Abstract:The vibration caused by the multiple narrow band disturbances exists widely in the mechanical systems. In this paper, a Youla parameterized adaptive control approach is introduced for the rejection of unknown multiple narrow band disturbances. The adaptive notch filter weighted Q (Youla) parameter is adopted to the online internal model principle-based regulator, so that the disturbances can be fully attenuated and the robustness of the closed-loop system is improved. A central controller is first designed to … Show more
This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.
This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.
“…The use of YK parametrization Q in adaptive noise and vibration rejection context covered different applications requiring high control precision and low noise sensitivity as: wafer scanning in semiconductors Chen, Jiang, and Tomizuka (2015) , data storage systems (reading/writing) Chen and Tomizuka (2013) ; Martinez and Alma (2012) ; Wu, Zhang, Chen, and Wang (2018) , mechatronics Tomizuka (2008) , active suspension systems Doumiati et al (2017) ; Landau, Constantinescu, and Rey (2005) , and biochemistry Valentinotti et al (2003) where the regulation problem is to maximize the biomass productivity in the fed-batch fermentation of a specie of yeasts and the cell growth is considered as an unstable disturbance rejected by the YK parameter Q. In Luca, Rodriguez-Ayerbe, and Dumur (2011) the feedback YK noise rejection controller is extended to control a LPV plant.…”
Youla-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control. This paper collects the recent work and classifies them according to the use of YK parametrization, Dual YK parametrization or both, providing the latest advances with main applications in different control fields. A final discussion gives some insights on the future trends in the field.
“…The synthesis of the results of the benchmarking can be found in I. as well as in the references of the contributors: Aranovskiy and Freidovich (2013),Airimitoaie, Castellanos Silva, and Landau (2013),Castellanos-Silva, Landau, and Airimitoaie (2013),Castellanos-Silva et al (2016),de Callafon and Fang (2013), Chen and Tomizuka (2013), Karimi and Emedi (2013), Wu and Ben Amara (2013). A recent application paper using adpative Youla Kucera feedback compensation is Wu, Zhang, Chen, and Wang (2018)…”
Section: Feedback Compensation Of Narrow Band Disturbancesmentioning
Youla-Kucera parametrization plays a very important role in adaptive active vibration control and adaptive active noise control. This concerns both vibration and noise attenuation by feedback as well as by feedforward compensation when a measurement of an image of the disturbance (noise or vibration) is available. The paper will review the basic algorithms and various extensions trying to emphasize the advantages of using Youla-Kucera parametrization. Specific aspects related to the use of this approach in adaptive active vibration and noise control will be mentioned. A brief review of applications and experimental testing will be provided.
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