Fuzzy Control of Wing Rock for Slender Delta wings

Tarn, J. H.; Hsu, Feng-Yih

doi:10.23919/acc.1993.4793048

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…For the demonstration, a wing-rock model for delta-wing aircraft is used (Tarn and Hsu, 1993). The continuous-time dynamics of the wing-rock model can be written as and Hsu, 1993), and therefore, the optimization variable space is U := U N . The horizon and time step are set as N = 5 and ∆t = 0.1s, respectively.…”

Section: Case 2: Learning-based Nonlinear Model Predictive Controlmentioning

confidence: 99%

The Department of Neurosurgery at Seoul National University: Past, Present, and Future

Kim¹

2003

Neurosurgery

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Image-text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa. One challenge of this task is the vulnerability to input image/text corruptions. Such corruptions are often unobserved during the training, and degrade the retrieval model's decision quality substantially. In this paper, we propose a novel image-text retrieval technique, referred to as robust visual semantic embedding (RVSE), which consists of novel image-based and text-based augmentation techniques called semantic-preserving augmentation for image (SPAug-I) and text (SPAug-T). Since SPAug-I and SPAug-T change the original data in a way that its semantic information is preserved, we enforce the feature extractors to generate semantic-aware embedding vectors regardless of the corruption, improving the model's robustness significantly. From extensive experiments using benchmark datasets, we show that RVSE outperforms conventional retrieval schemes in terms of image-text retrieval performance.1 Index Terms-image-text retrieval, data augmentation, robustness, image and text corruption INTRODUCTIONRecently, image-text retrieval, a task to find out images (sentence) that accurately describe a given sentence (image), has received special attention due to its wide range of applications such as image search, social networking service (SNS) hashtag/post generation, and semantic communication for Internet of Things (IoT), to name just a few [1,2,3,4,5]. Since it is in general very difficult to compare samples obtained from two different modalities (image and text), a projection of image and text to the common embedding space (a.k.a. visual semantic embedding (VSE) space) is required [1,2,6,7,8,9]. To generate the image and text embedding vectors, deep learning (DL)-based image and text feature extractors (i.e., ResNet and BERT) have been popularly used [10,11]. By comparing the obtained vectors, we can compute the similarity scores between available image-text pairs and then choose

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Section: Case 2: Learning-based Nonlinear Model Predictive Controlmentioning

confidence: 99%

The Department of Neurosurgery at Seoul National University: Past, Present, and Future

Kim¹

2003

Neurosurgery

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“…The assumption of (S2) assures that the FLC represented by This assumption of (D2) is not heavily restrictive in that this skew symmetry can be found in the vast majority of FLC's proposed to date [6], [11], [16]- [19]. Especially, the fuzzy sliding mode controllers (FSMC) satisfy the above skew-symmetric condition [20]- [22] D3)…”

Section: For the Dynamic Casementioning

confidence: 99%

“…The plant to be controlled in this example is an Electrocraft Corporation E530 motor system governed by the following equation: (19) where the variables and the parameters are as follows:…”

Section: B Examplementioning

confidence: 99%

Limit-cycle prediction of a fuzzy control system based on describing function method

Kim

Lee

Park

2000

IEEE Trans. Fuzzy Syst.

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Limit-cycle is the phenomenon that can be observed in the systems composed of nonlinear elements. The phenomenon is of fundamental importance in nonlinear systems and, as far as the design of a nonlinear system is concerned, it should be considered along with the stability analysis. In this paper, the limit-cycle of a system controlled by a fuzzy logic controller (FLC) is addressed via some of the classical control techniques used to analyze nonlinear systems in the frequency domain. First, reasonable assumptions are made on the structure of the FLC by using fuzzy basis functions (FBF's) and the describing function of the FLC is derived to analyze and predict the existence of the limit-cycle of the closed-loop system including the FLC. Finally, computer simulation is performed to show how the analysis given in the paper is used to predict the existence of the limit-cycle of the fuzzy control system.

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“…In Ref. [4], fuzzy logicbased roll controllers have been designed to control wingrock motion of a free-to-roll stinged delta wing. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive tracking control of delta wing vortex-coupled roll dynamics using RBF neural networks

Pakmehr

Gordon

Rabbath

Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005.

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In this paper a robust adaptive control strategy has been proposed and applied for vortex-coupled delta wing roll dynamics with parameter uncertainty in the rolling moment coefficient. The robust adaptive tracking neurocontroller employs a new network of gaussian Radial Basis Functions (RBF) to adaptively compensate for the rolling moment coefficient. Rolling moment coefficient, as a function of left and right vortex breakdown positions, is estimated online in adaptive neuro-controller structure using a special feature of RBF networks for the delta wing case. The proposed controller is stable with good tracking performance.

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Fuzzy Control of Wing Rock for Slender Delta wings

Cited by 12 publications

References 9 publications

The Department of Neurosurgery at Seoul National University: Past, Present, and Future

The Department of Neurosurgery at Seoul National University: Past, Present, and Future

Limit-cycle prediction of a fuzzy control system based on describing function method

Robust adaptive tracking control of delta wing vortex-coupled roll dynamics using RBF neural networks

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