LMI toolbox
Linear Matrix Inequalities (LMIs) and LMI techniques have emerged as powerful design tools in areas ranging from control engineering to system identification and structural design. The LMI Control Toolbox implements state-of-the-art interior-point LMI solvers. While these solvers are significantly faster than classical convex optimization algorithms, it should be kept in mind that the complexity of LMI computations remains higher than that of solving, say, a Riccati equation. For instance, problems with a thousand design variables typically take over an hour on today’s workstations. However, research on LMI optimization is still very active and substantial speed-ups can be expected in the future. Thanks to its efficient “structured” representation of LMIs, the LMI Control Toolbox is geared to making the most out of such improvements
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References in zbMATH (referenced in 1463 articles )
Showing results 1 to 20 of 1463.
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- Cheraghiyan, M.: A new separated fault estimator and fault-tolerant control design strategy for uncertain nonlinear systems using T-S fuzzy modeling (2021)
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- Polyak, B. T.; Khlebnikov, M. V.; Shcherbakov, P. S.: Linear matrix inequalities in control systems with uncertainty (2021)
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- Thuan, Mai V.; Niamsup, Piyapong; Phat, Vu N.: Finite-time control analysis of nonlinear fractional-order systems subject to disturbances (2021)
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- Yu, Xiao; Liao, Fucheng: A tracking controller design with preview action for a class of nonlinear Lur’e systems with time-varying delays and external disturbances. (2021)
- Achour, Hakim; Boukhetala, Djamel; Labdelaoui, Hilal: An observer-based robust (H \infty) controller design for uncertain Takagi-Sugeno fuzzy systems with unknown premise variables using particle swarm optimisation (2020)