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Joaquin Carrasco

Researcher at University of Manchester

Publications -  101
Citations -  1588

Joaquin Carrasco is an academic researcher from University of Manchester. The author has contributed to research in topics: Nonlinear system & Monotone polygon. The author has an hindex of 19, co-authored 93 publications receiving 1120 citations. Previous affiliations of Joaquin Carrasco include University of Murcia.

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Zames-Falb multipliers for absolute stability: From O'Shea's contribution to convex searches

TL;DR: The equivalence of results obtained by different techniques, specifically Lyapunov and Popov's stability theories, led to one of the most important results in control engineering: the KYP Lemma.
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Comments on “On the Existence of Stable, Causal Multipliers for Systems With Slope-Restricted Nonlinearities”

TL;DR: Correct application of the conditions still gives an improvement over absolute stability criteria in the literature for at least one example, but some of the claims for lack of conservativeness in the above technical note should be moderated.
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LMI searches for anticausal and noncausal rational Zames–Falb multipliers

TL;DR: The conservatism of the restriction to causality on the multipliers is analyzed and the addition of a Popov multiplier to the anticausal Zames–Falb multiplier is implemented by analogy with the causal search.
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A Less Conservative LMI Condition for Stability of Discrete-Time Systems With Slope-Restricted Nonlinearities

TL;DR: Two less conservative LMI conditions for discrete-time systems with slope-restricted nonlinearities are constructed, derived via Lyapunov theory and the theory of integral quadratic constraints and noncausal Zames-Falb multipliers.
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A complete and convex search for discrete-time noncausal FIR Zames-Falb multipliers

TL;DR: The subclass is shown to be phase-equivalent to the class of discrete-time rational noncausal Zames-Falb multipliers, and can be expressed as an LMI (linear matrix inequality) whose number of parameters increases quadratically with model order andwhose number of linear constraints increases linearly with model orders.