per
Iranian Society of Instrumentation and Control Engineers
Journal of Control
2008-8345
2538-3752
-
مقالات پذیرفته شده
0
0
article
Direct Power Control in BLDC Motor Drives Using Finite Control Set Model Predictive Control to Reduce Torque Ripple and Speed Fluctuation and Improve Harmonic Distortions
Arash Dehestani Kolagar
a_dehestani@mut.ac.ir
1
Ahmad Entezari
Ahmad_entezari6@yahoo.com
2
Mohammad Reza Alizadeh Pahlavani
mr_alizadehp@mut.ac.ir
3
Malek-Ashtar University of Technology
Malek-Ashtar University of Technology
Malek-Ashtar University of Technology
Brushless dc motors (BLDC) are widely used in industrial applications due to their simple structure, high efficiency and long lifetime. The drive of these motors also has a fast transient response and has high quality waveforms in steady state. In this paper, the direct power control using the model predictive method with finite control set (DP-FCS-MPC) is presented in BLDC motor drive and compared with the conventional current control method based on FCS-MPC. This comparison is made under the same operating conditions and includes the steady state operation of the BLDC motor. The simulations performed in PLECS software show the performance of both methods in BLDC motor speed control under sudden load changes. Nevertheless, it is shown that the direct power control using model predictive method with finite control set has better performance in terms of torque ripple reduction, less speed and torque fluctuations, less active and reactive power ripple, and current waveforms with less harmonic distortions.
http://joc.kntu.ac.ir/article-1-975-en.pdf
BLDC Motor Drive
Direct Power Control
Direct Current Control
Finite Control Set Model Predictive Control (FCS-MPC)
per
Iranian Society of Instrumentation and Control Engineers
Journal of Control
2008-8345
2538-3752
-
مقالات پذیرفته شده
0
0
article
Improving Tracking of Splitting Group Targets Using the Main Target Density in the PMBM Filter
Iman Mirsadraei
i.mirsadraiy@gmail.com
1
Mohammad-Mahdi Dehghan Bonadaki
smmd@mut.ac.ir
2
Ali Mohammadi
alimohammadi@mut.ac.ir
3
Malek-Ashtar University
Malek-Ashtar University
Malek-Ashtar University
The Poisson Multi-Bernoulli Mixture filter is one of the most efficient filters for group target tracking. In this filter, target spawning, i.e., the appearance of a new target in the proximity of an existing one in the surveillance area is modeled as a newborn group target. Using this approach may result in missed targets or false alarms. In this paper, profiting from useful information provided by the density of existing group targets, it is possible to predict spawning for all members in the surveillance area. With modification in the birth model in the Poisson density of the filter based on the latest state of detected group targets in the Bernoulli part, the spawning detection probability increases, and the error caused by missed targets is reduced. This approach benefits from the moderated computational complexity property of this filter, particularly for splitting group/point targets, and prevents generating new Bernoulli components for spawned and undetected group targets. The results of Monte Carlo simulations confirm that the modified Poisson Multi-Bernoulli Mixture filter can reduce missed targets and false alarms and increase the reliability of tracking.
http://joc.kntu.ac.ir/article-1-976-en.html
Group Target Tracking
Targets Spawning
PMBM Filter
GGIW density
GOSPA metric
per
Iranian Society of Instrumentation and Control Engineers
Journal of Control
2008-8345
2538-3752
-
مقالات پذیرفته شده
0
0
article
Voltage control of DC microgrids using hierarchical controller based on Kharitonov theory
Nima Mahdian Dehkordi
nimamahdian@sru.ac.ir
1
Mohamma Hafez Alavian
hafezalavian96@gmail.com
2
Mohammad Javad Najafirad
najafirad.mj@gmail.com
3
Shahid Rajaee University
Shahid Rajaee University
Shahid Rajaee University
In this paper, a new hierarchical robust control approach based on the combination of decentralized and distributed control is proposed for voltage control and power sharing in islanded DC microgrids by considering uncertainties and disturbances in the primary control loops and communication channels in the secondary layer. Uncertainties and disturbances are the main factors that can affect the stability of a microgrid. Unlike the previous methods, first by using a decentralized robust PI control structure based on Kharitonov's theory, the primary voltage control loop is robustly designed considering uncertainties and disturbances. By anticipating these changes and preventing them from entering the communication channel in the secondary layer, we compensate for the voltage deviations in the primary layer by using the distributed PI robust control structure. In addition to being simple and robust, the proposed controller is based on a new consensus and robust decentralized protocol, which has a higher convergence rate than the previous protocols and its performance is completely satisfactory under the conditions of uncertainties and large disturbances. Different simulations are performed in MATLAB/SimPowerSystems toolbox on a standard DC microgrid including four distributed generations and under different disturbances. The simulation results show the effectiveness of the proposed controller. In general, the proposed controller increases the reliability of microgrid by sending low data in communication channels.
http://joc.kntu.ac.ir/article-1-954-en.html
Hierarchical control
Kharitonov theory
distributed controller
DC microgrid
secondary controller
per
Iranian Society of Instrumentation and Control Engineers
Journal of Control
2008-8345
2538-3752
-
مقالات پذیرفته شده
0
0
article
Model-free control of a fixed-wing aircraft based on convolutional neural networks
Yousef Seifouripour
yseifouri@ae.sharif.edu
1
Hadi Nobahari
nobahari@sharif.edu
2
Sharif University of Technology
Sharif University of Technology
In this paper, a model-free nonlinear architecture is presented to control a fixed-wing UAV. This architecture has inner and outer loops. The inner loops, which are designed based on convolutional neural networks, control the internal dynamics of the aircraft in a model-free procedure. The outer loops, which use conventional linear controllers, are designed to control the kinematics of the UAV. The neural networks used to control the inner loops are trained offline based on generated databases to avoid time-consuming online learning processes. These databases are created by simulating simple training models. Then, the input-output data of these training models are pre-processed and mapped to image frames so that they can be given as input to convolutional neural networks. After that, a suitable network structure is selected and the networks are trained based on the mapped databases. These trained networks, together with cascaded linear controllers, are applied to the nonlinear simulation of the fixed-wing UAV and its performance is investigated. The inner loops that control the internal dynamics of the UAV have been applied in both single-stage and two-stage forms and their performance has been compared. Also, the sensitivity of the controller to measurement noises and disturbances has been investigated.
http://joc.kntu.ac.ir/article-1-983-en.html
convolutional neural networks
fixed wing aircraft
flight controller
model-free control