Optimizing iterative learning control of cyclic production processes with application to extruders

Conventional and new methods for the control of cyclic processes are described and compared on the basis of their performance results achieved in an aluminum extruder plant. The thrust of the work lies in the area of iterative learning control systems. After a brief description of (linear) iterative learning control, the optimizing iterative learning control of cyclic processes is presented. In this method the control input is adjusted from cycle to cycle such that a prescribed quantitative performance index is made to take on an extremum. The results which the presented methods of cyclic control yield when applied to a simulation model of an aluminum extruder are compared with one another. Finally, results obtained in an actual industrial extruder plant are given. The new method yields an increase of production by 10% as compared to methods in current use