Spotted Hyena Optimizer enhances the performance of Fractional-Order PD controller for Tri-copter drone

Alaq F. Hasan; Nabil Al-Shamaa; Suha S. Husain; A. J. Humaidi; Ayad Al-dujaili

doi:10.1556/1848.2023.00659

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International Review of Applied Sciences and Engineering

Volume/Issue:: Volume 15: Issue 1

Spotted Hyena Optimizer enhances the performance of Fractional-Order PD controller for Tri-copter drone

Authors:

Alaq F. Hasan

Alaq F. Hasan Technical Engineering College, Middle Technical University, Baghdad, Iraq

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alaaqf.hasan@mtu.edu.iq

Nabil Al-Shamaa

Nabil Al-Shamaa Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

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nabilshamaa59@gmail.com

Suha S. Husain

Suha S. Husain Department of Construction and Projects, University of Technology Baghdad, Iraq

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11698@uotechnology.edu.iq

A. J. Humaidi

A. J. Humaidi Control and Systems Engineering Department, University of Technology, Baghdad, Iraq

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amjad.j.humaidi@uotechnology.edu.iq https://orcid.org/0000-0002-9071-1329

, and

Ayad Al-dujaili

Ayad Al-dujaili Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

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ayad.qasim@mtu.edu.iq https://orcid.org/0000-0002-1126-3290

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Pages:: 82–94

Online Publication Date:: 31 Oct 2023

Publication Date:: 22 Jan 2024

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2023.00659

Keywords:: fractional order proportional-derivative controller; spotted hyena optimization algorithm; particle swarm optimization; Tricopter drone

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Abstract

In this study, nonlinear control design is presented for trajectory tracking of Tricopter system. A Fractional Order Proportional Derivative (FOPD) controller has been developed. The performance of controlled Tri-copter system can be enhanced by suggesting modern optimization technique to optimally tune the design parameters of FOPD controller. The Spotted Hyena Optimizer (SHO) is proposed as an optimization method for optimal tuning of FOPD's parameters. To verify the performance of controlled Tricopter system based on optimal SHO-based FOPD controller, computer simulation is implemented via MATLAB codes. Moreover, a comparison study between SHO and Particle Swarm Optimization (PSO) has been made in terms of robustness and transient behavior characteristics of FOPD controller.

Abstract

1 Introduction

The absence of onboard pilot is the main feature of Unmanned Aerial Vehicle (UAV), which can fly autonomously for remote distances by controlling the aerodynamic forces. Recently, these UAV have been applied in different applications like surveillance, saving, monitoring, fire-extinguishing, mailing, fishing, and aerial photographing. One of the famous types of UAV is the vertical takeoff and landing (VTOL) aircraft. The VTOL is supported by multi-rotors and it can actuate one or more than rotor such that to achieve desired tasks. It has different modes of flight including vertical takeoff, hovering, lateral motion and landing. It is capable of making transition from one mode to another [1–4].

Another configuration of multi-rotor aircraft, called “Tricopter”, has recently appeared. This special UAV includes three propellers which are actuated by three rotors. The Tri-rotor VTOL has two types. In the first type, the arms of UAV are equipped with three coaxial rotors, while the other type of this special aircraft has three single rotors. In the first type, one can eliminate, for example, the yaw motion by actuating the propellers of each twin in opposite directions [5]. On the hand, the yaw motion in the second type of vehicle can be changed by changing the rotation direction of the actuating motors. The motion and orientation control of vehicle orientation is the task of the proposed controller. However, the stabilization of yaw moment for the UAV is the main goal of the controller in both configurations [6]. Figure 1 demonstrates a simple sketch of the tri-copter aircraft. The figure shows three forces $f_{1}$ , $f_{2}$ and $f_{3}$ which are produced due to rotation of corresponding propellers. The angular position $μ$ represents the angular deviation between the z-axis and the vector of generated force by propeller 1. For more details on the control modes of motion and orientation for this special UAV, one can consult previous and relevant work [6].

Fig. 1.

Sketch of Tri-copter aircraft

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

The tri-copter aircraft is underactuated system and it is characterized by high nonlinearity and high cross-coupling effect due to aerodynamics structure and the presence of three propellers (rotors). In addition, the Tri-copter is sensitive to disturbance of wind gust, especially at low height, which in turn results in degradation of aircraft performance and even leads to instability problems [3]. Accordingly, robust and nonlinear controller is required to achieve good dynamic characteristics and trajectory tracking performance. In what follows, the literature review has presented brief explanations of previous control schemes related to control of Tri-copter UAV.

Z. A. Ali et al. [7] have designed hybrid adaptive control scheme, which consists of RST control, pole-placement and fuzzy regulation to control the height and orientation of Tricopter. The adaptive gains of fuzzy logic controller (FLC) are used to tune the RST controller. The FL-based RST control showed better tracking error and robust characteristics than conventional RST control. In [8], A. Prach and E. Kayacan combined both the linear model predictive controller (MPC) and the classical PID controller for controlling the position of tri-copter supported by tilt-rotors. The MPC is responsible for control of the vertical body velocity and angular dynamics, while the PID controller is responsible for position control. Under actuator limits, the MPC could give good trajectory tracking performance. K. J. Nam et al. [9] used two classic controllers (PI and PID controllers) to control the flight maneuver of tri-copter aircraft. The simulated and experimental results showed that a larger roll rate could be obtained with study as compared to conventional UAV supported with control surface (ailerons). Z. Song et al. [10] presented attitude control design based on back-stepping control approach for flight maneuvering of tri-copter UAV. In [11], H. K. Tran et al. used the genetic algorithm (GA) to improve the performance of fuzzy gain scheduling PID controller by tuning the control design parameters. The control scheme has been designed for controlling a single-tilted Tri-copter UAV under different operating conditions. As compared to classic PID controller, PID controller based on GA-tuned adaptive fuzzy-gain-scheduling exhibited better dynamic characteristics. In [12], S. Yoon et al. presented attitude control of single-tilted Tri-copter UAV using LQR-based optimal control. Numerical simulation has been implemented to assess the effectiveness of the proposed controller and the experimental tests have been conducted to verify the simulated results. Fast yaw motion and good performance of controller was the conclusion drawn by this study. In [13], Hasan, A.F. et al. have designed trajectory tracking controller based on Active Disturbance Rejection Control (ADRC) for Tri-copter drone. A comparison study has been established among three types of ESO (extended state observer), which is the core element of ADRC approach. These observers are Super Twisting ESO (STESO), fractional order ESO (FOESO), and nonlinear ESO (NESO). Under these observers, the performance and robustness of ADRC-based UAV have been tested and assessed.

The main contributions addressed by this study can be highlighted by the following points:

❑Developing a FOPD control algorithm to control the altitude and attitude of Tri-copter aerial vehicle.
❑Optimal tuning of FOPD control parameters to improve the performance of FOPD-based Tri-copter.
❑Conducting a comparative study between SHO and PSO algorithms in terms of dynamic performance and robustness characteristics.

The rest of the article is arranged as follows: Section 2 developed the mathematical model of Tri-copter. Section 3 presents the analysis of Fractional Order Proportional Derivative (FOPD). Section 4 is devoted to explaining and developing the SHO algorithm, which is responsible for optimal FOPD controller by optimal tuning of its design parameters. Section 5 presented computer simulation to verify the performance of the proposed control scheme. In section 6, the conclusion has been drawn based on numerical results. In addition, a future suggestion has been added to the conclusion part.

2 Mathematical model

The Tri-copter UAV represents a six Degree of Freedom (DOF) system. The dynamic position and orientation of the aircraft in space can be represented by the six variables $x$ , $y$ , $z$ , $φ$ , $θ$ and $ψ$ . The instantaneous position of UAV's center of mass (body frame) with respect to Earth inertial frame $X_{E} - Y_{E} - Z_{E}$ is represented by the variables $x$ , $y$ and $z$ . The orientation of UAV is represented by yaw angle $ψ$ , pitch angle $θ$ and roll angle $φ$ . The geometric representation of Tri-copter UAV with respect to Earth inertial frame is illustrated in Fig. 2, where the angular velocity of i-th rotor is designated by $Ω_{i}$ .

Fig. 2.

Geometric representation of Tri-copter UAV showing body-fixed and Earth inertial frames

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

The kinematics and dynamics representation of Tricopter model have been established by applying Newton-Euler method. The following assumptions have to be taken into account before conducting the analysis:

A rigid structure,
Symmetrical structure.

2.1 The kinematic model of tri-copter

As has been mentioned earlier, there are two frames to describe the kinematic and dynamic models of Tri-copter UAV. These frames are the Earth inertial frame (E-frame) and Body fixed frame (B-frame) as indicated in Fig. 2.

The distance vector, which describes the range between the two frames, is defined by

r = {[\begin{array}{c} x & y & z \end{array}]}^{T}

. The Earth inertial frame is defined as:

Γ_{E} = {[\begin{array}{c} X_{E} & Y_{E} & Z_{E} \end{array}]}^{T}

(1)

The variables pitch, roll, and yaw angles (

θ

φ

and

ψ

) are used to define the relative orientation of E-frame and B-frame. The vector

θ_{B}

combines these angular variables as follows:

θ_{B} = [φ θ ψ]

(2)

This can be represented by orientation matrix, which will be derived based on the sequence of principle rotations and it is defined by

H_{B \to E}

[14]:

H_{B \to E} = H (z, ψ) \times H (y, θ) \times H (z, φ)

(3)

H_{B \to E} = [\begin{array}{c} \cos ψ & - \sin ψ & 0 \\ \sin ψ & \cos ψ & 0 \\ 0 & 0 & 1 \end{array}] \times [\begin{array}{c} \cos θ & 0 & \sin θ \\ 0 & 1 & 0 \\ - \sin θ & 0 & \cos θ \end{array}] \times [\begin{array}{c} 1 & 0 & 0 \\ 0 & \cos φ & - \sin φ \\ 0 & \sin φ & \cos φ \end{array}]

(4)

H_{B \to E} = [\begin{array}{c} c ψ c θ & c ψ s θ s φ - s ψ c φ & c ψ s θ c φ + s ψ s φ \\ s ψ c θ & s ψ s θ s φ + c ψ c φ & s ψ s θ c φ - c ψ s φ \\ - s θ & c θ s φ & c θ c φ \end{array}]

(5)

where, the symbols

s

and

c

in Eq. (5) represent the short form of

\sin (.)

and

\cos (.)

, respectively. The transformation of body frame to the Earth frame can be performed via translational kinematic model given by

{\dot{Γ}}_{E} = H_{B \to E} V

(6)

where V is the linear velocity w.r.t to body frame B and

{\dot{Γ}}_{E}

denotes the linear velocity w.r.t. earth frame E.

2.2 The tri-copter dynamic model

There are two distinct motions which can be detected in the Tri-copter UAV: The linear (translation) motion and angular (rotation) motion. The rotational motion is expressed relative to B-frame, while the linear motion is described relative to E-frame:

m \ddot{r} = [\begin{array}{c} 0 \\ 0 \\ - m g \end{array}] + H_{B \to E} F_{B}

(7)

where

\ddot{r}

is linear acceleration vector,

m

is the Tricopter mass,

g

is the gravity acceleration, and

F_{B}

represents a force of the body, which is given by

F_{B} = [\begin{array}{c} f_{x} \\ f_{y} \\ f_{z} \end{array}] = [\begin{array}{c} 0 \\ F_{1} \sin (μ) \\ F_{2} + F_{3} + F_{1} \cos (μ) \end{array}] = [\begin{array}{c} 0 \\ 0 \\ k_{f} ({Ω_{2}}^{2} + {Ω_{3}}^{2} + {Ω_{1}}^{2} \cos (μ)) \end{array}] = [\begin{array}{c} 0 \\ 0 \\ U_{1} \end{array}]

(8)

The force component $f_{y}$ is responsible for eliminating the moment reaction due to tail rotor and it has a small value.

The setup of rotational motion, based on Newton-Euler method, is established by applying the following equation

J {\ddot{Θ}}_{B} = τ_{B} - {\dot{Θ}}_{B} \times J {\dot{Θ}}_{B}

(9)

where

J

denotes the inertial matrix of Tri-copter UAV and

τ_{B}

stands for the torque acting on the aircraft at B-frame.

J = [\begin{array}{c} I_{x} & 0 & 0 \\ 0 & I_{y} & 0 \\ 0 & 0 & I_{z} \end{array}]

(10)

τ_{B} = [\begin{array}{c} τ_{φ} \\ τ_{θ} \\ τ_{ψ} \end{array}] = [\begin{array}{c} U_{2} \\ U_{3} \\ U_{4} \end{array}] = [\begin{array}{c} \frac{\sqrt{3}}{2} l k_{f} ({Ω_{2}}^{2} - {Ω_{3}}^{2}) \\ \frac{1}{2} l k_{f} ({Ω_{2}}^{2} + {Ω_{3}}^{2}) - l k_{f} {Ω_{1}}^{2} \cos (μ) + k_{t} {Ω_{1}}^{2} \sin (μ) \\ - l k_{f} {Ω_{1}}^{2} \sin (μ) - k_{t} {Ω_{1}}^{2} \cos (μ) - k_{t} {Ω_{2}}^{2} - k_{t} {Ω_{3}}^{2} \end{array}]

(11)

where

k_{f}

denotes the thrust factor,

l

is the distance between rotor's center and Tri-copter UAV's center, while

k_{t}

represents the drag factor.

Based on Eqs. (7) and (9), the motion equations of Tri-copter UAV can be presented by the following system of differential equations.

{\begin{array}{c} \begin{array}{c} \ddot{φ} = \frac{U_{2}}{I_{x}} + \frac{\dot{θ} \dot{ψ} (I_{y} - I_{z})}{I_{x}} \\ \ddot{φ} = \frac{U_{3}}{I_{y}} + \frac{\dot{φ} \dot{ψ} (I_{z} - I_{x})}{I_{y}} \\ \ddot{ψ} = \frac{U_{4}}{I_{z}} + \frac{\dot{θ} \dot{φ} (I_{x} - I_{y})}{I_{z}} \end{array} \\ \begin{array}{c} \ddot{z} = - g + \frac{1}{m} (\cos θ \cos φ) U_{1} \\ \ddot{x} = (\cos ψ \sin θ \cos φ + \sin ψ \sin φ) \frac{U_{1}}{m} \\ \ddot{y} = (\sin ψ \sin θ \cos φ - \cos ψ \sin φ) \frac{U_{1}}{m} \end{array} \end{array}

(12)

In order to reflect the external disturbances and parametric uncertainties in dynamic model of, for example, the rolling channel, one can have

\ddot{φ} = f_{φ} + ∆ f_{φ} + b_{φ} U_{2} + d_{φ}

(13)

where

f_{φ}

∆ f_{φ}

and

b_{φ}

are represented by

{\begin{array}{c} f_{φ} = \frac{\dot{θ} \dot{ψ} (I_{y} - I_{z})}{I_{x}} \\ ∆ f_{φ} = \frac{\dot{θ} \dot{ψ} (∆ I_{y} - ∆ I_{z})}{{∆ I}_{x}} \\ b_{φ} = \frac{1}{I_{x}} \end{array}

(14)

where

{∆ I}_{x}

{∆ I}_{y}

, and

{∆ I}_{z}

represent the percentages of parametric changes (uncertainties) in the variables

I_{x}

I_{y}

and

I_{z}

, respectively, with respect to their corresponding nominal values. The parameter

d_{φ}

denotes the external disturbance.

3 Design of FOPD controller

The PID controllers are well known for their enormous and consistent demand in many applications and therefore they are under continuous improvements efforts [15–17]. One of the new version of a PID controller is the fractional order type controller called fractional order PID (FOPD) controller. In this type, the integration and the derivation parts are non-integer and the control law resulting from input error is described as [18, 19],

u = k_{p} e (t) + k_{i} I^{- λ} e (t) + k_{d} D^{μ} e (t)

(15)

The integer-type (conventional) PID controller is obtained when setting the order of integration $λ$ and the order of differentiation $μ$ equal to 1. But they are both real numbers that could be integers, taking into notice that when $λ = 1$ then it is considered as a FOPD controller. Ignoring or considering any one of these factors will present a new type of this FOPD controller.

In FOPD-based control of Tri-copter UAV, each channel is supported by individual FOPD controller, and therefore six separate control laws are designed to track the desired angles in roll, pitch and yaw channels and in the Cartesian coordinates (x, y and z axes) (Fig. 3).

U_{1} = m (k_{p z} (z_{d} - z) + k_{d z} D^{μ_{z}} (z_{d} - z) + g)

(16)

U_{2} = I_{x x} (k_{p φ} (φ_{d} - φ) + k_{d φ} D^{μ_{φ}} (φ_{d} - φ))

(17)

U_{3} = I_{y y} (k_{p θ} (θ_{d} - θ) + k_{d θ} D^{μ_{θ}} (θ_{d} - θ))

(18)

U_{4} = I_{z z} (k_{p ψ} (ψ_{d} - ψ) + k_{d ψ} D^{μ_{ψ}} (ψ_{d} - ψ))

(19)

U_{x} = k_{p x} (x_{d} - x) + k_{d x} D^{μ_{x}} (x_{d} - x)

(20)

U_{y} = k_{p y} (y_{d} - y) + k_{d y} D^{μ_{y}} (y_{d} - y)

(21)

Fig. 3.

Control scheme

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

U_{1}

- the total thrust force - is representing the motion in x and y direction,

U_{x}

and

U_{y}

representing the orientation for it, hence Eqs. (22) and (23) can be utilized to find

φ_{d}

and

θ_{d}

φ_{d} = \arcsin ((U_{x} \sin ψ_{d} - U_{y} \cos ψ_{d}) / U_{1})

(22)

θ_{d} = \arcsin ((U_{x} \cos ψ_{d} + U_{y} \sin ψ_{d}) / \cos φ_{d} U_{1})

(23)

For example, the controller in pitch channel can be designed as indicated in Fig. 4. Each channel has the same structure of controller shown in the figure.

Fig. 4.

FOPD control scheme

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

4 Spotted Hyena Optimizer

Dhiman et al. [20, 21] developed meta-heuristic bio-inspired optimization technique known as “spotted hyena” optimization (SHO). This algorithm, which is inspired from the social behaviors of prey and hyena, became very well-known control problems, which require optimization methods in their design due to high capability of achieving a satisfactory result. The SHO method establishes an algorithm of four main steps: encircling, hunting, attacking and searching demeanors. The four steps that the spotted hyena followed to hunt are:

I.Searching around for the prey.
II.Chasing the prey in a way to make it tired and to hunt easily.
III.Surrounding the prey by group of spotted hyenas such as to catch it in perfect time (prey encircling).
IV.Attacking the prey to catch it by group of hyenas.

4.1 Prey encircling

According to the position of prey, which is the best solution for the algorithm, several agents (individuals, hyenas) are involved to update their positions. The model representation of encircling can be described mathematically as follows:

{\vec{D}}_{h} = | \vec{B} \times {\vec{P}}_{p} (x) - \vec{P} (x) |

(24)

\vec{P} (x + 1) = {\vec{P}}_{p} (x) - \vec{E} \times {\vec{D}}_{h}

(25)

where,

x

represents the current iteration,

{\vec{D}}_{h}

denotes the distance between the spotted hyena and the prey, respectively. The vectors

{\vec{P}}_{p}

and

\vec{P}

represent, respectively, the position of prey and spotted hyenas. The vectors

\vec{E}

and

\vec{B}

are coefficients vectors, which can be determined as

\vec{B} = 2 \times {\vec{r}}_{d 1}

(26)

\vec{E} = 2 \vec{s} \times 2 {\vec{r}}_{d 2} - \vec{h}

(27)

\vec{s} = 5 - (I_{i t e r a t i o n} \times \frac{5}{{Max}_{i t e r a t i o n}})

(28)

where,

I_{i t e r a t i o n} = 0, 1, \dots, {Max}_{i t e r a t i o n}

. The vector

\vec{s}

is proportionally reduced from 5 to 0. The vectors

{\vec{r}}_{d 1}

and

{\vec{r}}_{d 2}

are randomly chosen so that they do not exceed the range [0, 1].

4.2 Exploitation: hunting

The following equations constitute the hunting step of SHO algorithm:

{\vec{D}}_{h} = | \vec{B} \times {\vec{P}}_{h} - {\vec{P}}_{k} |

(29)

{\vec{P}}_{k} = {\vec{P}}_{h} - \vec{E} \times {\vec{D}}_{h}

(30)

{\vec{C}}_{h} = {\vec{P}}_{k} + {\vec{P}}_{k + 1} + \dots + {\vec{P}}_{k + N}

(31)

For the first best fitted spotted Hyena, the predator vector is represented by

{\vec{P}}_{h}

, while the position of other spotted Hyenas is defined by

{\vec{P}}_{k}

. The total number of

{\vec{P}}_{h}

is equal to

N

, which is calculated as:

N = {C o u n t}_{n o s} ({\vec{P}}_{h}, {\vec{P}}_{h + 1}, {\vec{P}}_{h + 2}, \dots, ({\vec{P}}_{h} + \vec{M}))

(32)

where,

\vec{M}

is a random vector and its magnitude lies within the range [0.5,1]. All candidate solution are defined by

n o s

4.3 Attacking: the hyenas attack the prey

This step, which represents the stage of attacking the prey, can be modeled mathematically using the following equation:

\vec{P} (x + 1) = {\vec{C}}_{h} / N

(33)

where,

{\vec{C}}_{h}

is the optimal solutions in the group and

N

represents the number of optimal solutions. The equation is supposed to save the best position,

\vec{P} (x + 1)

, together with the update of position of other search agents associated with this best solution.

4.4 Exploration: search for prey

The vector $\vec{E}$ may be greater (or less) than 1 as to guarantee proper agent search towards the prey. Another important factor of the algorithm, which contributes to exploration, is $\vec{B}$ . According to Eq. (25), this vector provides random weights for prey from its random values. Choosing vector $\vec{B} > 1$ in precedence to vector $\vec{B} < 1$ that will explore the random behavior of SHO algorithm and elucidate the action in the distance. The implementation of SHO algorithm can be made with help of the following pseudo codes.

5 Numerical simulation

The numerical simulation has been conducted using MATLAB/Simulink and the “Ode45” is applied as a numerical solver [22–24]. The algorithm of parameter tuning for FOPD controller has been developed based on SHO. The Tri-copter physical parameters were assumed as in Table 1 [25].

Table 1.

The physical parameters of Tri-copter system

Parameter Description	Value (unit)
Moment of Inertia in Yawing direction $I_{z}$	0.0770 $k g . m^{2}$
Moment of Inertia in Rolling direction $I_{x}$	0.0430 $k g . m^{2}$
Moment of Inertia in Pitching direction $I_{y}$	0.0480 $k g . m^{2}$
The length of the arm $l$	0.180 $m$
The mass of Tri-copter UAV $m$	0.85 $k g$
Aerodynamic moment coefficient $k_{t}$	2.88 $\times$ 10⁻⁷ $N . {m . \sec}^{2}$
Motor rotor's moment of Inertia $I_{M}$	1.97 $\times$ 10⁻⁶ $k g . m^{2}$
Aerodynamic force coefficient $k_{f}$	1.970 $\times$ 10⁻⁶ $N . \sec^{2}$
Ground acceleration $g$	9.81 ${m / s e s}^{2}$

The reference trajectory is defined as:

x_{d} = \sin (\frac{p i}{20} t), y_{d} = \sin (\frac{p i}{20} t), z_{d} = 2, ψ_{d} = 0

The particle swarm optimization (PSO) has been applied as competitive optimization method. The PSO method is a population-based algorithm and it is inspired by motion of schooling fish and bird flocks [26–28]. The dynamic performance and robustness characteristics have been used as measure of comparison between the SHO-based FOPD controller and PSO-based FOPD controller.

The Root Mean Square of Error (RMSE) has been adopted as the performance index (cost function) in optimal search for minimization of cost function.

R M S E = \sqrt{\frac{\sum_{i = 1}^{n} {e_{i}}^{2}}{n}}

(34)

The algorithms have run by setting number of iterations equal to 60, and 100 for the population size. Table 2 shows the framework for the design-parameters based SHO and PSO, those optimal parameters were utilized in the proposed controller and, according to that, this controller can be expressed as optimal FOPD.

Table 2.

The results of PSO and SHO algorithms for optimized design parameters

		$φ$	$θ$	$ψ$	$x$	$y$	$z$
SHO	$k_{p}$	0.0901	0.0901	12.46	4.959	4.959	18.602
	$k_{d}$	61.208	61.208	7.128	3.616	3.616	8.0714
	$μ$	0.8489	0.8489	0.99	0.831	0.831	0.99
PSO	$k_{p}$	0.0523	0.0523	12.23	4.8457	12.5457	24.4591
	$k_{d}$	65.7587	65.7587	7.445	3.2301	6.2301	12.3741
	$μ$	0.9809	0.9809	0.99	0.97	0.98	0.99

The performances of PSO-based and SHO-based controlled system have been evaluated numerically in Table 3. The performance evaluation is based on the summation of elements for error vector, which is defined as:

MSE = \frac{1}{m} \sum_{i = 1}^{m} (e_{x}^{2} + e_{y}^{2} + e_{z}^{2} + e_{θ}^{2} + e_{φ}^{2} + e_{ψ}^{2})

(35)

Table 3.

Evaluation of controlled Tri-copter using SHO and PSO

	$M S E$
$S H O$	26.989
$P S O$	28.698

In the case of disturbance-free and uncertainty-free conditions, the behaviors of controlled Tri-copter system based on both versions of optimal controllers (SHO-based FOPD controller and PSO-based FOPD controller) are demonstrated in Figs 5 –10. Also, the behaviors of control efforts resulting from both SHO-based FOPD controller and PSO-based FOPD controller are shown in Figs 11 –14. Figure 15 displayed 2D and 3D scenarios of trajectory tracking based on the two optimal controllers.

Fig. 5.

The behaviors of Tri-copter along $x$ -axis channel due to SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 6.

The behaviors of Tri-copter along $y$ -axis channel due to SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 7.

The behaviors of Tri-copter along $z$ -axis channel due to SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 8.

The behavior of rolling angle $φ$ due to optimal SHO-based and PSO-based control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 9.

The behavior of pitching angle $θ$ due to optimal SHO-based and PSO-based control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 10.

The behavior of yawing angle $ψ$ due to optimal SHO-based and PSO-based control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 11.

The behavior of control effort $U_{1}$ resulting from SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 12.

The behavior of control effort $U_{2}$ resulting from SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 13.

The behavior of control effort $U_{3}$ resulting from SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 14.

The behavior of control effort $U_{4}$ resulting from SHO-based and PSO-based FOPD control algorithms

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

Fig. 15.

Trajectory tracking of Tri-copter controlled by optimal SHO-based and PSO-based controllers: (a) 2D scenario (b) 3D scenario

Citation: International Review of Applied Sciences and Engineering 15, 1; 10.1556/1848.2023.00659

According to above figures and Table 3, one can conclude that the Tri-copter controlled by SHO-based FOPD controller has better trajectory tracking capability, less tracking error and lower control effort as compared to PSO-based FOPD control algorithm. This contributes to the prolonged life of the Tricopter battery.

6 Conclusion

The study has addressed trajectory tracking problem for Tri-copter UAV by developing a control design using SHO-based and PSO-based FOPD controllers. The objective of PSO and SHO algorithms is to optimize the performance of a FOPD controller by fine-tuning of controller's design parameters. A comparison study has been presented between SHO-based FOPD controller and SHO-based FOPD controller via computer simulation using MATLAB codes. According to numerical results and Table 3, one can conclude that the Tri-copter controlled by SHO-based FOPD controller has better trajectory tracking capability, less tracking error and lower control effort as compared to PSO-based FOPD control algorithm. The contributed feature of low control effort will work to prolong the life of Tri-copter battery.

A future work can be conducted for this study; either by suggesting comparison between the proposed SHO and other optimization techniques in the literature like Butterfly Optimization algorithm (BOA), Whale Optimization Algorithm (WOA), Gray Wolf Optimization (GWO) algorithm, and Social Spider Optimization (SSO) [29–32], or between the FOPD controller with other controllers in previous works [33–41].

References

[1]↑
B. H. Sababha, H. M. A. Zu’bi, and O. A. Rawashdeh, “A rotor- Tilt-free tricopter UAV: design, modelling, and stability control,” Int. J. Mechatronics Autom., vol. 5, nos 2–3, pp. 107–113, 2015. https://doi.org/10.1504/IJMA.2015.075956.
- Search Google Scholar
- Export Citation
[2]
A. A. Al-Qassar, A. I. Abdulkareem, A. F. Hasan, A. J. Humaidi, K. I. Ibraheem, A. T. Azar, and A. H. Hameed, “Grey-Wolf optimization better enhances the dynamic performance of roll motion for tail-sitter VTOL aircraft guided and controlled by STSMC,” J. Eng. Sci. Technol., vol. 16, no. 3, pp. 1932–1950, 2021.
- Search Google Scholar
- Export Citation
[3]↑
A. R. Ajel, A. J. Humaidi, I. K. Ibraheem, and A. T. Azar, “Robust model reference adaptive control for tail-sitter vtol aircraft,” Actuators, vol. 10, no. 7, p. 162, 2021.
- Search Google Scholar
- Export Citation
[4]
J. T. Zou, K. L. Su, and H. Tso, “The modeling and implementation of tri-rotor flying robot,” Artif. Life Robot., vol. 17, no. 1, pp. 86–91, 2012. https://doi.org/10.1007/s10015-012-0028-2.
- Search Google Scholar
- Export Citation
[5]↑
D.-W. Yoo, H.-D. Oh, D.-Y. Won, and M.-J. Tahk, “Dynamic modeling and stabilization techniques for tri-rotor unmanned aerial vehicles,” Int. J. Aeronaut. Sp. Sci., vol. 11, no. 3, pp. 167–174, 2010. https://doi.org/10.5139/ijass.2010.11.3.167.
- Search Google Scholar
- Export Citation
[6]↑
J. Źrebiec, “Modelling of unmanned aerial vehicle – tricopter,” Automatyka/Automatics, vol. 20, no. 1, p. 7, 2016. https://doi.org/10.7494/automat.2016.20.1.7.
- Search Google Scholar
- Export Citation
[7]↑
Z. A. Ali, D. Wang, S. Masroor, and M. S. Loya, “Attitude and altitude control of trirotor UAV by using adaptive hybrid controller,” J. Control Sci. Eng., vol. 2016, 2016. https://doi.org/10.1155/2016/6459891.
- Search Google Scholar
- Export Citation
[8]↑
A. Prach and E. Kayacan, “An MPC-based position controller for a tilt-rotor tricopter VTOL UAV,” Optim. Control Appl. Methods, vol. 39, no. 1, pp. 343–356, 2018. https://doi.org/10.1002/oca.2350.
- Search Google Scholar
- Export Citation
[9]↑
K. J. Nam, J. Joung, and D. Har, “Tri-copter UAV with individually tilted main wings for flight maneuvers,” IEEE Access, vol. 8, pp. 46753–46772, 2020. https://doi.org/10.1109/ACCESS.2020.2978578.
- Search Google Scholar
- Export Citation
[10]↑
Z. Song, K. Li, Z. Cai, Y. Wang, and N. Liu, “Modeling and maneuvering control for tricopter based on the back-stepping method,” in CGNCC 2016 – 2016 IEEE Chinese Guid. Navig. Control Conf., no. rotor 1, 2017, pp. 889–894. https://doi.org/10.1109/CGNCC.2016.7828903.
- Search Google Scholar
- Export Citation
[11]↑
H. K. Tran, J. S. Chiou, N. T. Nam, and V. Tuyen, “Adaptive fuzzy control method for a single tilt tricopter,” IEEE Access, vol. 7, pp. 161741–161747, 2019. https://doi.org/10.1109/ACCESS.2019.2950895.
- Search Google Scholar
- Export Citation
[12]↑
S. Yoon, S. J. Lee, B. Lee, C. J. Kim, Y. J. Lee, and S. Sung, “Design and flight test of small tri-rotor unmanned vechicle with LQR based onboard attitude control system,” Int. Journal; Innov. Comput. Inf. Control, pp. 2347–2360, 2013.
- Search Google Scholar
- Export Citation
[13]↑
A. F. Hasan, A. J. Humaidi, A. S. M. Al-Obaidi, A. T. Azar, I. K. Ibraheem, A. Q. Al-Dujaili, A. K. Al-Mhdawi, and F. A. Abdulmajeed, “Fractional order extended state observer enhances the performance of controlled tri-copter UAV based on active disturbance rejection control,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, Cham: Springer, 2023. Available: https://doi.org/10.1007/978-3-031-26564-8_14.
- Search Google Scholar
- Export Citation
[14]↑
H. K. Tran, J. S. Chiou, and S. T. Peng, “Design Genetic Algorithm Optimization education software based fuzzy controller for a tricopter fly path planning,” Eurasia J. Math. Sci. Technol. Educ., vol. 12, no. 5, pp. 1303–1312, 2016. https://doi.org/10.12973/eurasia.2016.1514a.
- Search Google Scholar
- Export Citation
[15]↑
N. M. Noaman, A. S Gatea, A. J. Humaidi, S. K. Kadhim, and A. F. Hasan, “Optimal tuning of PID-controlled magnetic bearing system for tracking control of pump impeller in artificial heart,” J. Européen des Systèmes Automatisés, vol. 56, no. 1, pp. 21–27, 2023. Available: https://doi.org/10.18280/jesa.560103.
- Search Google Scholar
- Export Citation
[16]
A. J. Humaidi, A. A. Oglah, S. J. Abbas, and I. K. Ibraheem, “Optimal augmented linear and nonlinear PD control design for parallel robot based on PSO tuner,” Int. Rev. Model. Simulations, vol. 12, no. 5, 2019. Available: https://doi.org/10.15866/iremos.v12i5.16298.
- Search Google Scholar
- Export Citation
[17]
A. J. Humaidi and A. I. Abdulkareem, “Design of augmented nonlinear PD controller of Delta/Par4-like robot,” J. Control Sci. Eng., 2019, Art no. 7689673. https://doi.org/10.1155/2019/7689673.
- Search Google Scholar
- Export Citation
[18]↑
E. Edet and R. Katebi, “On fractional-order PID controllers,” IFAC-Papers on Line, vol. 51, no. 4, pp. 739–744, 2018. https://doi.org/10.1016/j.ifacol.2018.06.208.
- Search Google Scholar
- Export Citation
[19]↑
C. A. Monje, Y. Q. Chen, B. M. Vinagre, D. Xue, and V. Feliu, Fractional-order Systems and Controls Fundamentals and Applications. Springer-Verlag London Limited, 2010. https://doi.org/10.1007/978-1-84996-335-0.
- Search Google Scholar
- Export Citation
[20]↑
G. Dhiman and A. Kaur, “Optimizing the design of airfoil and optical buffer problems using spotted hyena optimizer,” Designs, vol. 2, no. 3, pp. 1–16, 2018. https://doi.org/10.3390/designs2030028.
- Search Google Scholar
- Export Citation
[21]↑
G. Dhiman and V. Kumar, “Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications,” Adv. Eng. Softw., vol. 114, pp. 48–70, 2017. https://doi.org/10.1016/j.advengsoft.2017.05.014.
- Search Google Scholar
- Export Citation
[22]↑
M. Y. Hassan, A. J. Humaidi, and M. K. Hamza, “On the design of backstepping controller for Acrobot system based on adaptive observer,” in International Review of Electrical Engineering, vol. 15, 4th ed. Italy: Praise Worthy Prize, 2020, pp. 328–335. Available: https://doi.org/10.15866/iree.v15i4.17827.
- Search Google Scholar
- Export Citation
[23]
A. Al-Dujaili, V. Cocquempot, M. E. El Najjar, D. Pereira, and A. Humaidi, “Fault diagnosis and fault tolerant control for n-linked two wheel drive mobile robots,” in Mobile Robot: Motion Control and Path Pla nning. Studies in Computational Intelligence, vol. 1090, Cham: Springer.
- Search Google Scholar
- Export Citation
[24]
H. A. Jaleel, K. S. Khalefa, S. M. Esam, A. S. Jabbar, A. A. Qasim, and A. A. Rashid, “Design of optimal sliding mode control of PAM-actuated hanging mass,” ICIC Express Lett., vol. 16, no. 11, pp. 1193–1204, 2022.
- Search Google Scholar
- Export Citation
[25]↑
S. J. Raheema and M. H. Saleh, “An experimental research on design and development diversified controllers for tri-copter stability comparison,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1105, no. 1, 2021, Art no. 012019.
- Search Google Scholar
- Export Citation
[26]↑
AQ Al-Dujaili, A. Falah, D. A. Pereira, and I. K. Ibraheem, “Optimal super-twisting sliding mode control design of robot manipulator: design and comparison study,” Int. J. Adv. Robotic Syst., vol. 17, no. 6, 2020.
- Search Google Scholar
- Export Citation
[27]
A. J. Humaidi and H. M. Badr, “Linear and Nonlinear Active Disturbance Rejection Controllers for single-link flexible joint robot manipulator based on PSO tuner,” J. Eng. Sci. Technol. Rev., vol. 11, no. 3, pp. 133–138, 2018. Available: https://doi.org/10.25103/jestr.113.18.
- Search Google Scholar
- Export Citation
[28]
R. F. Hassan, A. R. Ajel, S. J. Abbas, and A. J. Humaidi, “FPGA based HILL Co-Simulation of 2dof-PID controller tuned by PSO optimization algorithm,” ICIC Express Lett., vol. 16, no. 12, pp. 1269–1278, 2022. Available: https://doi.org/10.24507/icicel.16.12.1269.
- Search Google Scholar
- Export Citation
[29]↑
A. J. Humaidi, H. T. Najem, A. Q. Al-Dujaili, D. A. Pereira, I. K. Ibraheem, and A. T. Azar, “Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot,” Meas. Control, vol. 54, nos 3-4, pp. 303–323, 2021. https://doi.org/10.1177/0020294021997483.
- Search Google Scholar
- Export Citation
[30]
T. Ghanim, A. R. Ajel, and A. J. Humaidi, “Optimal fuzzy logic control for temperature control based on social spider optimization,” in IOP Conference Series: Materials Science and Engineering, vol. 745, no. 1, 2020, Art no. 012099. Available: https://doi.org/10.1088/1757-899X/745/1/012099.
- Search Google Scholar
- Export Citation
[31]
Z. A. Waheed and A. J. Humaidi, “Design of optimal sliding mode control of elbow wearable exoskeleton system based on whale optimization algorithm,” J. Européen des Systèmes Automatisés, vol. 55, no. 4, pp. 459–466, 2022. Available: https://doi.org/10.18280/jesa.550404.
- Search Google Scholar
- Export Citation
[32]
N. Q. Yousif, A. F. Hasan, A. H. Shallal, A. J. Humaidi, and T. Luay, “Performance improvement of nonlinear differentiator based on optimization algorithms,” J. Eng. Sci. Technol., vol. 18, no. 3, pp. 1696–1712, 2023.
- Search Google Scholar
- Export Citation
[33]↑
A. J. Humaidi, E. N. Talaat, M. R. Hameed, and A. H. Hameed, “Design of adaptive observer-based backstepping control of cart-pole pendulum system,” in Proceeding of IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT 2019), Coimbatore, India, 2019, pp. 1–5.
- Search Google Scholar
- Export Citation
[34]
Al-Dujaili, V. Cocquempot, M. E. B. El Najjar, D. Pereira, and A. Humaidi, “Adaptive fault-tolerant control design for multi-linked two-wheel drive mobile robots,” in Mobile Robot: Motion Control and Path Planning. Studies in Computational Intelligence, vol. 1090, Cham: Springer, 2023. Available: https://doi.org/10.1007/978-3-031-26564-8_10.
- Search Google Scholar
- Export Citation
[35]
A. J. Humaidi and M. R. Hameed, “Design and performance investigation of block-backstepping algorithms for ball and arc system,” in Proceeding of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI 2017), Chennai, India. IEEE, 2017, pp. 325–332. Available: doi: 10.1109/ICPCSI.2017.8392309.
- Search Google Scholar
- Export Citation
[36]
A. Q. Al-Dujaili, A. J. Humaidi, Z. T. Allawi, and M. E. Sadiq, “Earthquake hazard mitigation for uncertain building systems based on adaptive synergetic control,” in Applied System Innovation, vol. 6, 2nd ed. MDPI, 2023, pp. 1–15. Available: https://doi.org/10.3390/asi6020034.
- Search Google Scholar
- Export Citation
[37]
A. J. Humaidi and H. A. Hussein, “Adaptive control of parallel manipulator in Cartesian space,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India. IEEE, 2019, pp. 1–8.
- Search Google Scholar
- Export Citation
[38]
A. Q. Al-Dujaili, A. J. Humaidi, D. A. Pereira, and I. K. Ibraheem, “Adaptive backstepping control design for ball and beam system,” Int. Rev. Appl. Sci. Eng., vol. 12, no. 3, pp. 211–221, 2021. Available: https://doi.org/10.1556/1848.2021.00193.
- Search Google Scholar
- Export Citation
[39]
M. Q. Kasim, R. F. Hassan, A. J. Humaidi, A. I. Abdulkareem, A. R. Nasser, and A. Alkhayyat, “Control algorithm of five-level asymmetric stacked converter based on Xilinx system generator,” in 2021 IEEE 9th Conference on Systems, Process and Control (ICSPC 2021), Malacca, Malaysia. IEEE, 2021, pp. 174–179. Available: https://doi.org/10.1109/ICSPC53359.2021.9689173.
- Search Google Scholar
- Export Citation
[40]
Z. A Waheed and A. J. Humaidi, “Design of optimal sliding mode control of elbow wearable exoskeleton system based on whale optimization algorithm,” Journal Européen des Systèmes Automatisés, vol. 55, no. 4, pp. 459–466, 2022. Available: https://doi.org/10.18280/jesa.550404.
- Search Google Scholar
- Export Citation
[41]
M. N. Ajaweed, M. T. Muhssin, A. J. Humaidi, and A. H. Abdulrasool, “Submarine control system using sliding mode controller with optimization algorithm,” in Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 2, pp. 742–752, 2023. Available: http://doi.org/10.11591/ijeecs.v29.i2.pp742-752.
- Search Google Scholar
- Export Citation

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International Review of Applied Sciences and Engineering

Print ISSN:: 2062-0810

Online ISSN:: 2063-4269

Editorial Board

Mohammad Nazir AHMAD, Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

Murat BAKIROV, Center for Materials and Lifetime Management Ltd., Moscow, Russia

Nicolae BALC, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Umberto BERARDI, Toronto Metropolitan University, Toronto, Canada

Ildikó BODNÁR, University of Debrecen, Debrecen, Hungary

Sándor BODZÁS, University of Debrecen, Debrecen, Hungary

Fatih Mehmet BOTSALI, Selçuk University, Konya, Turkey

Samuel BRUNNER, Empa Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

István BUDAI, University of Debrecen, Debrecen, Hungary

Constantin BUNGAU, University of Oradea, Oradea, Romania

Shanshan CAI, Huazhong University of Science and Technology, Wuhan, China

Michele De CARLI, University of Padua, Padua, Italy

Robert CERNY, Czech Technical University in Prague, Prague, Czech Republic

Erdem CUCE, Recep Tayyip Erdogan University, Rize, Turkey

György CSOMÓS, University of Debrecen, Debrecen, Hungary

Tamás CSOKNYAI, Budapest University of Technology and Economics, Budapest, Hungary

Anna FORMICA, IASI National Research Council, Rome, Italy

Alexandru GACSADI, University of Oradea, Oradea, Romania

Eugen Ioan GERGELY, University of Oradea, Oradea, Romania

Janez GRUM, University of Ljubljana, Ljubljana, Slovenia

Géza HUSI, University of Debrecen, Debrecen, Hungary

Ghaleb A. HUSSEINI, American University of Sharjah, Sharjah, United Arab Emirates

Nikolay IVANOV, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Antal JÁRAI, Eötvös Loránd University, Budapest, Hungary

Gudni JÓHANNESSON, The National Energy Authority of Iceland, Reykjavik, Iceland

László KAJTÁR, Budapest University of Technology and Economics, Budapest, Hungary

Ferenc KALMÁR, University of Debrecen, Debrecen, Hungary

Tünde KALMÁR, University of Debrecen, Debrecen, Hungary

Milos KALOUSEK, Brno University of Technology, Brno, Czech Republik

Jan KOCI, Czech Technical University in Prague, Prague, Czech Republic

Vaclav KOCI, Czech Technical University in Prague, Prague, Czech Republic

Imre KOCSIS, University of Debrecen, Debrecen, Hungary

Imre KOVÁCS, University of Debrecen, Debrecen, Hungary

Angela Daniela LA ROSA, Norwegian University of Science and Technology, Trondheim, Norway

Éva LOVRA, Univeqrsity of Debrecen, Debrecen, Hungary

Elena LUCCHI, Eurac Research, Institute for Renewable Energy, Bolzano, Italy

Tamás MANKOVITS, University of Debrecen, Debrecen, Hungary

Igor MEDVED, Slovak Technical University in Bratislava, Bratislava, Slovakia

Ligia MOGA, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Marco MOLINARI, Royal Institute of Technology, Stockholm, Sweden

Henrieta MORAVCIKOVA, Slovak Academy of Sciences, Bratislava, Slovakia

Phalguni MUKHOPHADYAYA, University of Victoria, Victoria, Canada

Balázs NAGY, Budapest University of Technology and Economics, Budapest, Hungary

Husam S. NAJM, Rutgers University, New Brunswick, USA

Jozsef NYERS, Subotica Tech College of Applied Sciences, Subotica, Serbia

Bjarne W. OLESEN, Technical University of Denmark, Lyngby, Denmark

Stefan ONIGA, North University of Baia Mare, Baia Mare, Romania

Joaquim Norberto PIRES, Universidade de Coimbra, Coimbra, Portugal

László POKORÁDI, Óbuda University, Budapest, Hungary

Roman RABENSEIFER, Slovak University of Technology in Bratislava, Bratislava, Slovak Republik

Mohammad H. A. SALAH, Hashemite University, Zarqua, Jordan

Dietrich SCHMIDT, Fraunhofer Institute for Wind Energy and Energy System Technology IWES, Kassel, Germany

Lorand SZABÓ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Csaba SZÁSZ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Ioan SZÁVA, Transylvania University of Brasov, Brasov, Romania

Péter SZEMES, University of Debrecen, Debrecen, Hungary

Edit SZŰCS, University of Debrecen, Debrecen, Hungary

Radu TARCA, University of Oradea, Oradea, Romania

Zsolt TIBA, University of Debrecen, Debrecen, Hungary

László TÓTH, University of Debrecen, Debrecen, Hungary

László TÖRÖK, University of Debrecen, Debrecen, Hungary

Anton TRNIK, Constantine the Philosopher University in Nitra, Nitra, Slovakia

Ibrahim UZMAY, Erciyes University, Kayseri, Turkey

Andrea VALLATI, Sapienza University, Rome, Italy

Tibor VESSELÉNYI, University of Oradea, Oradea, Romania

Nalinaksh S. VYAS, Indian Institute of Technology, Kanpur, India

Deborah WHITE, The University of Adelaide, Adelaide, Australia

International Review of Applied Sciences and Engineering
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International Review of Applied Sciences and Engineering
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