LPV control and full block multipliers

By Carsten W. Scherer. at - Automatisierungstechnik, 2001

682 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/6d25b8727741913611377348f1cfb2a49b49f6ce

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In Computational Theory and Mathematics

In Control and Systems Engineering

Citations in the corpus

IQC‐synthesis with general dynamic multipliers (2014, background): “1 to robust gain-scheduled controller synthesis, by adopting the results of [24, 27, 45].”
Matrix Sum-of-Squares Relaxations for Robust Semi-Definite Programs (2006, methodology): “One can then apply the full block S-procedure [16, 30 ] to infer that”
Bumpless transfer between observer-based gain scheduled controllers (2005, background): “Linear parameter varying control (Apkarian & Gahinet 1995, Packard 1994, Scherer 2001), which is a form of gain scheduling, can be used to design controllers with stability guarantees for fast parameter and/or operating point changes, but they require an exact model of the plant in between the…”
From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, methodology): “Then, we combine the full block S-procedure (cf. [49], [51, p. 367]) with the fact that the LMI relaxation of [51] is exact for a single full block [51, Th. 5.3].”
The global modal parameterization for non‐linear model‐order reduction in flexible multibody dynamics (2007, background): “polytopic linear model [28], model based on linear fractional transformation [29]).”
Distributed control design for systems interconnected over an arbitrary graph (2004, background): “This condition—which physically means that the plant and controller use identical communication channels—is of the same nature (and plays the same role in rewriting the closed-loop system as an LFT) as the basic assumption in LPV control design, where one is looking for controllers that are paramete”
Interpolating model identification for SISO linear parameter-varying systems (2009, background): “There is a continuing effort to design LPV controllers that achieve higher performance while still guaranteeing stability for all possible parameter variations [6–17].”
A Survey of Linear Parameter-Varying Control Applications Validated by Experiments or High-Fidelity Simulations (2015, methodology): “, the Bounded Real Lemma [18], [21], [40].”
Data-Driven Dissipativity Analysis: Application of the Matrix S-Lemma (2021, methodology): “Dualization is an important concept in robust control, see e.g. [25], [30].”
The scenario approach to robust control design (2006, background): “The LPV formulation has recently received considerable attention, since it forms the basis of systematic gainscheduling approaches to non-linear control design, see for instance [8, 13, 59, 14] and the survey [58].”