一种多变量模糊神经网络解耦控制器的设计

Design of Multivariable Fuzzy-neural Network Decoupling Controller

为提高多变量、非线性和强耦合系统的动态特性和解耦能力,根据解耦原理和神经网络思想,提出一种两级串联结构的自适应模糊神经网络解耦控制器.前级是基于智能权函数规则的自调整模糊控制器,后级是基于动态耦合特性的自适应神经网络解耦控制器.同时从理论上证明了学习算法的收敛性.仿真实例表明,所提出的解耦控制器具有良好的鲁棒性和自适应解耦能力,是解决多变量、非线性和强耦合问题的一种简便有效的控制算法.

To improve the dynamical property and decoupling capability for a class of multivariable nonlinear systems with strong coupling, based on the principle of decoupling and neural network, a cascade-connected self-adaptive fuzzy-neural network decoupling controller is proposed. The former is a self-tuning fuzzy controller by using the intelligent weight function rulers, and the latter is a self-adaptive neural network deeoupling controller based on the learning algorithm of dynamical coupling characteristic. The convergence of this self-learning algorithm is theoretically proved. The simulation results show that the proposed controller is of perfect robustness and self- adaptive decoupling control properties.