Data-based optimisation of control parameters

Abstract

As control structures become increasingly complex, it becomes more difficult and time-consuming to determine and apply optimum parameters. This paper presents a data-driven approach for the self-tuning of existing control structures using iterative learning control (ILC). This makes it possible to determine the control parameters quickly and effectively. The performance and effectiveness of the proposed method are demonstrated using a simulation model of a one stage turbocharged internal combustion engine with wastegate.