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Quantized event‐triggered H ∞ control of linear networked systems with time‐varying delays and packet losses
Author(s) -
Soltaninejad Moein,
Ghiasi Amir Rikhtehgar,
Ghaemi Sehraneh,
Bagheri Peyman
Publication year - 2019
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2545
Subject(s) - control theory (sociology) , network packet , quantization (signal processing) , networked control system , emulation , exponential stability , packet loss , computer science , upper and lower bounds , logarithm , stability (learning theory) , bounded function , mathematics , control (management) , nonlinear system , algorithm , computer network , mathematical analysis , physics , quantum mechanics , artificial intelligence , machine learning , economics , economic growth
Summary This paper studies the event‐triggered (ET) H ∞ control of linear networked systems based on static output‐feedback. An emulation‐based stabilization of the networked system is investigated under the constraints such as (i) quantizations, (ii) network‐induced delays, (iii) external disturbances, and (iv) packet losses. In particular, output measurement and control input quantizations, lower and upper bounds of the network‐induced delays, bounded external disturbances, and packet losses are carefully taken into account. The process of stability analysis has three steps. In the first step, a quantized ET control is defined, and then three sector‐bound methods for logarithmic quantization are formulated. In the second step, the ET‐mechanism is described with an input delay model. Based on the model, for two approaches, namely, switching‐ET and periodic‐ET, the criteria for the exponential stability and L 2 ‐gain analysis of perturbed networked system are established, respectively. In the third step, a constraint for packet loss effect is provided. In summary, the stability analysis is based on linear matrix inequalities (LMIs) through a Lyapunov‐Krasovskii functional method. Finally, the simulation results are given to evaluate the validation of the analysis using two benchmark examples.