A periodic iterative learning scheme for finite-iteration tracking of discrete networks based on FlexRay communication protocol

Abstract In this study, the finite-iteration tracking of discrete networks is analyzed by designing a new kind of periodic iterative learning control (ILC) strategy. In the proposed ILC scheme, the FlexRay communication protocol with both static and dynamic segments is introduced in the design. The design has the advantage to reduce the communication channel bandwidth load and hence improve the performance of finite-iteration tracking. This paper provides the first integrating approach to iterative learning design based on the dynamic capability of FlexRay communication channels, and can be generalized to other learning process in network control design. Simulation results are shown to clarify the effectiveness of the obtained criteria, and demonstrate that the proposed periodic ILC scheme performs better than the traditional ILC schemes.