Open AccessPerformance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems
Author(s) -
Radu Precup,
RaulCristian Roman,
ElenaLorena Hedrea,
ClaudiaAdina BojanDragos,
Miruna-Maria Damian,
Monica-Lavinia Nedelcea
Publication year - 2022
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2022.1.4623
Subject(s) - control theory (sociology) , payload (computing) , position (finance) , computer science , fuzzy logic , tower , iterative learning control , fuzzy control system , mathematical optimization , mathematics , control (management) , artificial intelligence , engineering , computer network , civil engineering , finance , network packet , economics
This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.