基于自适应模型非线性系统容错控制的研究

Adaptive Model-based Active Tolerant Control for Nonlinear Process

针对未知的多变量非线性动态系统,提出了一种新型的自适应故障容错控制方法。该方法首先通过设计一个自适应RBF神经网络模型建立未知过程的动态模型,并利用扩展卡尔曼滤波算法在线调节RBF网络权值学习系统的时变参数和故障动态,然后设计基于此模型的自适应迭代逆模控制算法实现相应的容错控制策略。将容错方法成功应用到一个连续的多变量三水箱过程。

A new Fault Tolerant Control （FFC） scheme based on the inversion of adaptive RBF neural network model for unknown multi-variable dynamic systems was proposed. An adaptive RBF model was designed to build process model and was adapted on-line by using Extend Kalrnan Filter （EKF） technique to learn fault dynamics caused by component faults. Then, an inversion of the RBF model was used to the adaptive controller to maintain the system performances after fault occurrence. The proposed scheme was applied to a multiple variable three-tank process to demonstrate the performance of the scheme.