Security-Based Fuzzy Control for Nonlinear Networked Control Systems With DoS Attacks via a Resilient Event-Triggered Scheme

Pan, Yingnan; Wu, Yanmin; Lam, Hak‐Keung

doi:10.1109/tfuzz.2022.3148875

Cited by 275 publications

(115 citation statements)

References 42 publications

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“…

M

is the number of fuzzy rules,

{\psi}_n^c

and

{\overline{\omega}}^c

are fuzzy sets. According to References 43‐45, the FLSs can be written as

y(x)=\frac{\sum \limits_{c=1}^M{\nu}_c{\Pi}_{i=1}^n{\eta}_{\psi_i^c}\left({x}_i\right)}{\sum \limits_{c=1}^M\left(\prod \limits_{i=1}^n{\eta}_{\psi_i^c}\left({x}_i\right)\right)},

where

x={\left[{x}_1,\dots, {x}_n\right]}^T

{\nu}_c={\max}_{y\in R}{\eta}_{{\overline{\omega}}^c}(y)

{\eta}_{\psi_i^c}\left({x}_i\right)

...…”

Section: The Formulation Of the Problemmentioning

confidence: 99%

Fixed‐time adaptive anti‐disturbance and fault‐tolerant control for multi‐agent systems

Liang

Xue

2022

Intl J Robust & Nonlinear

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This article investigates the fixed‐time adaptive anti‐disturbance and fault‐tolerant control problem for a class of nonlinear multi‐agent systems with non‐affine nonlinear faults. A simplified backstepping approach is presented that only requires designing one Lyapunov function for high‐order multi‐agent systems, which reduces the computational burden. By designing a new compound observer, an anti‐disturbance and fault‐tolerant control strategy is developed to estimate and compensate the compound nonlinear terms composed of external disturbances and nonlinear faults. In addition, an auxiliary control variable is designed to solve the problem of “explosion of complexity” and ensure the system stability in a fixed time. Based on the Lyapunov stability method, it is proved that the nonlinear multi‐agent systems are semi‐global practical fixed‐time stable. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

show abstract

“…

M

is the number of fuzzy rules,

{\psi}_n^c

and

{\overline{\omega}}^c

are fuzzy sets. According to References 43‐45, the FLSs can be written as

y(x)=\frac{\sum \limits_{c=1}^M{\nu}_c{\Pi}_{i=1}^n{\eta}_{\psi_i^c}\left({x}_i\right)}{\sum \limits_{c=1}^M\left(\prod \limits_{i=1}^n{\eta}_{\psi_i^c}\left({x}_i\right)\right)},

where

x={\left[{x}_1,\dots, {x}_n\right]}^T

{\nu}_c={\max}_{y\in R}{\eta}_{{\overline{\omega}}^c}(y)

{\eta}_{\psi_i^c}\left({x}_i\right)

...…”

Section: The Formulation Of the Problemmentioning

confidence: 99%

Fixed‐time adaptive anti‐disturbance and fault‐tolerant control for multi‐agent systems

Liang

Xue

2022

Intl J Robust & Nonlinear

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show abstract

“…In the seminal works [27] and [28], the consensus control problem for fault-estimation-in-the-loop multiagent systems and the distributed filtering under the Cauchykernel-based maximum correntropy have been investigated, respectively. On the other hand, the problem of optimizing L 2 −L ∞ /H ∞ mixed performance and the problem of resilient event-triggered control for nonlinear networked control systems have been deeply researched in [29] and [30], respectively. How to combine the framework of this paper with these important results remains an interesting open question.…”

Section: Stability Analysismentioning

confidence: 99%

Event-Triggered Consensus Control for Nonlinear Multiagent Systems With Unmeasured State Variables

Yang

2022

IEEE Access

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This article is concerned with event-triggered consensus control for nonlinear multi-agent systems with unknown nonlinear functions, unmatched disturbances, and unmeasured state variables. A novel distributed control algorithm is designed for high-order nonlinear multi-agent systems by using inputdriven filters and defining dynamic feedback systems. In addition, the salient features of the proposed control algorithm are highlighted as follows: 1) our proposed control algorithm employs only local information from itself and its neighboring agents, 2) only a binary signal is transmitted to the actuator during the control process, and 3) the proposed control scheme avoids the use of approximate structures and lacks complex calculations. Finally, the simulation of the inverted pendulum verifies the validity of our theoretical findings.INDEX TERMS Multi-agent systems, output feedback, event-triggered, binary signals.

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“…Through the MFD approach, the specific information of the membership functions can be included into the stability conditions. The related work was reported to further relax the stability conditions and endow the fuzzy controller more flexibility in control synthesis [13], [18], [21]- [23], [30]- [42]. In addition, interesting applications of type-3 fuzzy systems can be found in [43], [44].…”

Section: Introductionmentioning

confidence: 99%

Optimization for Interval Type-2 Polynomial Fuzzy Systems: A Deep Reinforcement Learning Approach

Xiao

Lam

Xuan

et al. 2023

IEEE Trans. Artif. Intell.

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It is known that the interval type-2 (IT2) fuzzy controllers are superior compared to their type-1 counterparts in terms of robustness, flexibility, etc. However, how to conduct the type reduction optimally with the consideration of system stability under the fuzzy-model-based (FMB) control framework is still an open problem. To address this issue, we present a new approach through the membership-function-dependent (MFD) and deep reinforcement learning (DRL) approaches. In the proposed approach, the reduction of IT2 membership functions of the fuzzy controller is completing during optimizing the control performance. Another fundamental issue is that the stability conditions must hold subject to different type-reduction methods. It is tedious and impractical to resolve the stability conditions according to different type-reduction methods, which could lead to infinite possibility. It is more practical to guarantee the holding of stability conditions during type-reduction rather than resolving the stability conditions, the MFD approach is proposed with the imperfect premise matching (IPM) concept. Thanks to the unique merit of the MFD approach, the stability conditions according to all the different embedded type-1 membership functions within the footprint of uncertainty (FOU) are guaranteed to be valid. During the control processes, the state transitions associated with properly engineered cost/reward function can be used to approximately calculate the deterministic policy gradient to optimize the acting policy and then to improve the control performance through determining the grade of IT2 membership functions of the fuzzy controller. The detailed simulation example is provided to verify the merits of the proposed approach.Impact Statement-The connection between the membership functions of type-2 fuzzy systems and reinforcement learning is observed and investigated for the first time. In the paper, the authors present the reinforcement-learning-based type-reduction for the interval type-2 fuzzy-model-based control systems. The theoretical guarantee of the stability conditions holds during the optimization process conducted by the reinforcement learning agent. The proposed research work bridges the areas of fuzzy control with reinforcement learning. Adopting reinforcement learning techniques to improve the control performance of fuzzy systems with the theoretically guaranteed stability conditions, which has impact on both artificial intelligence and control communities.

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Security-Based Fuzzy Control for Nonlinear Networked Control Systems With DoS Attacks via a Resilient Event-Triggered Scheme

Cited by 275 publications

References 42 publications

Fixed‐time adaptive anti‐disturbance and fault‐tolerant control for multi‐agent systems

Fixed‐time adaptive anti‐disturbance and fault‐tolerant control for multi‐agent systems

Event-Triggered Consensus Control for Nonlinear Multiagent Systems With Unmeasured State Variables

Optimization for Interval Type-2 Polynomial Fuzzy Systems: A Deep Reinforcement Learning Approach

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