Event-Triggered Sampled-Data Iterative Learning Control for Fractional-Order Cyber-Physical Systems

This paper investigates the output synchronization of fractional-order cyber-physical systems (FOCPSs) under communication constraints. To address limited bandwidth and high transmission costs, an event-triggered encoding-decoding sampled-data iterative learning control (ET-EDSDILC) protocol is proposed. The control law integrates a quantized sampling framework with an encoding–decoding mechanism to reconstruct control signals and address communication constraints. Furthermore, an event-triggered mechanism based on error energy attenuation (EEA) is developed to adjust communication frequency by monitoring error trends, thereby reducing unnecessary data transmissions. By applying fractional-order calculus and the contraction mapping principle, sufficient conditions for output synchronization are derived. Numerical simulations show that the proposed ET-EDSDILC framework reduces communication overhead and data redundancy while maintaining tracking performance, offering a solution for FOCPSs under communication constraints.