Transmit power policies for stochastic stabilisation of multi-link wireless networked control systems

被引:0
|
作者
Maass, Alejandro I. [1 ]
Nesic, Dragan [2 ]
Postoyan, Romain [3 ]
Varma, Vineeth S. [3 ]
Lasaulce, Samson [3 ,4 ]
Munoz-Carpintero, Diego [5 ]
机构
[1] Pontificia Univ Catolica Chile, Dept Elect Engn, Santiago 7820436, Chile
[2] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia
[3] Univ Lorraine, CNRS, CRAN, F-54000 Nancy, France
[4] Khalifa Univ, Abu Dhabi 127788, U Arab Emirates
[5] Univ OHiggins, Inst Engn Sci, Rancagua 2841959, Chile
基金
澳大利亚研究理事会;
关键词
Wireless networked control systems; Transmit power; Stochastic stability; Packet dropouts; Random transmissions; INPUT-OUTPUT STABILITY; NONLINEAR-SYSTEMS; TRACKING CONTROL; SENSOR NETWORKS; HYBRID SYSTEMS; COMMUNICATION; CHANNELS; PERFORMANCE; PROTOCOL; LINKS;
D O I
10.1016/j.automatica.2024.111936
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Transmit power control is crucial in wireless networks due to limited battery power, impacting both economic and environmental aspects by reducing power consumption. High transmission power diminishes node lifespan, causes interference, and pollution. Existing work in wireless networks mainly focuses on power policies for communication aspects like quality of service and channel capacity, while wireless networked control systems (WNCSs) require adapted policies for controloriented requirements such as stability. Recent research in the control community has predominantly focused on linear systems or non-linear systems with a single-link perspective. This paper introduces a framework for designing stabilising transmit power levels for broader classes of non-linear plants and multi-link scenarios. By considering the non-linear relationship between channel success probabilities and transmit power, we establish stability conditions linking channel probabilities and transmission rate. These results, along with practical interference models, offer a methodology for stabilising transmit power in non-linear and multi-link WNCSs. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页数:13
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