电磁永磁混合悬浮系统的神经元PID控制

悬浮控制是磁悬浮系统的关键问题之一，其牵引系统必须在稳定悬浮的基础上设计。为了降低悬浮的能量损耗，在本文的悬浮系统中加入了永磁磁极。提出了结合神经控制和传统PID控制的混合悬浮系统的神经元PID控制策略，该策略有很好的在线学习能力，自适应神经元通过自学习和相关搜索方法．用于调节PID控制器的参数，实现实时性能优化。所提出的控制策略经实验验证，在未知数学模型的情况下，可实现快速、精确和稳定的悬浮。

Neuron-PID based Levitation Control for the Hybrid Maglev System

The levitation control scheme is the key problem of the maglev system, and the propulsion system has to be designed on the base of stable levitation.To lower the suspension power loss,permanent magnets are added into the levitation system to assistant the magnetic coil.Novel scheme combined neuron control with the conventional PID control is proposed for the hybrid levitation system,which has good online learning ability.The adaptive neuron is used to regulate the parameters of the PID controller by self-learning and associative searching method,the real-time performance is optimized. The suggested strategy is verified by experiments, which is model-free, and can realize fast, precise and stable suspension.