ℒ 2 gain tracking control of linear completely unknown discrete-time networked control systems with dropout

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作   者：

Huang, Deng;  Xiao, Feng;  Feng, Qian;  Zhang, Cong; 

作者机构：

North China Electric Power University School of Control and Computer Engineering; 

关键词：

INFINITY;  model-free;  ZERO-SUM GAMES;  dropout;  (2 )gain tracaking optimal control;  & Laplacetrf;  reinforcement learning;  DESIGNS; 

期刊名称：

International Journal of Robust and Nonlinear Control

i s s n：

1049-8923

年卷期：

2024 年 34 卷 13 期

页   码：

9130-9152

页   码：

摘   要：

We introduce an online, model-free algorithm to address the & Laplacetrf;(2) gain optimal tracking problems in discrete linear networked control systems. The algorithm is specifically proposed to handle stochastic information dropout in the feedback loop. Our goal is to design a control law that achieves the system output reference tracking while attenuating the effect of the disturbance input. We first construct an augmented system consisting of the original system and the command generator system. Then, the performance index is expressed in a quadratic form taking into account packet loss. Next, we obtain the optimal solution by solving the dropout generalized algebraic riccati equation (GARE). Finally, a Q-learning algorithm is utilized to estimate the control and disturbance feedback gains of the system, using only measurement data with unknown system dynamics in the presence of dropout. Two algorithms are tested on a numerical example to demonstrate the validity and effectiveness of the proposed methodology.