Adaptive frequency regulation of an LPG generator using an assistive model-free iterative learning controller - Inov Ivandany; Arya Kusumawardana; Muhammad Afnan Habibi; Muhammad As'ad Sahroni (Journal of Mechatronics, Electrical Power, and Vehicular Technology, 2026)
Mechanical speed governors in small generator sets often provide only coarse frequency regulation, leading to steady-state error and poor transient recovery under load disturbances. To address this limitation, this study proposes a hybrid governor for an (liquefied petroleum gas) LPG-converted generator, in which the built-in mechanical governor is retained as the primary stabilizing layer, and a model-free iterative learning control (ILC) is added as an assistive electronic controller. The proposed method was validated experimentally under dynamic multi-step load disturbances and internal parameter shifts. In the dynamic load test, the proposed hybrid ILC achieved the lowest root mean square error (RMSE) of 0.9144 Hz, compared with 0.9581 Hz for the (proportional-integral) PI-controller benchmark and 1.5512 Hz for the mechanical governor. This corresponds to an RMSE improvement of 41.05 % relative to the mechanical governor and 4.56 % relative to the PI-controller benchmark. In terms of relative tracking accuracy, both electronic controllers substantially reduced the mean absolute percentage error (MAPE) relative to the mechanical governor, with the proposed hybrid ILC achieving the lowest value of 1.14 %, slightly lower than 1.15 % for the PI-controller and much lower than 2.04 % for the mechanical governor. Under internal parameter detuning, the proposed method maintained better regulation performance, with RMSE improvements reaching 79.68 % relative to the mechanical baseline. These results show that the proposed hybrid model-free ILC improves transient response, tracking accuracy, and robustness, while preserving the original mechanical governor as a practical baseline controller.