scholarly journals Polytopic Representation of Parameter Varying Neural Models for Loading Systems

2014 ◽  
Vol 7 (3) ◽  
Author(s):  
T. Vadvári ◽  
P. Várlaki
Keyword(s):  
Neural Models ◽  
Parameter Varying ◽  
Polytopic Representation

scholarly journals Derivative-Based Sampled Data Control for Continuous Linear Parameter Varying System With Unknown Parameters

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Sonia Maalej ◽  
Alexandre Kruszewski ◽  
Lotfi Belkoura
Keyword(s):  
Robust Stabilization ◽  
Sampled Data ◽  
Linear Parameter ◽  
Unknown Parameters ◽  
Linear Parameter Varying ◽  
Data Control ◽  
Sampled Data Control ◽  
Parameter Varying ◽  
Polytopic Representation ◽  
Linear Parameter Varying System

This paper deals with the robust stabilization of a class of linear parameter varying (LPV) systems in the sampled data control case. Instead of using a state observer or searching for a dynamic output feedback, the considered controller is based on output derivatives estimation. This allows the stabilization of the plant with very large parameter variations or uncertainties. The proof of stability is based on the polytopic representation of the closed-loop under Lyapunov conditions and system transformations. The result is a control structure with only one parameter tuned via very simple conditions. Finally, the effectiveness of the proposed method is verified via a numerical example of a second-order LPV system.

Fault Reconstruction and Accommodation in Linear Parameter-Varying Systems via Learning Unknown-Input Observers

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Qingxian Jia ◽  
Wen Chen ◽  
Yingchun Zhang ◽  
Xueqin Chen
Keyword(s):  
Fault Tolerant ◽  
Lyapunov Stability Theory ◽  
Unknown Input ◽  
Actuator Faults ◽  
Linear Parameter ◽  
Linear Parameter Varying ◽  
Fault Reconstruction ◽  
Parameter Varying ◽  
Polytopic Representation ◽  
The Stability

This paper addresses the problem of observer-based fault reconstruction and accommodation for polytopic linear parameter-varying (LPV) systems. A polytopic representation of an LPV system subject to actuator faults and external disturbances is first established; then, a novel polytopic learning unknown-input observer (LUIO) is constructed for simultaneous state estimation and robust fault reconstruction. The stability of the presented LUIO is proved using Lyapunov stability theory together with H∞ techniques. Further, using reconstructed fault information, a reconfigurable fault-tolerant controller is designed to compensate for the influence of actuator faults by stabilizing the closed-loop system. At last, an aircraft example is employed to illustrate the effectiveness and practicability of the proposed techniques.

Introductory paper

1966 ◽  
Vol 24 ◽  
pp. 3-5
Author(s):  
W. W. Morgan
Keyword(s):  
Line Intensity ◽  
Classification Scheme ◽  
Two Dimensional ◽  
Spectral Classification ◽  
Dimensional Array ◽  
Rr Lyrae ◽  
Metallic Line ◽  
Introductory Paper ◽  
Parameter Varying ◽  
Definition Of

1. The definition of “normal” stars in spectral classification changes with time; at the time of the publication of theYerkes Spectral Atlasthe term “normal” was applied to stars whose spectra could be fitted smoothly into a two-dimensional array. Thus, at that time, weak-lined spectra (RR Lyrae and HD 140283) would have been considered peculiar. At the present time we would tend to classify such spectra as “normal”—in a more complicated classification scheme which would have a parameter varying with metallic-line intensity within a specific spectral subdivision.

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