DEELT - Departamento de Engenharia Elétrica

URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/5266

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2020 - 20223

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Autor: search.filters.author.Santos, Luciano Antonio Frezzatto

Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
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    An auxiliary system discretization approach to Takagi-Sugeno fuzzy models.
    (2022) Campos, Víctor Costa da Silva; Braga, Marcio Feliciano; Santos, Luciano Antonio Frezzatto
    This paper proposes a new procedure for discretizing nonlinear systems described by Takagi-Sugeno fuzzy models. The discretization procedure consists of obtaining a linear auxiliary system that approximates the Takagi-Sugeno model over a sampling instant. By discretizing this auxiliary system, a norm bounded uncertain linear discrete-time system is found, which is capable of representing the fuzzy model. This auxiliary system, as well as the norm bounded uncertainty, is found by solving an optimization problem with Linear Matrix Inequality (LMI) constraints. To illustrate the discretization procedure, a constant state observer is synthesized based on simple LMI conditions and then applied to a real nonlinear Chua’s circuit. Additionally, a state-feedback controller based on our discretization approach is synthesized and we obtain larger maximum sampling periods than other tested strategies from the literature.
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    Control of event-triggered quasi-LPV systems based on an exact discretization approach - a linear matrix inequality approach.
    (2022) Campos, Víctor Costa da Silva; Santos, Luciano Antonio Frezzatto; Oliveira, Tiago Gaiba de; Manzoc, Víctor Estrada; Braga, Marcio Feliciano
    This paper presents an H∞ event-triggered state-feedback controller design for continuous-time non-linear systems via convex optimization techniques. The proposal is based on an exact discretization and its quasi-linear parameter varying representation. Thus, two sets of design conditions in terms of linear matrix inequalities by means of both parameter-dependent and quadratic Lyapunov functions are proposed. The proposed conditions also provide an estimate to system’s domain of attraction and an extra set of conditions is presented for a guaranteed minimum time between events. Well-known examples are employed to illustrate the effectiveness of the proposal.
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    Analytical upper bound for the error on the discretization of uncertain linear systems by using the tensor product model transformation.
    (2020) Campos, Victor Costa da Silva; Braga, Marcio Feliciano; Santos, Luciano Antonio Frezzatto
    This work provides analytical upper bounds on the discretization error of uncertain linear systems. The Tensor Product Model Transformation is used to approximate the derived discretized system, with a reduced number of vertices. Digital state feedback controllers are then designed for the discretized system, for comparison to other available work in the current literature.