Adaptive control of time delay teleoperation system with uncertain dynamics

Lu, Siyu; Ban, Yuxi; Zhang, Xia; Yang, Bo; Liu, Shan; Yin, Lirong; Zheng, Wenfeng

doi:10.3389/fnbot.2022.928863

Cited by 121 publications

(45 citation statements)

References 31 publications

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“…e training of the RBM network can be regarded as the initialization of the weights of a deep BP network, which gives DBN the advantage of strong feature learning ability, but it does not have the disadvantages of the BP network that it is easy to fall into the local optimum and the training is slow [13][14][15].…”

Section: Supervised Fine-tuningmentioning

confidence: 99%

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Sun

Chen

2022

International Transactions on Electrical Energy Systems

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Aiming at the problems of low prediction accuracy, long time, and poor results in current wind turbine generation power prediction methods, an offshore wind turbine generation power prediction method based on cascaded deep learning is proposed. Using deep belief networks, stacked autoencoding networks, and long short-term memory networks, a cascaded deep learning method is proposed to predict the power generation of offshore wind turbines. Multiple feature extractors are used to extract and fuse high-level features to form a unified feature with richer information to predict the power generation sequence of offshore wind turbines. According to the modeling strategy and port design strategy, using the stacked autoencoding networks as the basic unit, a cascaded deep learning model for generating power prediction of offshore wind turbines is established. Through the selection of input variables, the variables that have a great correlation with wind power are obtained. The layer-by-layer greedy algorithm is used for training from bottom to top, and supervised learning is used to fine-tune the network parameters from top to bottom to realize the generation power prediction of the offshore wind turbine. The experimental results show that the proposed method is effective in predicting the power generation of offshore wind turbines, which can effectively improve the prediction accuracy and shorten the prediction time.

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Section: Supervised Fine-tuningmentioning

confidence: 99%

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Sun

Chen

2022

International Transactions on Electrical Energy Systems

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“…In the game practice of ecological enrichment, compared with oligopoly industries with a small number of players, monopolistic competitive industries with a large number of players face more uncertainties of the returns from choosing ecological enrichment with the increase in the number of players, and some players may make an adverse selection to reach the critical scale boundary at point X. e self-resistance slope can be set by "PARTS" to change the expected return of choosing ecological enrichment and that of not choosing ecological enrichment, so that the number of players not choosing ecological enrichment will be blocked at a certain number, instead of falling completely or even bringing the strategy of choosing ecological enrichment to collapse [11][12][13]. When curve AS slides down to point X, the choice of ecological enrichment will become favorable, and the dominant strategy of the players will change to choosing ecological enrichment from not choosing ecological enrichment.…”

Section: Self-resistance Slope Modelmentioning

confidence: 99%

The Game and Impetus Analysis of Ecological Enrichment of Corporate Citizens

Li¹,

Ning

2022

Discrete Dynamics in Nature and Society

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Ecological enrichment is not the growth of the economic value of a business entity and the use of surplus to fund deficits, but the coordinated growth of corporate citizens’ ecological value and economic value, which is conducive to the positive evolution of ecological beauty and economic vitalization. Through the game model, this paper makes a game analysis of various possibilities of the game of ecological enrichment and further gives a balanced solution to the impetus of ecological enrichment. Positive-sum return, return equilibrium, and dynamic equilibrium are all the necessary conditions for the positive selection of repeated games for corporate citizens’ ecological enrichment and play a decisive role in the impetus of ecological enrichment.

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“…(1) Interregional Decoupling Modularity M 1 . According to the principle of inter-regional decoupling, a reasonable scheme of distributed zone should make the electrical connection of the nodes at both ends of the branch within the region close, and the electrical connection of the nodes at both ends of the branch at the regional boundary weak [27,28]. In order to reflect the different characteristics of PV node and PQ node, the definition of electrical distance is improved and M 1 is defined.…”

Section: Reduction Stage Reduction Adjustment Phasementioning

confidence: 99%

Automatic Distributed Control Method for Indoor Low-Voltage Electricity Based on Modal Symmetry Algorithm

Lian

2022

International Transactions on Electrical Energy Systems

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The traditional control method has a low margin of indoor voltage stability, which can easily lead to voltage collapse, and the control effect is not ideal. An automatic distributed control method for indoor low-voltage electricity system based on modal symmetry algorithm is proposed. In order to improve the voltage stability margin, the decoupling between the indoor low-voltage electricity characteristics and regions was analyzed. Based on the analysis results, the decoupling between regions and the robustness to faults of the distributed zone scheme were considered comprehensively, and the modularity and the optimal zoning scheme were screened. Based on the principle of modal symmetry, the original data of the distributed control matrix of low-voltage electricity was obtained. Mean standardization method was used to standardize the matrix, so as to realize the distributed control of indoor low-voltage electricity. The experimental results show that the proposed method improves the margin of voltage stability, overcomes the drawbacks of traditional methods, and reduces the probability of voltage accidents. It shows that this method has higher flexibility, higher reliability, and higher application value as well as improving the automatic control level of voltage.

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Adaptive control of time delay teleoperation system with uncertain dynamics

Cited by 121 publications

References 31 publications

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

The Game and Impetus Analysis of Ecological Enrichment of Corporate Citizens

Automatic Distributed Control Method for Indoor Low-Voltage Electricity Based on Modal Symmetry Algorithm

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