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This paper shows the viability of implementing a control strategy based on the internal-model control paradigm, which is a useful synergy of a dynamic ANN trained from real-life data and used to predict process output and a fuzzy-logic control (FLC) that enhances the control system's overall performance. A force control problem involving a complex electromechanical system, represented here by the machining process, is considered as a case study. The main goal is to control a single-output variable, cutting force, by changing a single-input variable, feed rate. The proposed neurofuzzy-control (NFC) 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. Four approaches, the fuzzy-logic controller (FLC), the direct inverse controller based on ANNs (DIC-NN), the internal-model controller (IMC-NN) and a neurofuzzy 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 the IMC-NN and the FLC for the cases analyzed. |
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