In this paper, an adaptive neural network (NN) output feedback control is investigated for incommensurate fractional-order permanent magnet synchronous motors under the condition of input saturation. First, a NN state observer is presented to obtain the ‘virtual estimate’ of angle speed, where the unknown function is approximated by the NN. Then, in order to solve the input saturation problem, an auxiliary system is developed under fractional-order framework. Next, the command filtered technology with an error compensation mechanism is used to handle the ‘explosion of complexity’ in backstepping and remove the filtering errors. In addition, the frequency distributed model is utilized such that the Lyapunov theory is available in the backstepping design and the system stability is demonstrated. Finally, numerical simulations confirm the availability of the proposed design.

本文研究了输入饱和条件下不等分数阶永磁同步电动机的自适应神经网络输出反馈控制。首先，提出了一个NN状态观测器以获得角速度的“虚拟估计”，其中未知函数由NN近似。然后，为了解决输入饱和问题，在分数阶框架下开发了一个辅助系统。接下来，具有错误补偿机制的命令过滤技术用于处理反步中的“复杂性爆炸”并消除过滤错误。另外，利用频率分布模型，使得Lyapunov理论在反推设计中可用，并证明了系统稳定性。最后，