&#x210B;<inf>&#x221E;</inf> dynamic output feedback for LPV systems subject to inexactly measured scheduling parameters

Gain‐scheduled continuous‐time control using polytope‐bounded inexact scheduling parameters

“…Now, for the mixed uncertainty case with | θ i | ≤ 1, , , for i = 1,2, the minimum values of γ obtained by Theorem considering s = 2 are given in Table , where the parameter admissible regions are modeled through minimum convex polytopes. Subsequently, the proposed method by Agulhari et al is employed to design a full‐order GSOF controller for different values of ζ . This method is initiated by first designing a state‐feedback controller by presetting a value for a scalar β .…”

“…Additionally, state feedback control design problem with absolute uncertainty on the measurements of the scheduling parameters is also previously considered in the literature. More recently, the simultaneous existence of proportional and absolute uncertainties (mixed uncertainty) are tackled in the works of Agulhari et al and Sato for reduced‐order GSOF control for continuous‐time systems and full‐order GSOF control for discrete‐time systems, respectively. The presented method by Agulhari et al is a two‐stage technique initiated from a parameter‐dependent state feedback and, subsequently, a GSOF controller is sought.…”

“…More recently, the simultaneous existence of proportional and absolute uncertainties (mixed uncertainty) are tackled in the works of Agulhari et al and Sato for reduced‐order GSOF control for continuous‐time systems and full‐order GSOF control for discrete‐time systems, respectively. The presented method by Agulhari et al is a two‐stage technique initiated from a parameter‐dependent state feedback and, subsequently, a GSOF controller is sought. The drawback is that the initialization with the state feedback may not always lead to a feasible optimization problem in the second stage.…”

“…The proposed method is formulated in terms of solutions to a set of parameter‐dependent LMIs using parameter searches for two scalar values. Similar to the work by Agulhari et al, since the controller matrices do not depend on the Lyapunov matrix, parameter‐dependent Lyapunov and auxiliary matrices are employed without requiring bounds on the derivatives of measurement errors to be available, contrary to the method of Sato and Peaucelle, where a quadratic Lyapunov function is employed to avoid requiring bounds on the time derivatives of the measurement errors. By rigorous theoretical proof, it is shown that the proposed method is not more conservative than some available approaches .…”

Switching LPV controller design under uncertain scheduling parameters

“…A more general approach was proposed in Sato and Peaucelle (2013), which considered additive uncertainties. Coexistence of additive and multiplicative uncertainties were addressed in Agulhari, Tognetti, Oliveira, and Peres (2013) and Sato (2015).…”