论文部分内容阅读
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．
The principle and simplified linearization model of the magnetic suspension bearing system are briefly described. The principle and main components of the control system based on the simplified model and the linear control theory are described. The performance limits of the magnetic suspension bearing system based on the simplified model and the linear control theory Sex. On this basis, the nonlinear recurrent neural network is used to model and control the magnetic suspension system, and the learning problems of the neural network in practice are discussed. It avoids the influences of the nonlinearity and uncertainty of the magnetic levitation system on the system performance and has strong robustness and greatly improves the performance of the magnetic levitation system.