本文提出了一种控制磁悬浮系统（磁悬浮）不确定性的方法。为了实现精确的控制系统，基于神经网络的智能控制系统，提出补偿的不确定性。首先，磁悬浮系统的动态模型被建立。第二，滑动模式控制（SMC）被用于补偿发生在磁悬浮系统中的不确定性。所施加的控制器保证了系统的稳定性。此外，为了增加坚固性和释放约束的不确定性的要求，使用径向基函数网络（SMCRBFN）的滑动模式控制的建议。的成效，通过仿真和实验结果验证
This thesis presents an approach to control the magnetic levitation system (Maglev) with uncertainty. To achieve precise control system, a neural network based intelligent control system was proposed to compensate the uncertainties. First, the dynamic model of a magnetic levitation system was built. Second, a sliding mode control (SMC) was applied to compensate the uncertainties that occurred in the magnetic levitation system. The applied controller guarantees the stability of the system. Moreover, to increase the robustness and to release the requirement of the uncertainty bound, a sliding mode control using a radial basic function network (SMCRBFN) is proposed. The effectiveness was verified through the simulation and experimental results.
