Adaptive neural internal model control for complex process with delay

This paper adopts the adaptive neural internal model control strategy to design a control system for complex and uncertain process with delay. Because of the shortage of the classic internal model control system the performance of the control system will slip back if there exist large errors between the real plant and the model, an adaptive mechanism based on single neuron which can tune the parameters of the internal model and that of controller in the control system on line is designed. The multimodel approach is exploited to represent the direct model by a model basis made up of five linear models, and to construct a controller trained by the corresponding reverse models. The simulation results shows that the adaptive internal model control system designed in this paper has good performance of overcoming deviations of model parameters.