Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Yu, Ziquan; Zhang, Youmin; Liu, Zhixiang; Qu, Yaohong; Su, Chun‐Yi

doi:10.1049/iet-cta.2018.6262

Cited by 61 publications

(52 citation statements)

References 57 publications

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“…where ρ = D − H M − O M > 0. Similar to [35], the negative semidefinite V˙2 implies that all signals in (31) are bounded such that the closed-loop stability can be achieved. This completes the proof.…”

Section: Sliding Mode Controller Using Mcfnn Approximatormentioning

confidence: 99%

“…Assumption There exists optimal weight

W^{*}

, centre

m^{*}

, width

σ^{*}

and feedback gain

ω_{r}^{*}

for estimating uncertain term

f ()(, i_{c})

which has such definition as

f ()(, i_{c}) bold= W^{*} l^{*} bold+ ε

, where

l^{*} bold= l^{*} ()(, x, m^{*} bold, σ^{*}, ω_{r}^{*})

ε

is the mapping error which is uniformly bounded as:

(||, ε) \leq ε_{b}

, where

ε_{b}

is a positive arbitrary small constant [35–37]. Theorem : Consider single‐phase APF with lumped uncertainties represented by (1), if the MCFNN control law is taken the form as (21) and the adaptive laws for MCFNN parameters are formulated as (23)–(26), then the closed‐loop stability of the proposed MCFNN control strategy can be assured.…”

Section: Proposed Fnnsmentioning

confidence: 99%

See 1 more Smart Citation

Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

Hou

Chu

Fei

2020

IET Control Theory & Appl

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This study mainly focuses on the development of a second-order sliding mode-based meta-cognitive fuzzy neural network (MCFNN) control for power quality improvement, whereby an MCFNN is employed to learn modelling uncertainties. First, a second-order sliding mode control (SOSMC) is designed to track reference current for active power filter. Then, an MCFNN system with accurate approximation capability is further investigated to learn the unknown parts in SOSMC. Different from the existing predefined structure approaches, only necessary data can be extracted to adjust the structure and parameters of the networks in MCFNN. Subsequently, the Lyapunov stability analysis is presented to guarantee tracking performance and stability of the closed-loop system. Moreover, the excellent performance of the proposed MCFNN scheme is verified by simulation and experimental studies, and its remarkable characteristics are exhibited in comparison with other intelligent control schemes.

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Section: Sliding Mode Controller Using Mcfnn Approximatormentioning

confidence: 99%

“…Assumption There exists optimal weight

W^{*}

, centre

m^{*}

, width

σ^{*}

and feedback gain

ω_{r}^{*}

for estimating uncertain term

f ()(, i_{c})

which has such definition as

f ()(, i_{c}) bold= W^{*} l^{*} bold+ ε

, where

l^{*} bold= l^{*} ()(, x, m^{*} bold, σ^{*}, ω_{r}^{*})

ε

is the mapping error which is uniformly bounded as:

(||, ε) \leq ε_{b}

, where

ε_{b}

Section: Proposed Fnnsmentioning

confidence: 99%

Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

Hou

Chu

Fei

2020

IET Control Theory & Appl

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“…Distributed networks have been applied in wide areas such as WSN applications and fault-tolerant cooperative controls for unmanned aerial vehicle systems (Yu et al, 2019a(Yu et al, , 2019b(Yu et al, , 2020. Specifically, distributed estimation algorithms in WSNs are focused on network systems with interference.…”

Section: Introductionmentioning

confidence: 99%

Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

Wang

Sun

et al. 2021

Front Inform Technol Electron Eng

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This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm for use over a wireless sensor network (WSN). This algorithm is based on information freshness, which is calculated as the age of information (AoI) of the sampled data. The proposed algorithm integrates the traditional event-triggered mechanism, information freshness calculation method, and Kalman consensus filter (KCF) algorithm to estimate the concentrations of pollutants in the aircraft more efficiently. The proposed method also considers the influence of data packet loss and the aircraft's loss of communication path over the WSN, and presents an AoI-freshness-based threshold selection method for the ET-KCF algorithm, which compares the packet AoI to the minimum average AoI of the system. This method can obviously reduce the energy consumption because the transmission of expired information is reduced. Finally, the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory. Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.

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“…In the last century, fractional-order calculus has been widely used in many electrical systems, robot systems, engineering systems, and thermodynamic systems, which has attracted extensive attention [1][2][3][4][5]. e fractional-order system (FOS) has more advantages than classical integer-order system [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

T-S Fuzzy Control of Uncertain Fractional-Order Systems with Time Delay

Hao

Zhang

2021

Journal of Mathematics

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In this article, the adaptive control of uncertain fractional-order time-delay systems (FOTDSs) with external disturbances is discussed. A Takagi-Sugenu (T-S) fuzzy model with if-then rules is adopted to characterize the dynamic equation of the FOTDS. Besides, a fuzzy adaptive method is proposed to stabilize the model. By utilizing the Lyapunov functions, a robust controller is constructed to stabilize the FOTDS. Due to the uncertainty of system parameters, some fractional-order adaptation laws are designed to update these parameters. At the same time, some if-then rules with linear structure based on the fuzzy T-S adaption concept are established. The designed method not only guarantees that the state of closed-loop system asymptotically converges to origin but also keeps the signal in the FOTDS bounded. Finally, the applicability of the control method is proved by simulation examples.

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Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Cited by 61 publications

References 57 publications

Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification

Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

T-S Fuzzy Control of Uncertain Fractional-Order Systems with Time Delay

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