Hybridization schemes for particle swarm iterative learning controllers in repetitive systems

Two novel hybridization schemes for the particle swarm repetitive controller (PSRC) are developed and tested herein. They are designed to be used in power electronic converters, such as grid-tie converters or constant-amplitude constant-frequency (CACF) true sine wave inverters, as well as in motion control systems, especially in assembly line robots. The proposed control algorithms combine the good responsiveness of the classic deterministic iterative learning controller (ILC) with the robustness offered by the evolutionary population-based swarm controller. Properties of the control algorithms are illustrated in the CACF inverter using computer simulations. One out of the schemes is then employed in the three-phase grid-tie converter to facilitate sinusoidal currents under the distorted grid voltage conditions. A weakness not manifested in the case of the CACF inverter, however clearly apparent in the case of the grid-tie converter if envisaged to interface a superconducting magnetic energy storage (SMES), is identified. The concept of an adaptive evaporation rate for the PSRC is then investigated to enhance the performance of the original hybrid repetitive controller. This is to facilitate the controller's future deployment in the power electronic interface for an SMES system where learning should last at most a few seconds instead of tens of seconds.