基于模糊神经网络的有源电力滤波器全局滑模控制

Fuzzy neural network based global sliding mode control for active power filter

针对有源电力滤波器电流跟踪控制问题,提出一种基于模糊神经网路的全局滑模控制方法.首先,为了消除到达阶段和抑制抖振,设计准全局滑模控制电流控制器;然后,在考虑参数摄动和传感器故障的情况下,利用元认知模糊神经网络设计基于模糊神经网络的全局滑模控制器,克服全局滑模控制依赖先验知识的缺点.不同于其他固定结构方法,元认知模糊神经网络可以实现结构和参数的在线更新,并利用李雅普诺夫稳定性理论证明所提出的控制策略满足控制目标以及稳定性要求.仿真和实验结果表明,所提出的控制方法在稳态和瞬态运行时都具有良好的性能.

This study mainly focuses on the development of a self-organizing global sliding mode control for active power filters. First, a quasi global sliding mode control method is designed for inner current control loop to eliminate the reaching mode and chattering phenomenon. Then, the control law is constructed based on the meta-cognitive fuzzy neural network(MCFNN) rather than the actual systems to overcome the drawbacks of global sliding mode control. Different from the predefined structure approaches, only necessary data can be extracted to adjust the structure and parameters of the networks in the MCFNN. Subsequently, the Lyapunov stability analysis is presented to satisfy the control objectives and system stability requirements at the same time. Finally, simulation and experimental results demonstrate that the proposed control methods offer superior properties in both steady state and transient operation.