Neural Network based Weighting Factor Selection of MPC for Optimal Battery and Load Management in MEA

This paper presents a Neural Network (NN)-based weighting factor (WF) selection method for the multi-objective cost function in Model Predictive Control (MPC). MPC is adopted for scheduling the loads and charging/discharging the battery intelligently on More-Electric Aircraft (MEA) in a preferred manner. The decisions which are made while the MPC is running utilize a cost function which weights together different objectives (using WFs). The final overall evaluation is performed by considering various objectives with full knowledge of what happened throughout the whole operation, which are weighted together by utilising weights appropriate to the user. The WFs utilized by the MPC to get the best overall result will usually differ from the weights used in the final evaluation. A NN is trained to predict the effects of different combinations of WF values, facilitating optimisation to find the minimum evaluation index, i.e. the most suitable weighting factors for the applied MPC.