Event‐triggered control of Takagi‐Sugeno fuzzy systems under deception attacks

Peixoto, Márcia L. C.; Coutinho, Pedro Henrique Silva; Bessa, Iury; Pessim, Paulo S.P.; Palhares, Reinaldo M.

doi:10.1002/rnc.6760

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…Nonlinearity exists throughout various practical systems and can affect the dynamic characteristics of systems, and many related studies have been reported. [1][2][3][4][5][6] The existence of nonlinearity inevitably brings great obstacles to system analysis and synthesis, because accurate mathematical models of nonlinear systems are often hard to establish, and traditional linear system methods usually have difficulty managing them. Takagi-Sugeno (T-S) fuzzy model, 7 due to its strong capability of approximating nonlinearity, has been recognized as an efficient technique to resolve this problem.…”

Section: Introductionmentioning

confidence: 99%

Improved dissipativity‐based analysis and synthesis of T‐S fuzzy systems with aperiodic memory sampled‐data controllers via an augmented looped‐functional

Liu,

Zhang,

Fan

et al. 2024

Intl J Robust & Nonlinear

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The dissipativity‐based analysis and synthesis issues of Takagi‐Sugeno (T‐S) fuzzy systems by applying aperiodic memory sampling control are investigated in this paper. Compared with the more conservative dissipative conditions and controller design methods proposed in the past based on Lyapunov theory, the main purpose of this paper is to obtain a less conservative dissipative criterion and devise an aperiodic memory sampled‐data controller. Firstly, the sampling controller is assumed to work in aperiodic sample mode, and the time delay in the communication network is considered. Next, an augmented delay‐product‐type looped‐functional is proposed such that the whole information of sampling and delay, which has not been studied before, is well taken into account. A new dissipative criterion and an aperiodic sampled‐data control strategy are proposed by applying free‐matrix‐based integral inequalities consequently. Finally, a truck‐trailer system is implemented to elaborate the availability and the merit of the designed strategy.

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Section: Introductionmentioning

confidence: 99%

Improved dissipativity‐based analysis and synthesis of T‐S fuzzy systems with aperiodic memory sampled‐data controllers via an augmented looped‐functional

Liu,

Zhang,

Fan

et al. 2024

Intl J Robust & Nonlinear

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“…It is widely acknowledged in the literature that LMI is a essential tool for addressing control problems in various fields. For example, it plays a significant role in event-triggered control, 17 gain-scheduled control for linear parameter-varying systems, 18 data-driven control, 19 and fault-tolerant control, 20 to name a few.…”

Section: Introductionmentioning

confidence: 99%

ℋ_∞ robust control of discrete‐time systems based on new linear matrix inequality formulations and evolutionary optimization

Gonçalves,

Oliveira,

da Silva Júnior

2023

Optim Control Appl Methods

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This study presents novel formulations for robust state‐feedback control synthesis for discrete‐time linear time‐invariant systems based on linear matrix inequalities. The proposed formulations require searching for two adjustment matrices. The synthesis formulations include other formulations from the literature as particular cases according to specific values of the adjustment matrices. We propose the application of evolutionary optimization to determine the optimal values of these two adjustment matrices. One approach to obtaining a single‐step formulation for static output‐feedback control synthesis is to transform a state‐feedback control synthesis formulation via the simple change of variables. The alteration of variables considered in this study requires an adjustment matrix. The adjustment matrix influences the performance of the resulting controller or the existence of a feasible solution to the problem. Here, we also propose the application of evolutionary optimization to determine the optimal value of this adjustment matrix and the two adjustment matrices of the proposed formulations to obtain the optimal robust control system. Case studies verify that despite the increased complexity, the proposed formulations and the method required to tune them may be indispensable in achieving a robustly stable control system or an enhanced performance for more intricate problems.

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A Novel Dual‐Loop Model‐Free Adaptive Iterative Learning Control and Its Application to the Refrigeration Systems

Ibrahim,

Dong

2024

Intl J Robust & Nonlinear

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This study investigates a novel dual‐input and dual‐output Model‐Free Adaptive Iterative Learning Control (A‐MFAILC) approach for energy‐saving control of refrigeration systems, aiming to maintain a minimum stable superheat and a constant evaporation temperature. Superheat control is often unstable due to the complex and high‐order nature of refrigeration systems. Furthermore, these systems often face large time delays, which complicate the tracking control process. Such delays can cause inefficiencies and instability in maintaining desired operational parameters, making it challenging to achieve energy savings. To get around these problems, a novel Model‐Free Adaptive Iterative Learning Control algorithm has been proposed by incorporating input rate constraints for time‐delayed systems.The proposed A‐MFAILC algorithm with a single input and single output has been extended to dual input and dual output energy‐saving control of refrigeration systems. Complete proofs of convergence analysis have been provided, and the algorithm's performance has been fully evaluated. Simulation tests based on the proposed A‐MFAILC algorithm, developed for dual‐loop control systems, have been conducted on refrigeration systems. Step signals have been used as input signals for comprehensive performance testing. As a result, the proposed approach demonstrates higher tracking stability and fast response speed, with an average tracking accuracy of 98.68% and 93.87% for superheat and evaporation temperature, respectively.

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Event‐triggered control of Takagi‐Sugeno fuzzy systems under deception attacks

Cited by 11 publications

References 38 publications

Improved dissipativity‐based analysis and synthesis of T‐S fuzzy systems with aperiodic memory sampled‐data controllers via an augmented looped‐functional

Improved dissipativity‐based analysis and synthesis of T‐S fuzzy systems with aperiodic memory sampled‐data controllers via an augmented looped‐functional

ℋ_∞ robust control of discrete‐time systems based on new linear matrix inequality formulations and evolutionary optimization

A Novel Dual‐Loop Model‐Free Adaptive Iterative Learning Control and Its Application to the Refrigeration Systems

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Event‐triggered control of Takagi‐Sugeno fuzzy systems under deception attacks

Cited by 11 publications

References 38 publications

Improved dissipativity‐based analysis and synthesis of T‐S fuzzy systems with aperiodic memory sampled‐data controllers via an augmented looped‐functional

Improved dissipativity‐based analysis and synthesis of T‐S fuzzy systems with aperiodic memory sampled‐data controllers via an augmented looped‐functional

ℋ∞ robust control of discrete‐time systems based on new linear matrix inequality formulations and evolutionary optimization

A Novel Dual‐Loop Model‐Free Adaptive Iterative Learning Control and Its Application to the Refrigeration Systems

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ℋ_∞ robust control of discrete‐time systems based on new linear matrix inequality formulations and evolutionary optimization