Fault tolerant multivehicle formation control framework with applications in multiquadrotor systems

Jun, Shi; Zhou, Donghua; Yang, Yuhao; Sun, Jun

doi:10.1007/s11432-018-9486-3

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Fortunately, the emergence of fault diagnosis technology has opened up a new way to improve the reliability of complex systems (Cheng, Yang, & Jiang, 2016;Fan & Wang, 2016), and the past several decades have witnessed a rapid development of fault diagnosis technology and much literature has been published (Chiang, Russell, & Braatz, 2001;Shi, He, Wang, & Zhou, 2014;To, Paul, & Liu, 2018;Vachtsevanos, Georgoulas, & Nikolakopoulos, 2016;Yin, Zhu, & Kaynak, 2015;Zhou, Qin, He, Yan, & Deng, 2017). Fault diagnosis technology has been used in aviation (Lu, Wang, & Wang, 2017), aerospace (Wang, Liu, Qing, Liu, & He, 2017), robots (Zhou, Qian, Ma, & Dai, 2017), multi-vehicle (Shi, Zhou, Yang, & Sun, 2018) and numerous industrial processes and tremendous economic benefits have been achieved.…”

Section: Introductionmentioning

confidence: 99%

Fault estimation for complex networks with model uncertainty and stochastic communication protocol

Zhang

Liu

2019

Systems Science & Control Engineering

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This paper aims to investigate the fault estimation problem for a class of complex networks with model uncertainty and stochastic communication protocol. The model uncertainty existing in the system is norm-bounded. Stochastic communication protocol is employed to cope with possible data collisions in the multiple signal transmissions. An augmented system is constructed by forming an augmented state vector consisting of system states and related faults. By designing the state estimator, the state estimation problem, in the presence of model uncertainty and random disturbance is solved. The parameters of the estimator are obtained by solving several recursive matrix equations such that an upper bound of the estimation error covariance is established and it is minimized. Finally, we give a simulation example to verify the feasibility of the proposed state estimation scheme.

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Section: Introductionmentioning

confidence: 99%

Fault estimation for complex networks with model uncertainty and stochastic communication protocol

Zhang

Liu

2019

Systems Science & Control Engineering

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show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In recent decades, process monitoring becomes an increasingly significant and valuable research topic due to the high requirements of safety and reliability in industrial applications. − Specularly, data-driven process monitoring has attracted worldwide attention and has acquired remarkable accomplishments. − The key contribution of data-driven techniques is to take advantage of sensing variables to detect faults, which makes them applicable in realistic industrial systems. − In modern society, industrial systems such as power plants and high-speed rail become more complex, and produce massive data even in just 1 h. Hence, how to extract valuable information from available data becomes the most critical issue at present.…”

Section: Introductionmentioning

confidence: 99%

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Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

Zhang

Chen

et al. 2019

Ind. Eng. Chem. Res.

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By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection T 2 OLPP and SPE OLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.

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“…In process monitoring in chemical engineering industries, predicting the RUL of core equipment has become important in avoiding accidents and economic losses. This is because predictive information, including expectation and probability density function (PDF) of RUL, can help engineers evaluate the operation safety with monitored data and create reasonable maintenance strategies such as the replacement of aging components, in addition to advanced fault diagnosis and fault tolerant control technologies . Therefore, RUL predictions of complicated degradation processes deserve careful attention from the reliability community.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for fractional degradation processes under varying modes

Zhou

Chen

2019

Can J Chem Eng

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Degradation processes of practical chemical engineering systems are difficult to model accurately because of complicated nonlinearities, non-stationarities, and non-Markovian properties. Traditional prognostics techniques tend to neglect the multi-mode switching issue, and therefore cannot be used for predicting the remaining useful life (RUL) of a piece of long-life equipment, especially when it is continuously operated under varying modes. Specifically, the stationarity of differential data also plays an important role in depicting the evolutionary trend, and further affects the extrapolation procedure for RUL prediction. In this paper, we construct a multi-mode degradation model with regard to fractional diffusion processes by considering both stationary and non-stationary increments. The drift term is represented as a time-related nonlinear function, while the diffusion term is driven by fractional Brownian motion (FBM) or sub-fractional Brownian motion (sub-FBM), according to the results of the stationary test. Based on the monitored data, the model is identified by combining change-point detection and parameter estimation. The closed-form distribution of RUL is then derived through a weak convergence transformation. A numerical example and a case study of a large blast furnace are provided to evaluate the performance of the proposed prognostics framework. K E Y W O R D S degradation, multi-mode, non-Markovian, remaining useful life, stationary test

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Fault tolerant multivehicle formation control framework with applications in multiquadrotor systems

Cited by 10 publications

References 8 publications

Fault estimation for complex networks with model uncertainty and stochastic communication protocol

Fault estimation for complex networks with model uncertainty and stochastic communication protocol

Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

Remaining useful life prediction for fractional degradation processes under varying modes

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