基于多模型的非线性系统自适应最小方差控制

对于一类典型的离散时间非线性系统, 提出了一种基于多模型的自适应最小方差控制方法. 通过在平衡点附近建立线性模型, 用径向基函数神经元网络来补偿建模误差和未建模动态, 形成了非线性系统的多模型表示. 采用了具有积分性质的切换指标函数作为切换法则和最小方差的控制方法构成了多模型自适应控制器. 仿真实验的结果表明了这种方法的有效性.

Multiple model based adaptive minimum variance control of nonlinear system

A multiple model based adaptive minimum variance control is provided for a nonlinear discrete time system that is subject to multiple operating regimes. The RBFNN, i.e. radial basis function neural network, is used to approximate the nonlinear unmodeled error of the local linear model at different equilibrium operating point. And the nonlinear system is modeled by the multiple linear models and neural network at different equilibrium operating point. A switching function with integral property and minimum variance algorithm are used to set up the multiple model adaptive controller. From the result of simulation, it can be seen that the controller proposed in this paper can give a better control performance for nonlinear system.