Decentralized sampled-data <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si43.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> fuzzy filtering with exponential time-varying gains for nonlinear interconnected systems

Jang, Yong Hoon; Kim, Han Sol; Kim, Euntai; Joo, Young Hoon

doi:10.1016/j.ins.2022.07.078

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…Since this study focused on decentralized control considering the communication delay of a quadrotor UAV, the effect of disturbance was excluded. There are studies that attenuated the effects of disturbances on the system in previous quadrotor control studies, which can be easily solved by using the H ∞ control [40,41].…”

Section: T-s Fuzzy Model-based Tracking Error Dynamicsmentioning

confidence: 99%

Decentralized Sampled-Data Fuzzy Tracking Control for a Quadrotor UAV with Communication Delay

Jang

Han

Kim

2022

Drones

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This study deals with the decentralized sampled-data fuzzy tracking control of a quadrotor unmanned aerial vehicle (UAV) considering the communication delay of the feedback signal. A decentralized Takagi–Sugeno (T–S) fuzzy approach is adopted to represent the quadrotor UAV as two subsystems: the position control system and the attitude control system. Unlike most previous studies, a novel decentralized controller considering the communication delay for the position control system is proposed. In addition, to minimize the increase in computational complexity, the Lyapunov–Krasovskii functional (LKF) is configured as the only state required for each subsystem. The design conditions guaranteeing the tracking performance of the quadrotor UAV are derived as linear matrix inequalities (LMIs) that are numerically solved. Lastly, the validity of the proposed design method is verified by comparing the results through simulation examples with and without communication delay.

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Section: T-s Fuzzy Model-based Tracking Error Dynamicsmentioning

confidence: 99%

Decentralized Sampled-Data Fuzzy Tracking Control for a Quadrotor UAV with Communication Delay

Jang

Han

Kim

2022

Drones

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“…However, in the fault estimation case, since the purpose is to optimize the fault estimation performance, it is hard to optimize the maximum interconnection bound α k . Thus, in this paper, we assume that the maximum interconnection bound α k is a given scalar, which is often assumed in the decentralized filtering techniques [36], [40]. Using center-average defuzzification, product inference and singleton fuzzifier, IT-THEN rule (1) can be inferred as the following fuzzy subsystem:…”

Section: Remarkmentioning

confidence: 99%

Decentralized Fuzzy Observer-Based Fault Estimation for Nonlinear Large-Scale Systems

Koo

2024

IEEE Access

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This paper presents a fault estimation technique based on the decentralized fuzzy observer for nonlinear large-scale systems, which are considered to be consisted of fuzzy subsystems and uncertain interconnections. Based on a Takagi-Sugeno fuzzy model for the subsystems of the large-scale system and the decentralized fuzzy observer, the H ∞ performance of the fault estimation problem is established by using the estimation error model. By using H ∞ performance inequalities to address the fault estimation problem, the decentralized fuzzy observer design techniques are proposed to guarantee the fault estimation conditions. Also, sufficient conditions of observer design are converted into the linear matrix inequality formats. Finally, an example is provided to verify the effectiveness of the proposed decentralized fuzzy observer design techniques for fault estimation.

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“…In [41], the fuzzy fault estimation has been addressed for nonlinear interconnected systems, but it still assumes that the all interconnections are known. To solve the unknown or uncertain interconnection problem, some techniques such as fuzzy observer [42] and fuzzy filter [43][44][45] have been presented for nonlinear interconnected systems with uncertain interconnection, but fault estimation issues have not been considered. To the best of the author's knowledge, the fault estimation technique has not been studied for discrete-time nonlinear interconnected systems with uncertain interconnections so far.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Fuzzy Fault Estimation Observer Design for Discrete-Time Nonlinear Interconnected Systems

Koo

2024

Electronics

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In this paper, a fault estimation technique is proposed for discrete-time nonlinear interconnected systems with uncertain interconnections. To achieve the fault estimation, the decentralized fuzzy observer is adopted based on the Takagi–Sugeno fuzzy model. Based on the estimation error model with the subsystems of the interconnected system and its decentralized fuzzy observer, the fault estimation condition with H∞ performance is presented. The main idea of this paper is that a novel inequality condition for H∞ performance is used, and the sufficient condition is presented to guarantee the good fault estimation performance. Also, the decentralized fuzzy observer design condition for the fault estimation is converted into linear matrix inequality formats. Finally, a simulation example is provided, and the effectiveness of the proposed fault estimation technique is verified by comparison of the fault estimation performance.

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Decentralized sampled-data H∞ fuzzy filtering with exponential time-varying gains for nonlinear interconnected systems

Cited by 11 publications

References 46 publications

Decentralized Sampled-Data Fuzzy Tracking Control for a Quadrotor UAV with Communication Delay

Decentralized Sampled-Data Fuzzy Tracking Control for a Quadrotor UAV with Communication Delay

Decentralized Fuzzy Observer-Based Fault Estimation for Nonlinear Large-Scale Systems

Decentralized Fuzzy Fault Estimation Observer Design for Discrete-Time Nonlinear Interconnected Systems

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