基于情感神经网络的有源电力滤波器智能终端滑模控制

为了增强有源电力滤波器的电流跟踪控制性能,提出一种基于连续径向基情感神经网络的递归终端滑模控制方案.首先介绍包括集总不确定的有源电力滤波器数学模型;然后构造递归终端滑模面,该滑模面由快速非奇异终端滑模面和递归积分终端滑模面组成,不仅可确保跟踪误差在有限时间内收敛到零,而且可通过为滑模面参数设置适当的初始值消除滑模面的到达模态.为了有效克服系统不确定因素的影响,采用连续径向基情感神经网络逼近系统不确定参数,并运用Lyapunov方法对其进行稳定性和收敛性分析.所设计的连续径向基情感神经网络,不仅结构简单、响应速度快,而且具备参数在线调节能力.仿真和实验结果均表明,该控制方案具有优异的电流跟踪能力以及抗干扰能力.

Emotional neural networks based intelligent terminal sliding mode control for active power filter

In order to enhance the current tracking control performance of the active power filter, this paper proposes an intelligent terminal sliding mode control scheme based on a continuous radial basis emotional neural network. Firstly, the mathematical model of an active power filter including lumped uncertainty is introduced. Then a recursive terminal sliding surface composed of a fast non-singular terminal sliding surface and a recursive terminal integral sliding surface is constructed, which can not only ensure that the tracking error converges to zero in a finite time, but also eliminate the arrival stage of the sliding surface by setting appropriate initial values for the sliding surface parameters. In addition, in order to effectively deal with uncertainty, a continuous radial basis emotional neural network is utilized to approximate the uncertain parameters of the system, and its stability and convergence is also ensured using the Lyapunov method. The designed continuous radial basis emotional neural network is not only simple in structure, fast in response, but also has the ability to adjust parameters online. Simulation and experimental results show that the control scheme has excellent current tracking ability and anti-interference ability.