文章基本信息

标题：Self-Learning Control System Concept for APU Test Cells
本地全文：下载
作者：Razvan Ciobanu ; Adrian Stoicescu ; Cristian Nechifor 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2018
卷号：210
DOI：10.1051/matecconf/201821002009
语种：English
出版社：EDP Sciences
摘要：The proposed concept presents an innovative test cell control system, compatible with an existing APU (Auxiliary Power Unit) test cell. The system is essentially a Non-Propulsive Energy (NPE) Power Management Unit that needs to efficiently distribute power among an aircraft’s pneumatic and electrical loads, based on key parameters read from: loads (electrical, pneumatical), a real APU and real-time models of main engines representative to the aircraft. For this, the concept suggests a hardware & software solution, based on the approach of Artificial Neural Network (ANN). The ANN processes all inputs according to a mathematical law trained from existing data sets, such that minimal power loss is considered, given all safety levels are achieved. Development of the neural network is made such that the fastest response time and best performance consist as general goals, and the resulting control system is tested via Hardware-in-the-Loop simulation. Thus, the neural network is also designed to be safe and stable given maximum performance. The hardware solution describes all the equipment included to fulfil the objectives of the concept.