CAREER: Intelligent Control Systems for Active Magnetic Bearings: An Enabling Technology for Flywheel Energy Storage Systems

本项目将探索使用新型的基于学习的智能控制来应对主动磁轴承(AMBS)控制的挑战。如果能够更有效地控制它们，AMBS有可能使用先进的飞轮储能系统显著提高混合动力汽车的效率；这反过来可能显著缓解世界向更高油价的过渡。众所周知，为了实现这一切，更有效地控制AMBS的挑战是需要解决的主要障碍。开发类似大脑的学习系统--像有机大脑一样输出控制或动作的系统--是普遍的基础科学重要性。AMB控制将被用作一个试验台，用于与更传统的设计相比，比较更类似大脑的设计的能力，并研究现有类似大脑的设计的特点，以期进行可能的改进。由于AMB问题是非常非线性的，对于传统方法来说是困难的，因此新方法很有可能(在经过大量分析后)表现得更好，并且该项目的结果可能有助于将困难的范式转变为基于学习的非线性方法。

This project will explore the use of novel learning-based intelligent control to meet the challenge of controlling Active Magnetic Bearings (AMBs). If they can be controlled more effectively, AMBs have the potential to make it feasible to use advanced flywheel energy storage systems to signigicantly improve the efficiency of hybrid electric vehicles; this, in turn, could measurably ease the world's transition to higher oil prices. The challenge of controlling AMBs more effectively is well-known to be the main obstacle which needs to be addressed, in order to make this all happen.The development of brain-like learning systems-systems which output contols or actions, as organic brains do-is general fundamental scientific importance. AMB control will be used as a testbed for benchmarking the capabilities of more brain-like designs compared with more traditional designs, and for studying the characteristics of existing brain-like design with an eye to possible improvements. Because the AMB problem is very nonlinear and difficult for traditional methods, there is a significant possiblity that novel methods will perform better (after considerable analysis) and that results of this project could assist in the difficult paradigm shift to learning-based nonlinear approaches.