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LMI-Based Robust Multivariable Super-Twisting Algorithm Design
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AI
DOI：10.1109/TAC.2024.3358235.png)
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摘要
En 中文
The aim of this article is to provide a new linear matrix inequality (LMI)-based robust multivariable super-twisting algorithm design able to deal with convex bounded model uncertainties in the input matrix and exogenous disturbances with norm-bounded time derivative. The final state feedback gain is calculated from a convex programming problem, expressed by LMIs with respect to all involved variables, that optimizes a guaranteed (worst case) performance index associated to the closed-loop system. As far as the nominal system is concerned, the existence of a solution to the control design problem is given in terms of a certain closed-loop transfer function H-infinity. An example illustrates the theoretical results reported in this article.
Keyword:
Symmetric matrices
Lyapunov methods
Uncertainty
Transient analysis
Stability criteria
Sliding mode control
Optimal control
Disturbance rejection
finite-time convergence
LMI-based design
multivariable super-twisting algorithm (MSTA)
optimal control
uncertain input matrix