Framework for implementation of iterative learning control on programmable logic controllers

Abstract

In this paper, an implementation approach for norm-optimal iterative learning control (ILC) on programmable logic controllers (PLCs) is presented. After a detailed conceptual overview and discussion of the norm-optimal ILC algorithm, the challenges for implementing ILC algorithms on PLCs are discussed and an efficient three-phase implementation approach is proposed. Here, the three phases consist of an offline calculation, the calculation of the feedforward part between consecutive iterations, and the online calculation of the current control input. It is also shown that this separation enables the efficient implementation of the norm-optimal ILC algorithm on standard industrial controllers like PLCs. The proposed norm-optimal ILC implementation approach is verified by a simulation of a gantry robot with three degrees of freedom, where the norm-optimal ILC algorithm is executed within a Soft-PLC.