Anti-Swing Rejection Based on PID Controller Optimized by Firefly Algorithm
Amjed H. Saleh1* , Shuruq A. Al-khafaji2
1 Electrical and Electron department, Faculty of Engineering, University of Al-Qadisiyah, Diwaniyah 00964, Iraq
2 Roads and Transports Department, Faculty of Engineering, University of Al-Qadisiyah, Diwaniyah 00964, Iraq
Corresponding Author Email: amjed.saleh@qu.edu.iq
https://doi.org/10.18280/mmep.100140 ABSTRACT
Received: 2 December 2022
Accepted: 25 January 2023
Gantry crane systems are often used in a variety of industrial applications to move and
raise enormous weights. The process of transferring loads and weights in crane systems
has always occupied the interest of researchers because of its importance in maintaining
the safety of workers on the one hand and preserving the load itself from damage as a
result of its swing and the possibility of it colliding with surrounding objects on the
other hand. This paper’s primary goal is to use a PID controller to regulate the position
and swing of a GCS utilizing genetic algorithms (GA) and firefly algorithms (FA).
Analytical techniques are used in the mathematical model, while Simulink in MATLAB
is used in the PID model. This technique gave excellent results compared to either using
the genetic algorithms (GA).
Keywords:
gantry crane system (GCS), firefly algorithm
(FA), PID controller, genetic algorithms (GA),
Optimization
1. INTRODUCTION
The crane system is an instrument or piece of equipment
that allows you to precisely lift and transfer large goods from
one point to another. Because of the dominating benefits of
high payload capacity, outstanding flexibility, and time–
saving capabilities, a gantry crane system (GCS) is regarded
as one of the most critical equipment in handling heavy load
items in industries. As for the crane’s acceleration and
deceleration during movement, the loads swing back and forth.
As a result, when the GCS is propelled into motion, it is
susceptible to disturbances such as wind, waves, and
environmental distortion [1]. Excessive load swing owing to
crane motion and difficulties in trolley placement on the
correct trajectory with a rapid response time are two of these
challenges [2].
Several gantry crane position and anti-swing control
algorithms have been developed and implemented in several
published publications. For industrial three-dimensional
overhead cranes, [3, 4] suggested a novel fuzzy logic antiswing control. The proposed control ensures both precise
position control and quick load swing damping for the crane’s
simultaneous travel, traverse, and hoisting actions, according
to the experimental findings. To improve the PID parameters
in the gantry crane system, meta-heuristic approaches are used.
To regulate the position and sway of an overhead crane, the
LQR controller’s settings are optimized using a genetic
algorithm [5]. A combination of PID and fuzzy control creates
a stable overhead crane controller [6]. To lower the payload
swing angle, a PID+Q controller was created [7]. Abdullah et
al. [8] propose a Hybrid Control Scheme (HCS) based on
energy balance and fuzzy logic controllers to achieve RIP
swing up and stabilization control. The extended method RRT,
which is asymptotically optimum, is presented by Karaman
and Frazzoli [9]. The paper [10] presents Overhead cranes
were subjected to an adaptive-fuzzy SMC for the robust antisway pursuit under both system uncertainty and actuator
nonlinearity. The FLC parameters are automatically tuned
using meta-heuristic optimization techniques. To increase the
efficacy of FLC design, the scaling factors of FLC are
optimized using three well-known meta-heuristic techniques
[11]. The paper [12] is used to regulate the motion of the
location of the overhead crane utilizing a PID controller and
optimization methods such as Genetic Algorithms (GA) and
Bee Algorithms (BA).
In this work, the proportional integral derivative (PID)
controller parameters for the Nonlinear Gantry Crane System
are modified using the firefly algorithm (FA) and genetic
algorithm (GA). It has been demonstrated in tests that FA is
more potent and performs better than the Genetic Algorithm
(GA). To discover the PID parameters, it has a flexible and
adaptable property. By decreasing or maximizing the factors
involved in the issues, optimization is a method of discovering
the optimal solution to make something as functional and
effective as feasible.