光伏发电伺服系统数字动态面智能控制策略

Digital Dynamic Surface Intelligent Control Strategy of Photovoltaic Power Generation Servo System

针对光伏发电伺服系统提出一种自适应数字动态面智能控制算法。通过结合径向基函数(radial basis function,RBF)神经网络技术,克服传统反步法中的“微分爆炸”问题,同时降低对被控系统结构的特殊要求,使得控制律设计过程变得更加简洁且易于实现。通过引入改进的磁滞量化器对控制信号幅值进行量化,减轻对数量化控制器中的抖振现象,实现数字控制。利用Lyapunov方法证明了闭环系统的一致最终有界稳定。通过StarSim Modeling Tech电力电子仿真实验平台以及光伏伺服系统对控制方案进行实验验证。实验结果表明了所提控制方案的可行性。

This paper proposed an adaptive digital dynamic surface intelligent control(DSC) algorithm for photovoltaic power generation servo system. By combining radial basis function(RBF) neural network technology, the "differential explosion" problem in traditional backstepping method was overcome and the special requirements for the structure of the control system were released. Therefore, the procedures of the controller design have become more concise and implementable. By quantizing the amplitude of control signal with modified hysteretic quantizer, the chattering phenomenon in logarithmic quantizer was reduced and the pure digital control objective was realized. The ultimately uniformly bounded property of the closed-loop system was proved by designing the Lyapunov functions. The experimental verification was carried out through the StarSim Modeling Tech simulation platform and photovoltaic servo system. The experimental results can demonstrate the feasibility of the proposed control scheme.