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SPECIAL ISSUE ON ‘SMC based observation, identification, uncertainties compensation and fault detection’

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This article is published in Asian Journal of Control.The article was published on 2019-01-01. It has received 139 citations till now. The article focuses on the topics: Fault detection and isolation & Compensation (engineering).

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Bifurcation Properties for Fractional Order Delayed BAM Neural Networks

TL;DR: In this article, the stability and Hopf bifurcation of fractional order BAM neural networks with or without leakage delay were investigated. And the authors derived the stability condition and the sufficient criterion of existence of Hopf-bifurcation for fractional-order BAM networks with delay (leakage delay).
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Partially-coupled gradient-based iterative algorithms for multivariable output-error-like systems with autoregressive moving average noises

TL;DR: The parameter estimation problem of multivariable output-error-like systems with autoregressive moving average noises is investigated, and a subsystem generalised extended gradient-based iterative algorithm is presented according to the decomposition technique.
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Adaptive fuzzy global sliding mode control for trajectory tracking of quadrotor UAVs

TL;DR: An adaptive fuzzy-based global sliding mode control strategy is proposed for quadrotor unmanned aerial vehicles in robust trajectory tracking against parameter uncertainties and external disturbances, and the reaching phase and the control chattering are eliminated in the control scheme.
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Path Following Control of Quadrotor UAV With Continuous Fractional-Order Super Twisting Sliding Mode

TL;DR: The simulation results clearly show the high control performance, efficiency and high disturbance rejection capacity of the controller strategy proposed in this work in comparison with the nonlinear internal model control (NLIMC) and FO backstepping sliding mode control (FOBSMC) strategies.
Journal ArticleDOI

Hierarchical multi-innovation generalised extended stochastic gradient methods for multivariable equation-error autoregressive moving average systems

TL;DR: This study presents the modelling technology of multivariable equation-error autoregressive moving average (EEARMA) systems through observational data of systems to develop a simplified identification algorithm.