磁浮支承系统的神经网络建模与控制

简述了磁悬浮支承系统的原理和简化的线性化模型,以及基于该简化模型和线性控制理论的控制系统原理、主要组成,并阐述了这种基于简化模型和线性控制理论的磁悬浮支承系统性能极限性.在此基础上,采用非线性递归神经网络对磁悬浮支承系统进行建模与控制,并针对实际应用中神经网络的学习问题进行了讨论.避免了磁悬浮系统的非线性和不确定性等因素对系统性能影响,并具有较强鲁棒性,大大提高了磁悬浮系统的性能.

Modeling and Control of Magnetic Bearing System Using Neural Networks

The principle and its approximative linear modeling of magnetic bearing system are given. The linear controller based on linear control theory and simplified modeling is developed, which results in the performance degradation and limitation of magnetic bearing system. The method of modeling and control of magnetic bearing system using nonlinear recurrent neural networks is studied,and the learning algorithm combined with the practical application of the magnetic bearing system is also developed. The proposed method restrains the effects of nonlinearity and uncertainty on the performance of magnetic bearing system,and has the ability of robustness,which improve the system performance greatly.