaces. A local linear model is established in each fuzzy subspace, and then the membership function is used to connect the local models smoothly, thus forming a global fuzzy model of nonlinear function
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