Accelerated and Adaptive Power Scheduling for More Electric Aircraft Via Hybrid Learning

Abstract

In recent studies, the power scheduling in more electric aircraft (MEA) has been formulated as a mixed-integer quadratic programming problem. Many model-driven methods, such as branch-and-bound algorithms, are advocated to solve it. However, these methods are often prone to considerably high complexities, which makes real-time processing a problem. In this article, a two-stage hybrid learning-based optimization approach is proposed to address this challenging issue. In the first stage, the task of optimizing integer variables is considered as a multilabel classification problem, which is solved by a data-driven method, i.e., ensemble deep neural networks (EDNNs). In the second stage, with the obtained integer solutions, the problem is transformed into a quadratic programming problem that can be quickly solved by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model-driven</i> numerical optimization methods. Compared to the state-of-the-art power scheduling algorithms, the model-driven and data-driven methods can compensate each other so that this two-stage hybrid learning-based approach can achieve orders of magnitude speedup in computational time, while guaranteeing the optimal scheduling performance. The structures of EDNNs are well designed so that the proposed approach can adapt to varying operating conditions in MEA. An offline simulation test and an online hardware-in-the-loop test validate the above advantages of the proposed approach.