Optimal interval type-2 fuzzy fractional order super twisting algorithm: A second order sliding mode controller for fully-actuated and under-actuated nonlinear systems

Zakeri, Ehsan; Moezi, Seyed Alireza; Eghtesad, Mohammad

doi:10.1016/j.isatra.2018.10.013

Cited by 48 publications

(20 citation statements)

References 44 publications

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“…We analysed multiple gene fragments separately with GMYC and combined under the multispecies coalescent model. Zakeri et al [64] pointed out that a strong phylogenetic signal from mtSSU may have dominated their analysis of the concatenated genes. We compared analyses with all data with a reduced nuclear dataset excluding the mitochondrial locus but this had marginal effect on the result (Additional file 3).…”

Section: Discussionmentioning

confidence: 99%

Species delimitation in the cyanolichen genus Rostania

et al. 2020

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Background In this study, we investigate species limits in the cyanobacterial lichen genus Rostania (Collemataceae, Peltigerales, Lecanoromycetes). Four molecular markers (mtSSU rDNA, β-tubulin, MCM7, RPB2) were sequenced and analysed with two coalescent-based species delimitation methods: the Generalized Mixed Yule Coalescent model (GMYC) and a Bayesian species delimitation method (BPP) using a multispecies coalescence model (MSC), the latter with or without an a priori defined guide tree. Results Species delimitation analyses indicate the presence of eight strongly supported candidate species. Conclusive correlation between morphological/ecological characters and genetic delimitation could be found for six of these. Of the two additional candidate species, one is represented by a single sterile specimen and the other currently lacks morphological or ecological supporting evidence. Conclusions We conclude that Rostania includes a minimum of six species: R. ceranisca, R. multipunctata, R. occultata 1, R. occultata 2, R. occultata 3, and R. occultata 4,5,6. Three distinct Nostoc morphotypes occur in Rostania, and there is substantial correlation between these morphotypes and Rostania thallus morphology.

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Section: Discussionmentioning

confidence: 99%

Species delimitation in the cyanolichen genus Rostania

et al. 2020

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“…Besides, the effect of uncertainties is not considered, and the overall controller's stability is not proved. To cope with this problem, employing sliding mode control method, would be an option to overcome large uncertainties and provide high tracking accuracy [20], [21], [37]. However, the conventional SMC suffers from chattering which emanates from a switching term within its control low.…”

Section: Introductionmentioning

confidence: 99%

“…However, the conventional SMC suffers from chattering which emanates from a switching term within its control low. To handle this problem, several methods have been suggested such as higher-order and Quasi SMCs [20], [21]. Another problem with SMC is its susceptibility to noisy feedback.…”

Section: Introductionmentioning

confidence: 99%

Optimal Image-Based Task-Sequence/Path Planning and Robust Hybrid Vision/Force Control of Industrial Robots

2022

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“…The interval type-2 fuzzy logic system applied in fractional order system mainly included 2 aspects. The first was designing of fractional order interval type 2 fuzzy controller, such as interval type-2 fractional order fuzzy PID-based power system stabilizer [48], the optimal time domain tuning of interval type-2 fractional order fuzzy PID controller using ABC-GA [49], interval type-2 fractional order fuzzy controller for a tractor active suspension system [50], interval type-2 fractional order controller for redundant robot [51], interval type-2 fuzzy fractional-order backstepping sliding mode controller [52], optimal interval type-2 fuzzy fractional order super twisting algorithm [53], interval type-2 fractional order fuzzy logic controllers for fractional order systems [54] and dynamic stability enhancement of power system [55]. The other was controlling of fractional order system using interval type 2 fuzzy logic system, such as interval type-2 fuzzy neural network for fractional-order chaotic systems [56], interval type-2 fuzzy system for chaotic non-linear fractional order systems [57], fractional order interval type-2 T-S fuzzy systems [58].…”

Section: Introductionmentioning

confidence: 99%

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

Zhong

2020

IEEE Access

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Fractional order PID controller was received attentions in control problems for it had 2 freedom adjustable parameters. Practices had proved that better results could be obtained by introduction of fractional order PID in control problems. And in recently years, fractional order PID combined with fuzzy logic system was gradually got attention, the typical of which was fractional order type-1 fuzzy PID controller. For fractional order PID and fractional order type-1 fuzzy PID controller couldn't deal with the uncertainty of system, so fractional order interval type-2 fuzzy PID controller was applied to solve this problem. But interval type-2 fuzzy sets were a simplification of type-2 fuzzy sets, and there may be have the defect of information loss. A fractional order general type-2 fuzzy PID controller was proposed in this article. The proposed controller based on general type-2 fuzzy logic system, which can make full use of the advantages of general type-2 fuzzy logic system in describing the uncertainty of the system. The fractional order general type-2 fuzzy PID controller utilized a simplified type reduction called NT type reduction algorithm. The NT type reduction algorithm can get the defuzzification result directly and avoided iterative process as KM type reduction commonly used in interval type-2 fuzzy controller. The simulations of 3 processes and a practical inverted pendulum system show that fractional order general type-2 fuzzy PID controller can reduce overshoot, improve system response speed and accelerate system stability time in comparing with other controllers. Especially, when the system has disturbance, parameters uncertainty or structure uncertainty, the fractional order general type-2 fuzzy PID controller has better control effects than other compared controllers. INDEX TERMS Fractional order control, fractional order fuzzy PID, general type-2 fuzzy sets, general type-2 fuzzy logic system, type reduction.

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Optimal interval type-2 fuzzy fractional order super twisting algorithm: A second order sliding mode controller for fully-actuated and under-actuated nonlinear systems

Cited by 48 publications

References 44 publications

Species delimitation in the cyanolichen genus Rostania

Species delimitation in the cyanolichen genus Rostania

Optimal Image-Based Task-Sequence/Path Planning and Robust Hybrid Vision/Force Control of Industrial Robots

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

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