基于数据学习逆模糊模型的自适应逆模糊控制

Adaptive Fuzzy Inverse Control Using Data-Based Learning Fuzzy Inverse Model

针对在逆模糊模型控制中出现的在线滚动数据窗口计算量大和控制模型精度低等问题,提出了基于数据逆模糊学习算法,并将该算法运用到建立逆模糊模型中。首先利用建模数据在时间与空间相邻的特点,从系统积累的数据中找出与当前模态相匹配的输入数据,在保证控制模型精度的同时大大减少了计算量,然后采用自适应算法在线调节系统模型参数,实现非线性系统的实时跟踪控制。该方法提高了系统控制精度与计算效率。仿真结果证明了该方法的有效性。

According to online rolling data window calculation and control model of low accuracy appeared in inverse fuzzy model control,present a data-based fuzzy learning algorithm and apply it to establish inverse fuzzy model.Firstly use the modeling data＇s relation in time and space for finding the data which matches with the current model form the accumulated data of system.In guaranteeing the accuracy of the control model greatly reduce the computation.Then the adaptive algorithm adjusting on-line system model parameters is used to achieve the tracking control of nonlinear system.The system control precision and computational efficiency is improved by this method.The simulation results demonstrate that the method is effective.