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

该项目将探索使用新颖的基于学习的智能控制来应对控制主动磁轴承（AMBs）的挑战。如果能够更有效地控制它们，amb有可能使使用先进的飞轮储能系统显着提高混合动力电动汽车的效率成为可能；反过来，这可能会显著缓解全球向高油价过渡的过程。众所周知，更有效地控制AMBs的挑战是需要解决的主要障碍，以实现这一切。类脑学习系统——像有机大脑一样输出控制或动作的系统——的发展具有普遍的基础科学重要性。AMB控制将被用作测试平台，以比较更像大脑的设计与更传统的设计的能力，并研究现有的像大脑的设计的特点，并着眼于可能的改进。由于AMB问题非常非线性，传统方法很难解决，因此新方法很有可能表现得更好（经过大量分析），而本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.