Akademik Veri Yönetim Sistemi
Current Studies on Electrictal-Electronics and Communication Engineering, Hasan ÜZMUŞ, Editör, Bidge Yayınları, Ankara, ss.174-207, 2023
TRMS is a nonlinear system with two degrees of freedoms
(2-DOF). The behavior of TRMS is similar to a helicopter, but the
aerodynamic forces are controlled by varying the speed of the DC
motors. DC motors drive two propellers. The controller of the system
adjusts the amount of voltage supplied to the DC motors to provide the desired values in the yaw and pitch positions. The counter weight
fixed to the beam. This weight provides a stable balance position. In
TRMS, DC motors are named as main motor and tail motor. When
TRMS and helicopter are compared in general terms, it cannot fly
like a helicopter and does not include cyclic control (Chalupa et. al.,
2015; Huu et al., 2016; Castillo et al., 2020). The purpose of the
nonlinear controller methods used for the TRMS system is to
increase robustness of the system by minimizing the effects of
external disturbances such as wind encountered in natural life and
the uncertainty of the TRMS system itself (Zeghlache S et al., 2020).
It also adjusts its position with controllers that increase the
robustness of the system (Tiwalkar et al., 2017). For TRMS methods,
there are metaheuristic methods inspired by herd communities of
animals to find control coefficients such as FOPID and PID (Mihaly
et al., 2021). Algorithms such as particle swarm optimization
algorithm, ant colony algorithm, bee colony algorithm, gray wolf
optimization and dragonfly algorithm are optimization algorithms
based on animal and swarm behaviors (Allouani et al., 2011; Rezoug
et. al., 2014; Meraihi et. al., 2020; Khalaf et al., 2020; Kumar et
al., 2021; Azar et al., 2020; Norsahperi et al., 2020). Traditional
and classical optimization methods are not sufficient for solving
high-dimensional, nonlinear, hybrid problems. Also, these
algorithms are categorized by algorithm type (for example, physicsbased, human based swarm based and evolutionary,) nature inspired
and non-nature inspired, population based, and single solution based.
These metaheuristic algorithms create computational paradigms
used to solve complex optimization problems (Rajabi Moshtaghi et
al., 2021; Abdel-Basset et al., 2018) In this study, the response of
metaheuristic algorithms on a nonlinear system such as TRMS has
been wondered. Therefore, examining the output responses of the
system by trying various algorithms has been our main motivation.
When the performances of different metaheuristic algorithms with
different controllers are compared in detail, the crucial aspects of the
study can be explained as follows: The stability of the system was
tested by examining the performance of metaheuristic algorithms on
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controllers in detail, When the output responses of the yaw and pitch
angles are examined, it has been determined that it is more difficult
to find a controller coefficient for pitch angle than for yaw angle,
Considering the existing algorithms in the literature, it was seen that
they were comparable and applicable with four different
metaheuristic algorithms and three different controller methods. The
fuzzy logic method was found to work with metaheuristic algorithms
to reduce coupling dynamics in pitch and yaw angles. The remainder
of the manuscript is arranged into several sections. Firstly, the
general dynamic structure and mathematical equations of the TRMS
system are introduced. The types of metaheuristic algorithms used
for the TRMS system are presented in section 3. Control methods
and block diagrams are given in detail. Cost functions, performance
analyses, and graphs are given in the following sections with
comparative tables and figures. Finally, results of the study were
interpreted in detail, and suggestions for future scope were
presented.