Nonlinear internal model control using neural networks: an application for machining processes
This paper presents a novel approach to the control of the cutting force on the basis of the internal model control (IMC) principle. The main goal is to control a single output variable, the cutting force, by changing a single input variable, the feedrate. A neural model is used as an internal model to determine the control inputs (feedrate) necessary to keep the cutting force constant. Three approaches, the fuzzy logic controller (FLC), the direct inverse controller (DIC) and the IMC, based on artificial neural networks (IMC-NN), are simulated and their performances are assessed in terms of several performance measurements. The results demonstrate that IMC-NN strategy provides a better disturbance rejection than FLC for the cases analysed.
internal model control direct inverse control feedforward networks fuzzy control multi-layer perceptron