Titlebook: Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems; Ding Wang,Chaoxu Mu Book 2019 Springer Nature Singapore

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書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems影響因子(影響力)




書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems影響因子(影響力)學(xué)科排名




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書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems被引頻次




書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems被引頻次學(xué)科排名




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書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems讀者反饋




書(shū)目名稱Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems讀者反饋學(xué)科排名

有罪

Joana Azeredo,Sanna Sillankorvaptimal control problem for a class of continuous-time uncertain nonlinear systems is investigated by using data-based adaptive critic designs. The neural network identification scheme is combined with the traditional adaptive critic technique, in order to design the nonlinear robust optimal control

Watemelon

dissolution

Jargon

https://doi.org/10.1007/978-3-319-41986-2anism and adaptive critic learning technique. The main idea is to combine the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem under uncertain environment. This can not only make better use of computation and communication resources, but al

生銹

Bacteriophage Discovery and Genomicsral learning mechanism. The main objective lies in that establishing an additional stabilizing term to reinforce the traditional training process of the critic neural network, so that to reduce the requirement with respect to the initial stabilizing control, and therefore, bring in an obvious conven

Coterminous

John J. Dennehy,Stephen T. Abedongning adaptive critic control strategies. In this chapter, based on the neural network learning component, the robust stabilization scheme of nonlinear systems with general uncertainties is developed. Remarkably, the involved uncertain term is more general than the matched case. Through system trans

Control-Group

Mundane

hegemony