ELECTRONICS (Basel), cilt.13, sa.16, ss.1-16, 2024 (SCI-Expanded)
Metaheuristic algorithms are computational techniques based on the collective behavior
of swarms and the study of organisms acting in communities. These algorithms involve different
types of organisms. Finding controller values for nonlinear systems is a challenging task using
classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multiinput multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems
to be more appropriate than using classical methods. In this study, different types of metaheuristic
algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm
(GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm,
and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based
on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS.
The experimental results show that when the rise and settlement times of the TRMS are compared in
terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the
pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s
to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the
COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s
for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in
this context.