Intelligent adaptive control of forces in milling processes

Intelligent schedules has gained attention in manufacture environments due to increase competence. In this paper an intelligent adaptive discrete control is applied to a practical milling system in order to minimize process malfunctions. Two hierarchical supervisory levels compose the control: tuning and switching. The continuous unknown milling transfer function is discretized under a set of fractional order hold of correcting gains beta epsi [-1,1] (beta-FROH) running in parallel. Each discrete plant parameter is tuned with a recursive least square algorithm. The correcting gain beta of (beta-FROH )is switched within the given set in order to generate the optimal real control input to the plant through the minimization of a estimated error tracking performance index which evaluates the tracking error. The intelligent supervisory scheme chooses online the one with the smallest value has at each multiple of the residence time.