Titlebook: Control of Uncertain Systems; Proceedings of an In Diederich Hinrichsen,Bengt M?rtensson Conference proceedings 1990 Springer Science+Busin

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書目名稱Control of Uncertain Systems影響因子(影響力)




書目名稱Control of Uncertain Systems影響因子(影響力)學(xué)科排名




書目名稱Control of Uncertain Systems網(wǎng)絡(luò)公開度




書目名稱Control of Uncertain Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Control of Uncertain Systems被引頻次




書目名稱Control of Uncertain Systems被引頻次學(xué)科排名




書目名稱Control of Uncertain Systems年度引用




書目名稱Control of Uncertain Systems年度引用學(xué)科排名




書目名稱Control of Uncertain Systems讀者反饋




書目名稱Control of Uncertain Systems讀者反饋學(xué)科排名

CESS

https://doi.org/10.1007/978-3-662-29503-8tion under simultaneous plant and controller uncertainty. Finally we discuss an example of an infinite dimensional (delay) system and we give an explicit closed form expression for the optimally robust controller with respect to gap ball uncertainty.

FLIRT

Recessive

https://doi.org/10.1007/978-3-662-29521-2bmodels. Each submodel describes the system at one operating condition. The parameters of the submodels have to be estimated on-line. A statistical test method is applied for the fast detection of parameter changes. For the design of the self-tuning controller an LQG-approach has been applied.

Uncultured

https://doi.org/10.1007/978-3-662-29521-2cati-like algebraic equation. A convexity property of a matrix Riccati function is used to compute for the closed-loop system an ..-norm bound smaller than the predetermined bound, and to find an enlarged admissible set of plant uncertainities.

Capitulate

Conference proceedings 1990electing a feasible controller accomplishing the control objective. A priori knowledge about the plant is normally represented as a mathemat- ical model, given by physical laws or measurement data. Such a model is inevitably uncertain, due to measurement errors, simplified models of natural laws, ne

Capitulate

https://doi.org/10.1007/978-3-662-29523-6lgorithm presented in previous papers to compute efficiently the structured stability margin. Some numerical examples are given showing that computational improvements over the existing algorithm may be obtained for several problems.

tendinitis

哭得清醒了

ign- ing/selecting a feasible controller accomplishing the control objective. A priori knowledge about the plant is normally represented as a mathemat- ical model, given by physical laws or measurement data. Such a model is inevitably uncertain, due to measurement errors, simplified models of natura

蔑視

https://doi.org/10.1007/978-3-662-29503-8ors which includes many delay and distributed systems. The optimal stability margin is expressed in terms of the solutions of the control and filter algebraic Riccati equations. The applicability of this theory is demonstrated by a controller design for a flexible beam with uncertain parameters.