Learning Control for Networked Stochastic Systems With Random Fading Communication
研究了输出和输入数据通过多个独立衰落信道传输的网络化随机系统的学习控制策略，改进了传统P型学习控制方案，证明了在随机衰落和系统噪声下输入误差随迭代次数增加收敛到零。
The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning gain is replaced by a variable one to suppress the effect of various uncertainties. Strong convergence of the proposed scheme is established under random fading phenomena and system noise. The input error is shown convergent to zero as the cycle number increases. Two numerical examples demonstrate the applications of the proposed scheme.