Point-to-Point Learning Tracking Control via Fading Communication Using Reference Update Strategy
针对网络通信中的信号衰落问题，提出一种参考更新策略，使点对点跟踪性能随迭代次数增加而持续提升，并证明了均方和几乎必然收敛性。
The networked structure has attracted significant attention due to high demand for industrial systems and rapid developments of network communication. Among various network randomness, fading is a common phenomenon, which can lead to signal attenuation, distortion, loss, and interference. This study concentrates on the point-to-point tracking problem via fading communications by proposing a reference update strategy. Using this strategy, the tracking performance is continuously improved even with faded information as the number of iterations increases. A learning control scheme is established and proved convergent in both mean-square and almost-sure senses under mild conditions. The convergence rate is accelerated by introducing the virtual reference compared with the traditional update approach. Illustrative simulations verify the theoretical results.