Point-to-Point Learning Tracking Control via Fading Communication Using Reference Update Strategy

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.