Multivariable internal model adaptive decoupling controller with neural network for nonlinear plants

Combining neural network identification technique and internal model control (IMC) strategy, a novel nonparametric design for nonlinear plants is presented. Based on this idea, a multivariable adaptive decoupling internal model controller (DIMC) is developed to deal with multivariable nonlinear coupling systems with unknown structure and parameters. A neural network is used to detect the unknown nonlinear internal model. One advantage is that the design does not require the computation of the inverse model of the IMC parameters, but only depends on system input-output data and neural network output