Switched Reluctance Motor Speed and Torque Control using ACO and
PSO Algorithms
HAFID OUBOUADDI, ADIL BROURI, ABDELMALEK OUANNOU,
FATIMA EZZAHRA EL MANSOURI, ALI BOUKLATA, LAILA GADI
ENSAM, L2MC Laboratory,
Moulay Ismail University,
MOROCCO
Abstract: - A Switched Reluctance Machine (SRM) is an electric motor that operates based on the principle of
magnetic reluctance. In addition, SRMs have several advantages over other types of electric motors. However,
SRMs also have some disadvantages. They can produce high noise and vibration levels, especially at high
speeds. This paper describes the torque and speed controller for SRM using two techniques, Particle Swarm
Optimization (PSO) and Ant Colony Optimization (ACO) combined with a PI corrector. The conventional PI
controller is nowadays used in most engineering, being acknowledged for their ability to give up superior
control in power electronic systems. Moreover, finding appropriate values for the PI controller is not easy. A
solution based on ACO and PSO is used to overcome this problem and simplify tuning the PI controller
parameters. Simulation in MATLAB-Simulink was used to demonstrate the effectiveness of the suggested
controllers.
Key-Words: - SRM; Speed Controller; Torque Controller; Proportional-Integral (PI); ACO; PSO
Received: January 6, 2023. Revised: September 7, 2023. Accepted: October 11, 2023. Published: November 8, 2023.
1 Introduction
Nowadays, the SRM has unique features like simple
structure, high efficiency, high operating
temperature, low cost, simple geometry, and high
torque, [1]. As a result of its special features, the
SRM has extreme multivariable, nonlinear coupling
and ripples in torque, [2]. Recently, research on
various aspects of SRMs has also become a hot spot.
Overall, SRMs are a promising technology for
electric vehicles, renewable energy, and other
applications where high efficiency, low cost, and
compact size are important factors, [3]. In fact,
SRMs have several advantages over other types of
electric motors. They are relatively simple in design
and construction, with fewer parts compared to
other motor types, [4]. This makes them potentially
more cost-effective and easier to maintain.
Additionally, they have a high power density,
meaning that they can provide a lot of power in a
relatively small package. They are also highly
efficient, with low losses in the magnetic circuit.
However, SRMs also have some disadvantages.
They can produce high noise and vibration levels,
especially at high speeds. They also have a limited
speed range, making them less suitable for some
applications that require a wide range of speeds, [5].
Ongoing research and development efforts are
focused on improving the performance and reducing
the limitations of SRMs to make them even more
competitive with other motor types.
Many techniques have been proposed in the
literature for torque ripple and speed control of
SRM drives. However, a fuzzy logic technique is
used for torque ripple in SRM, [6]. Another
technique used an adaptive turn-on angle technique
with direct instantaneous torque control to minimize
the torque ripple is developed in, [7]. Also, for ideal
speed control of switched reluctance motors, the
traditional Proportional Integral (PI) controller is the
most favored controller because of its effectiveness
and simplicity in use. In addition. Engineers choose
the Classical (PI) controller because of its
dependability, structural simplicity, and the
complementing relationship between price and
performance, [8]. Moreover, some authors used Ant
Colony Optimization (PSO) and Genetic Algorithm
(GA) techniques for tuning the PID controller to
reduce torque ripple and speed control in, [9], [10].
In, [11], Fuzzy logic and neural networks are used
to reduce the error between the desired speed and
true speed in SRM. Based on fuzzy logic, a highly
efficient speed controlling of SRM is used in, [12].
Furthermore, the Ant Lion-based cascaded
Fractional Order PID controller (FOPID) was
designed to enhance the speed and torque profile of
a 6/4 SRM drive, [13]. Additionally, an adaptive
WSEAS TRANSACTIONS on SYSTEMS and CONTROL
DOI: 10.37394/23203.2023.18.38
Hafid Oubouaddi, Adil Brouri,
Abdelmalek Ouannou,
Fatima Ezzahra El Mansouri, Ali Bouklata, Laila Gadi
E-ISSN: 2224-2856
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Volume 18, 2023
control algorithm for optimum turn-on and turn-off
angles of SRM is proposed in, [14]. Other
researchers, however, have used a simpler approach
where numerous optimization techniques have been
applied to PID controllers to achieve optimal speed
and torque, [15]. Additionally, advanced control
techniques, such as predictive or adaptive controls,
have also been reported in the literature, [16]. In
fact, an adaptive control algorithm for optimum
turn-on and turn-off angles of SRM over a wide
range of speed control is proposed in, [17]. The
optimal turn-on and turn-off angles for minimizing
the torque ripple in SRM have been described in,
[18]. In, [19], a direct torque control based on the
Lyapunov function was selected to minimize the
torque ripples. In, [20], The innovative approach
integrated machine design with control algorithms,
enabling the profiling of phase currents to
effectively minimize torque ripples in the SRM.
However, another approach for torque optimization
using a fuzzy adaptive controller and off-line
Transfer sharing function is discussed respectively
in, [21], [22]. A TSF-based controller is proposed
in, [23], [24]. The TSF can achieve a minimized
torque ripple with reduced copper losses by
imposing optimal profiling for phase currents.
Due to the saturation, nonlinearity, and time-
varying nature of switched reluctance motors, it is
challenging to develop a precise current-torque
relationship. So, the control strategy of a switched
reluctance motor based on torque and current has
certain limitations.
The aim of this work is to develop a new control
of SRM for minimizing the torque ripple, less
vibration, and mitigating the error between the
desired speed and true speed. By addressing these
aspects, the research aims to enhance the overall
performance, efficiency, and reliability of the SRM
in various practical applications. Thus, the statement
indicates that the main objective of the ongoing
research project is to create an innovative control.
To evaluate the proposed controller by simulation, a
specific SRM 8/6 model in MATLAB is adopted.
Test results demonstrate the proposed controller's
effectiveness, robustness, and accuracy when load
torque and speed vary. In addition, according to the
simulation, the proposed controller such as ACO-PI
can minimize the torque ripple and the error
between the desired speed and the true speed.
While I can offer insights on the potential impacts
of research in power systems and optimization
methods. Generally, the impact of my paper in
these fields could be significant for various reasons:
- Advancements in Power Systems: Research
findings in power systems often contribute to the
development of more efficient, reliable, and
sustainable energy solutions. Your paper's
contributions may include improved control
strategies for power systems, enhancing the
stability, torque ripple, error and performance of
various components within the system.
- Optimization Techniques: Modern optimization
methods are crucial for enhancing the efficiency of
complex systems. my paper findings might
introduce novel approaches to optimize the
performance of power systems, including the fine-
tuning of control parameters and the improvement
of overall system efficiency.
2 SRM Functioning and Mathematical
Model
2.1 Mathematical Model of SRM
The Switched Reluctance Motor (SRM) is a type of
synchronous electric motor that operates based on
the principle of reluctance torque. Unlike other
types of motors, SRMs do not have any windings on
the rotor, which simplifies the construction and
reduces the manufacturing cost. (As shown in
Figure 1), [25]. Switched Reluctance Machines offer
several advantages that make them suitable for
various applications. In addition, these benefits
include having a simple structure, robust, high
efficiency, high-speed range, Silent Operation, and
Regenerative Braking. The unique combination of
these advantages makes SRMs a viable choice for
various industrial and commercial applications,
including electric vehicles, appliances, industrial
automation, and more. Moreover, SRMs operate
based on the principle of minimizing the reluctance
of the magnetic circuit, leading to the rotor aligning
itself with the stator poles. The stator and rotor are
typically made of ferromagnetic materials, and the
rotor has salient poles. By energizing the stator
windings in a sequence, the rotor is compelled to
rotate to minimize the reluctance. The direction of
the torque can be changed by altering the phase
sequence of the stator winding current.
Fig. 1: 3D projection of 8/6 SRM obtained by FEM
analysis.
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Hafid Oubouaddi, Adil Brouri,
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Fatima Ezzahra El Mansouri, Ali Bouklata, Laila Gadi
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The introduction noted that magnetic saturation is
necessary to improve SRM performance. This can
be achieved by expressing SRM parameters as
functions of phase current and rotor position. By
using the fundamental laws of dynamics and the
usual electrical laws, one can develop the following
dynamic system equation:
An equation for the voltage of a phase winding can
be written as follows:
(1)
Where R is the resistance per phase and φ is the flux
linkage per phase given by:
(2)
Finally, the voltage equation is given by:
(3)
Where: is the motor speed (). And the
equation of motion is as follows:
J
dt TTloadBr (4)
is the rotor moment of inertia (), is
the torque of the load (), is the rotor friction
force () and is the motor torque ().
2.2 SRM’s Mode of Operation
Switched reluctance motors are unique in that they
do not have any permanent magnets, and their rotor
is made up of steel or iron poles. The motor operates
by energizing the stator windings in a specific
sequence to create a magnetic field that attracts the
rotor poles. As the rotor poles align with the stator
poles, the magnetic field is then de-energized, and
the rotor poles move to the next set of stator poles.
This process is repeated continuously, causing the
rotor to rotate. In fact, the SRM is another type of
engine, this latter presents several advantages with
respect to other engines. However, among these
advantages low-power because this machine is
functioned when we are excited each phase in
depend on others. For this asymmetrical converter
is used (as shown in Figure 2), [26]. An
asymmetrical converter is a type of power converter
that is commonly used to drive switched reluctance
motors. The converter is designed to convert DC
voltage into a form that can be used to drive the
motor. The asymmetrical converter is designed to
deliver the necessary power to the motor in a
specific pattern to ensure optimal motor
performance. The converter consists of several
components, including diodes, capacitors, and
transistors, that work together to regulate the voltage
and current supplied to the motor. One of the key
features of the asymmetrical converter is its ability
to deliver a variable voltage and current to the
motor. This allows the motor to operate at optimal
efficiency and speed, depending on the load and
other factors. In summary, an asymmetrical
converter is a specialized power converter that is
commonly used to drive switched reluctance
motors. It is designed to deliver a variable voltage
and current to the motor, allowing for optimal motor
performance and efficiency. In addition, in this
work, we are utilized a converter with 8 switches
and 8 diodes to energize the 8/6 SRM. In addition,
each phase is excited by three steps; excitation
mode, freewheeling mode, and De-energizing mode.
Fig. 2: Asymmetrical converter to feed SRM 8/6
Unlike the present boost converter, this
converter has a few key differences. Based on the
operating conditions of the SRM, the converter
discussed here can calibrate the demagnetization
voltage and excitation voltage.
3 Description of SRM Controller
3.1 Description of the ACO Algorithm
The Ant Colony Optimization (ACO) algorithm is a
type of metaheuristics optimization algorithm that is
inspired by the behavior of ants in finding the
shortest path to a food source. ACO is a population-
based algorithm that uses a collection of ants to
explore the search space and find the optimal
solution, [27]. In fact, the algorithm starts by
initializing a population of artificial ants, which are
used to search the solution space. Each ant moves
through the solution space by selecting a candidate
solution based on a probability function. The
probability function is based on a combination of
pheromone trails and heuristics, which guide the
ants towards promising solutions. As the ants move
through the solution space, they deposit pheromone
trails that signal the quality of the solutions they
have found. These pheromone trails are used by the
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DOI: 10.37394/23203.2023.18.38
Hafid Oubouaddi, Adil Brouri,
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Fatima Ezzahra El Mansouri, Ali Bouklata, Laila Gadi
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other ants to guide their search, with ants more
likely to select a path with a higher concentration of
pheromones, [28]. Over time, the pheromone trails
converge towards the optimal solution, as the ants
favour the paths that lead to the best solutions. To
prevent the algorithm from getting stuck in local
optima, ACO uses a mechanism called pheromone
evaporation. This mechanism reduces the
concentration of pheromones over time, making it
less likely for the ants to follow suboptimal paths.
ACO has been successfully applied to a wide range
of optimization problems, including the traveling
salesman problem, the quadratic assignment
problem, and the vehicle routing problem. The
algorithm is known for its ability to find high-
quality solutions in a reasonable amount of time,
and for its ability to handle large and complex
optimization problems. The steps of the proposed
algorithm are summarized in Figure 3.
Fig. 3: Flowchart of ACO algorithm
3.2 Description of the PSO Algorithm
Particle Swarm Optimization (PSO) is a
computational optimization technique inspired by
social behavior, particularly the movement and
behavior of bird flocks or fish schools. It is used to
solve various optimization problems by simulating
the social behavior of individuals, known as
particles, within a search space, [29]. PSO's
simplicity, ease of implementation, and ability to
handle complex optimization problems have made it
a popular choice for researchers and practitioners
working on a wide range of optimization and search
problems in diverse domains.
The PSO algorithm is based on the idea of a
swarm of particles moving through the search space,
where each particle represents a potential solution to
the optimization problem. The position of each
particle represents a candidate solution to the
problem, and the velocity of the particle determines
the direction and magnitude of its movement
through the search space. During the optimization
process, each particle updates its position and
velocity based on the best solution found by itself
and the swarm, [30]. This is done using two key
components: the cognitive component and the social
component. The cognitive component represents the
particle's tendency to move towards its own best
solution, while the social component represents the
particle's tendency to move towards the best
solution found by the swarm. The flow chart, shown
in Figure 4, illustrates the steps involved in tuning a
proportional integral derivative controller using a
PSO.
Fig. 4: Flowchart of PSO algorithm
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4 Results and Simulation
To demonstrate the effectiveness of the proposed
methods, examples of simulations are presented. In
addition, the main problem with the PI corrector is
to find the best parameters Ki and Kp. A solution
based on ACO and PSO is used to overcome this
problem and simplify the process of tuning the PI
controller parameters for reduce the torque ripple
and minimize the error between the desired speed
and the true speed.
Figure 5, shows the diagram for controlling the
SRM using closed-loop control. The controller
parameters are optimized by PSO and ACO
algorithms. This approach allows for the efficient
fine-tuning of the controller's parameters, leading to
improved system performance, stability, and
responsiveness in achieving the desired control
objectives. Firstly, the PSO or ACO methods
generate initial values of two parameters Kp and Ki.
after, each iteration, the proposed algorithms give
the best values of parameters such as the minimum
value of the objective function.
Fig. 5: SRM control via PI controller
implementation in ACO and PSO
The proposed method is tested in several cases:
- For variable speed
The comparison is performed when the two
controllers are applied to SRM 8/6 simulator. In
Figure 6, the speed responses of the two controllers
are shown when the reference speed is a signal that
changes over time (from 2000 to 2500 rpm). As can
be seen, both controllers achieve the reference
speed, but the PI controller requires more time to
stabilize, which warrants the significant overshoot.
In addition, the control ACO presents fast
convergent and also the best accuracy vs PSO
algorithm.
Fig. 6: Speed response using proposed controllers.
- torque controller in variable speed
The torque waveforms obtained by both controllers
are shown in Figure 7. It is clear that the proposed
controller reduces torque ripples, such as the ACO
algorithm. In fact, in case of the speed is changed (at
0.5s) we observe the torque is changed, but one
moment torque follows the load torque. Figure 7,
justify that the control using ACO presents best
accuracy and low ripple torque.
Fig. 7: Torque waveform of controllers for a
100Nm fixed loa
- for load variable and speed constant
In order to evaluate the robustness of the
proposed controller, we applied a variable load
torque with a constant speed of 2000rad/s. Figure
8 depicts the torque response when the load
torque control law considered takes these values
100Nm and 150Nm. The proposed controller
always maintains the speed to its reference
despite variations in the load torque while
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guaranteeing minimum torque ripples, regardless
of load torque variations. Moreover, the control
using ACO gives good results at the level of
accuracy and torque ripple.
Fig. 8: Torque waveform of controllers for variable
load
According to simulation results, even with a
high torque value, the proposed controller
efficiently regulates speed and reduces torque
ripples. In summary, the statement emphasizes the
positive findings of simulation results, highlighting
the effectiveness of the proposed controller in
achieving efficient speed regulation and reducing
torque ripple, even in scenarios involving high
torque values. This suggests the controller's
suitability for applications where precise speed
control and stable motor performance are essential,
particularly in challenging operating conditions.
Furthermore, its robustness to load torque changes is
proven.
5 Conclusion
In this study, the torque and speed of SRM are
controlled by a PI regulator with optimization
algorithms ACO and PSO. In fact, finding
appropriate values for the PI controller is not an
easy task. A solution based on ACO and PSO is
used to overcome this problem and simplify the
process of tuning the PI controller parameters. This
approach enables the study to optimize the
performance of the PI controller for better control of
the system, particularly in the context of managing
the torque and speed of the SRM. The obtained
results show that the proposed controller using
ACO-PI given the best results in terms of torque
ripples and speed tracking error. This finding
suggests the effectiveness of the ACO-PI controller
configuration in achieving better control and
stability for the SRM in the context of the study.
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Mechanics and Materials. Trans Tech
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Contribution of Individual Authors to the
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Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
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