Browsing by Author "Palhares, Reinaldo Martinez"

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    Gain-scheduled control for discrete-time nonlinear parameter-varying systems with timevarying delays.
    (2021) Peixoto, Márcia Luciana da Costa; Braga, Marcio Feliciano; Palhares, Reinaldo Martinez
    This study addresses the gain-scheduled control problem for discrete-time delayed non-linear parameter-varying (NLPV) and linear parameter-varying (LPV) systems. First, by constructing the parameter-dependent Lyapunov–Krasovskii functional and employing multiple auxiliary functions, delay-dependent reciprocally convex inequality, and selecting a suitable augmented vector, novel delay-dependent linear matrix inequality conditions for the static output-feedback control design and state-feedback control design for delayed NLPV are provided. Second, the results obtained for discrete-time delayed NLPV systems are modified in a simple way to deal with discrete-time delayed LPV systems. Finally, the effectiveness of the proposed methods is illustrated by numerical examples.
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    Revisiting the TP model transformation : interpolation and rule reduction.
    (2015) Campos, Victor Costa da Silva; Tôrres, Leonardo Antônio Borges; Palhares, Reinaldo Martinez
    The tensor-product (TP) model transformation is a numerical technique that finds a convex representation, akin to aTakagi-Sugeno (TS) fuzzy model, from a given linear parameter varying (LPV) model of a system. It samples the LPV modelover a limited domain, which allows the use of the higher order singular value decomposition (HOSVD) and convex transfor-mations that leads to the TS representation of the LPV model. In this paper, we discuss different strategies that could be usedon the sampling step of the TP model transformation (which in turn lead to different membership function properties of a TSfuzzy model). Additionally, this paper discusses how the other steps could be used to reduce the number of rules of a given TSfuzzy model. In cases where nonzero singular values were discarded in the rule reduction, we also show how to obtain anuncertain model that covers the original.