Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one fathers this journal and its founding Editor-in-Chief till 2021. The addresses performance improvement three Single Input-Single Output (SISO) fuzzy control systems that separately positions interest tower crane systems, namely cart position, arm angular position payload position. Three separate low-cost SISO controllers are employed in terms first order discrete-time intelligent Proportional-Integral (PI) with Takagi-Sugeno-Kang Proportional-Derivative (PD) terms. Iterative Learning Control (ILC) system structures PD learning functions involved current iteration ILC structures. Optimization problems defined tune parameters functions. objective as sums squared errors, they solved domain using recent metaheuristic Slime Mould Algorithm (SMA). experimental results prove after ten iterations SMA.