Titlebook: Differential Linear Matrix Inequalities; In Sampled-Data Syst José C. Geromel Book 2023 The Editor(s) (if applicable) and The Author(s), un

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書目名稱Differential Linear Matrix Inequalities影響因子(影響力)




書目名稱Differential Linear Matrix Inequalities影響因子(影響力)學(xué)科排名




書目名稱Differential Linear Matrix Inequalities網(wǎng)絡(luò)公開度




書目名稱Differential Linear Matrix Inequalities網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Differential Linear Matrix Inequalities被引頻次學(xué)科排名




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書目名稱Differential Linear Matrix Inequalities讀者反饋




書目名稱Differential Linear Matrix Inequalities讀者反饋學(xué)科排名

MONY

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Sampled-Data Control Systems,nst parameter uncertainty is concerned. This important aspect is left to be treated with details in the forthcoming chapters. The state space realization of the open-loop system is introduced together with the sampled-data control structure. As usual, conditions for sampled-data systems stability ar

OGLE

Filtering and Control,hapters some aspects involving this important performance index, now our goal is to tackle some other aspects related to norm and convex bounded parameter uncertainty. To this end, in a first step of analysis, we need to express the objective function and the optimal design problems through DLMIs. S

Latency

Filtering and Control,e with model and parameter uncertainty of the important norm bounded class. Our intention is to present this theory but restricted to the framework of sampled-data systems. Several aspects of analysis and control and filtering design are presented and discussed in detail, given particular attention

債務(wù)

Markov Jump Linear Systems, MJLS models adequately, with precision, many practical applications of particular interest and importance. Nevertheless, concerning sampled-data control of MJLS, very few results are available in the literature. This chapter aims to design linear filters and controllers in both contexts of . and .

債務(wù)

Nonlinear Systems Control,the particular but important class of nonlinear systems known as Lur’e systems, which is fully characterized in the literature and in the very beginning of this chapter. The results reported afterward may have a useful impact in sampled-data control systems due to the use of the ideas that stemmed f

BURSA

Model Predictive Control,formed in the Bibliography notes, it has been successfully applied to many systems of practical importance. One of the main reasons for that success is the fact that MPC is able to cope with state and control variable constraints. Hence, our main purpose is to generalize the previous state feedback

說不出

Numerical Experiments,es introduced in the previous chapters of this book. In fact, the designed control will not be practically implemented but numerically simulated, in order to evaluate stability, robustness, performance and the impact that the sampling period magnitude has on those important attributes of the closed-

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https://doi.org/10.1007/978-3-031-29754-0Differential Linear Matrix Inequalities; Convex Optimization; Robustness in control; Sampled-data Syste