Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Control System Design
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
IEEE Transactions on Neural Networks
Controlling a complex electromechanical process on the basis of a neurofuzzy approach
Future Generation Computer Systems
Controlling a complex electromechanical process on the basis of a neurofuzzy approach
Future Generation Computer Systems
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This study explores the use of the internal-model control (IMC) paradigm using artificial neural networks (ANNs) and fuzzy logic (FL) to consider a force-control problem involving a complex electromechanical system, represented here by the machining process. The main goal is to control a single output variable, cutting force, by changing a single input variable, feed rate. This scheme consists of a dynamic model using ANNs to estimate process output and a fuzzy-logic controller (FLC) with the same static gain as the inverse model to determine the control inputs (feed rate) necessary to keep the cutting force constant. Three approaches, the fuzzy-logic controller (FLC), the internal-model controller (IMC) and a neuro-fuzzy controller (NFC), are simulated and their performances are assessed in terms of several performance measurements. The results demonstrate that the NFC strategy provides better disturbance rejection than IMC and FLC for the cases analysed.