Switching Stochastic Approximation and Applications to Networked Systems
Switching Stochastic Approximation and Applications to Networked Systems
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DOI：
10.1109/tac.2018.2882159
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发表时间：
2019-09
影响因子：
6.8
通讯作者：
G. Yin;L. Wang;Thu Nguyen
中科院分区：
文献类型：
--
作者：
G. Yin;L. Wang;Thu Nguyen
本文研究了网络系统中控制和通信之间的相互作用，通过研究一类随机逼近算法，适应随机网络拓扑切换过程，时变函数，非线性动力学，加性和非加性噪声，和其他不确定性。控制策略和多个随机过程之间的相互作用在这些问题中引入了关键的挑战。通过将随机切换建模为离散时间马尔可夫链，并在统一的框架下研究多个随机不确定性，证明了在广泛的条件下，算法是收敛的。通过建立算法的收敛速度和渐近特性，进一步分析了算法的性能。模拟案例研究进行评估的程序在各个方面的性能。
This paper investigates the interaction between control and communications in networked systems by studying a class of stochastic approximation algorithms that accommodate random network topology switching processes, time-varying functions, nonlinear dynamics, additive and nonadditive noises, and other uncertainties. Interaction among control strategy and the multiple stochastic processes introduces critical challenges in such problems. By modeling the random switching as a discrete-time Markov chain and studying multiple stochastic uncertainties in a unified framework, it is shown that under broad conditions, the algorithms are convergent. The performance of the algorithms is further analyzed by establishing their rate of convergence and asymptotic characterizations. Simulation case studies are conducted to evaluate the performance of the procedures in various aspects.