Fictitious Reference Iterative Tuning of Intelligent Proportional-Integral Controllers for Tower Crane Systems

The current paper introduces a hybrid data-driven control algorithm obtained by the mix of two data-driven algorithms, namely intelligent proportional-integral controllers as representative Model-Free Control (MFC) algorithms, whose parameters are optimally tuned via Fictitious Reference Iterative Tuning (FRIT) algorithms using metaheuristic Slime Mould Algorithm. The purpose of the current mix is to combine the advantages of MFC and FRIT. The efficiency of the novel data-driven algorithms is proved using real-time experiments by controlling the 3 degrees of freedom tower crane system equipment.