Application of Interval Type-2 Fuzzy Sets in Unmanned Vehicle Visual Guidance

Mo, Hong; Zhao, Xiaoqiang; Wang, Fei‐Yue

doi:10.1007/s40815-019-00680-4

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…In this paper, adaptive IT2FLS is applied to approximate the unknown term De. Inspired from the study in [42][43][44], the IT2FLS is adopted considering z 1 and z 2 as inputs and gðz=ûÞ as the approximation result.…”

Section: Adaptive Interval Type-2 Fuzzy Nonsingular Fast Terminal Sliding Mode Controlmentioning

confidence: 99%

“…In this context, the features of interval type-2 fuzzy logic system (IT2FLS) much stimulate use in the identification and control of the nonlinear system [41,42]. Several reasons have led to a great interest in research in applying adaptive IT2FLS to achieve control objectives over type-1 fuzzy logic system (T1FLS) approach [43,44]. In IT2FLS, the uncertainty in membership functions (MF) is represented as an extra dimension which makes it possible to show promising results in dealing with uncertainties and nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

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Robust Model-Free Adaptive Interval Type-2 Fuzzy Sliding Mode Control for PEMFC System Using Disturbance Observer

Abbaker

Wang

Tian

2020

Int. J. Fuzzy Syst.

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This paper proposes a novel robust model-free adaptive interval type-2 fuzzy sliding mode control (MF-AIT2FSMC) for the proton exchange membrane fuel cell (PEMFC) system using nonlinear disturbance observer. The main control objective is to adjust the oxygen stoichiometry at its reference trajectory under load disturbances and parameter uncertainties, to avert the oxygen starvation problem and to obtain maximum net power. The referred MF-AIT2FSMC control strategy is composed of three parts: first, a nonlinear disturbance observer is proposed to estimate both oxygen and nitrogen partial pressures which are considered as unmeasurable variables. Second, a nonlinear disturbance observer-based intelligent proportional-integral (NDBO-iPI) control is derived, whereas the NDBO is used to estimate the unmodeled system dynamics via the knowledge of input and output signals. Third, adaptive interval type-2 fuzzy nonsingular fast terminal sliding mode control is inserted to NDBO-iPI control to compensate the NDBO estimation error, enhance the control performance and ensure the global controlled system stability. Furthermore, the entire controlled system stability is verified via the Lyapunov approach. Finally, the corresponding numerical simulation on the PEMFC system is obtained to demonstrate the effectiveness and efficiency of the proposed MF-AIT2FSMC technique by comparing with the NDBO-iPI and standard PID controller.

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Section: Adaptive Interval Type-2 Fuzzy Nonsingular Fast Terminal Sliding Mode Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Model-Free Adaptive Interval Type-2 Fuzzy Sliding Mode Control for PEMFC System Using Disturbance Observer

Abbaker

Wang

Tian

2020

Int. J. Fuzzy Syst.

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“…In recent years, visual guidance technology has gained significant attention in the field of computer vision and has been widely applied in domains such as autonomous driving [8], unmanned drone landing [9], robot guidance [10], and automatically guided vehicle (AGV) visual guidance [11]. Visual sensors can capture rich environmental scene information, enabling the precise positioning and guidance of targets.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision Guidance for Unmanned Surface Vehicle Recovery

Li,

Xi,

Shi

et al. 2024

Applied Sciences

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The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of ±60 degrees.

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“…Fuzzy-sets based analysis often involves solution of interval variables, as demonstrated in papers by Dourado et al [1], Mo et al [2], Tian et al [3], and Liao et al [4]. In this conjunction, the solution of interval quadratic equations is of paramount importance (see, for example, anonymous, 2022 [5]).…”

Section: Introductionmentioning

confidence: 99%

“…Note that we set that 1 2 x x < . This implies that we need to be cautious whether 0 a < or 0 a > , because a cannot be zero in Equation (2). Indeed, when 0 a > , to respect 1 2 x x < , we will have to consider that: On the other hand, when 0 a < , to get 1 2 x x < , we have to consider that: The mathematical explanation of the previous statement will be provided in Appendix A.…”

Section: Introductionmentioning

confidence: 99%

Novel Method to Deal with Interval Quadratic Equations via Sign-Variation Analysis

Yvain,

Elishakoff

2023

JAMP

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In this article, analytical results are obtained apparently for the first time in the literature, for the lower and upper bounds of the roots of quadratic equations when two or all three coefficients a, b, c constitute an interval, with a method called the sign-variation analysis. The results are compared with the parametrization technique offered by Elishakoff and Miglis, and with the solution yielded by minimization and maximization commands of the Maple software. Solutions for some interval word problems are also provided to edulcorate the methodology. This article only focuses on the real roots of those quadratic equations, complex solutions being beyond this investigation.

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Application of Interval Type-2 Fuzzy Sets in Unmanned Vehicle Visual Guidance

Cited by 15 publications

References 11 publications

Robust Model-Free Adaptive Interval Type-2 Fuzzy Sliding Mode Control for PEMFC System Using Disturbance Observer

Robust Model-Free Adaptive Interval Type-2 Fuzzy Sliding Mode Control for PEMFC System Using Disturbance Observer

Monocular Vision Guidance for Unmanned Surface Vehicle Recovery

Novel Method to Deal with Interval Quadratic Equations via Sign-Variation Analysis

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