基于模糊神经分数阶PI2Dμ 的储能电源逆变控制

针对传统PID控制器存在抗干扰能力差、参数整定困难、时变控制不确定等不足，为进一步提高储能电源逆变输出电压波形质量，增强控制系统的鲁棒性，提出了基于优化模糊PID控制器的控制策略。在介绍分数阶控制器定义及其数学实现的基础上，该方法引入模糊控制规则对控制器的结构参数进行调整，同时融合了神经网络的自学习能力，通过在系统工作时动态调整隶属函数和完善模糊控制规则，实现控制器参数的在线调节和优化。仿真结果表明，优化后的控制器具有更灵活的结构和更强的鲁棒性，具备良好的动态特性和自适应能力，能够满足储能电源逆变控制的要求。

Control strategy for energy storage system inverter based on fuzzy neural network controller

The traditional PID controller has a shortage of an-interference performance, parameters setting and time-varying control. In order to improve the output-voltage performance of the inverter and robustness of battery energy storage system power supply, this paper puts forward a control method based on optimized fuzzy PID controller. Based on expounding the definition and math algorithm of fractional order controller, the method introduces fuzzy control rules to adjust the parameters of the controller, meanwhile, combines with the self-learning ability of the neural network to adjust and optimize the controller parameters online by improving the membership function and fuzzy control rules constantly. The simulation results show that the quality of anti-interference and robust of the optimized control system are enhanced, the controller has better construction and dynamic performance, above all, the optimized control methods are feasible for the inverter of battery energy storage system.