per Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-03233147163articleE. Shokati Asle.shokati@merc.ac.ir1M. Hasan Babayi Nozadianm.nozadian@basu.ac.ir2Department of Energy, Materials and Energy Research Center, Karaj, IranDepartment of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran<p style="direction: ltr;">The most basic method for controlling impedance-source inverters is the simple boost control method. Besides the simple boost control method, several other control strategies have been developed, including the maximum boost control method, the maximum boost control method with third-harmonic injection, the maximum constant boost control method, and the maximum constant boost control method with third-harmonic injection.</p> <p style="direction: ltr;">Compared with the simple boost control method, the maximum boost control method provides a wider operating range. However, in this method, the inductor current and capacitor voltage exhibit significant low-frequency ripple in addition to the high-frequency ripple. To reduce the size and cost of the impedance-source network, these low-frequency ripples must be eliminated. For this purpose, the maximum constant boost control method is employed.</p> <p style="direction: ltr;">Moreover, third-harmonic injection can be incorporated into the maximum constant boost control method to further extend the modulation index range. In this paper, the switching methods of simple boost, maximum boost, maximum boost with third-harmonic injection, maximum constant boost, and maximum constant boost with third-harmonic injection are presented in detail, and the advantages and disadvantages of each method are discussed. Finally, simulation results obtained using PSCAD/EMTDC are provided to validate the accuracy of the reviewed control methods.</p>http://ijece.org/Article/50559Z-source inverter simple boost control method maximum constant boost control method third harmonic injection modulation index voltage gainper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-03233164174articleM. Jafari Sejzehmehdijfr79@gmail.com1A. Halvaei Niasarhalvaei@kashanu.ac.ir2Dept. of Elec. and Comp. Eng., University of Kashan, Kashan, IranDept. of Elec. and Comp. Eng., University of Kashan, Kashan, Iran<p style="direction: ltr;">Permanent Magnet Synchronous Motors (PMSMs), due to their outstanding features such as high torque density and efficiency, as well as high reliability, have now secured a special position in many industrial applications and mass production applications. In order to further enhance the reliability of PMSM drives, an open-end winding structure is used for the motor, where each winding is supplied by a single-phase inverter. In some applications, to maximize reliability and achieve complete modularity of the drive, separate microcontrollers are employed to control each single-phase inverter. The use of this modular structure makes it impossible to utilize common modeling and control methods of three-phase PMSMs in two-axis reference frames. Therefore, in this research, a method of independent modeling and control of each motor phase in a stationary three-axis coordinate system is used. Moreover, under fault conditions and the loss of one phase, a fault-tolerant control method is employed to maximize torque production capability by the healthy phases while minimizing torque ripple caused by the second harmonic. The validity of the presented analyses and control method is proven through simulation in Simulink software. Additionally, to verify the presented theories and simulation results, the results of several experimental tests under fault conditions are provided.</p>http://ijece.org/Article/52214Torque ripple open-end winding three-phase PMSM reliability H-bridge inverter fault-tolerant controlper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-03233175186articlePeyman Madehkhaksap_khaksar@modares.ac.ir1Hamed Delkhoshh.delkhosh@modares.ac.ir2Power group, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranPower group, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran<p style="text-align: left;">Moving toward renewable generation and utilizing the potential of demand response in downstream power networks are considered the key solutions to address the challenges of future power systems. Also, the decentralization and digitalization gigatrends have highlighted the importance of optimal energy management for smart homes, including generation, consumption, and storage devices, based on modern approaches. This paper proposes a home energy management system (HEMS) for a building consisting of different appliances (three types of fixed power, time-shiftable, and power-shiftable) and a renewable distributed generation (solar panel). This HEMS receives the solar generation power and energy market price from a neural network-based forecasting tool to obtain the optimal daily schedule of the smart home using multi-agent reinforcement learning based on the Q-learning solution method. The main innovative aspect of the proposed model is the possibility of simultaneous participation in energy and flexibility markets, which can be aligned or opposite in terms of economic profit. Complicating factors of user satisfaction (comfort concerns) and upstream obligations (required generation curtailment) are also considered in the model. The effectiveness of the proposed method is demonstrated through the simulation for various scenarios and sensitivity analysis to key parameters.</p>http://ijece.org/Article/51333Energy management Smart Homes Electricity Markets Flexibility Services Multi-Agent Reinforcement Learningper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-03233187197articleS. Khamisi Nasabkhamisisajjad66@gmail.com1N. Erfani Majdnasser.erfanimajd@gmail.com2Dept.of Elec. Eng., Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, IranDept.of Elec. Eng., Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran<p style="direction: ltr;">This paper proposes a novel control strategy for the sensorless drive of a permanent magnet synchronous motor (PMSM), whose main innovation is the triple combination of Sliding Mode Control (SMC), Fractional-Order PID (FOPID) controllers, and a modified Particle Swarm Optimization (PSO) algorithm within a Field-Oriented Control (FOC) structure. In this approach, for the first time, SMC is used in the speed loop and FOPID controllers are simultaneously employed in the current loops and in a Model Reference Adaptive System (MRAS)-based speed estimator. Another innovation is the introduction of a modified PSO algorithm featuring a particle clustering mechanism, where weak particles are updated based on their distance from the trained best particle and distinct update rules are defined for each cluster. Performance evaluation under two scenarios (sudden load torque changes and reference speed changes) demonstrates that the proposed method reduces the maximum speed estimation error by up to 65%, improves the speed response settling time by up to 50%, and significantly reduces torque oscillations compared to classical PID control. This performance superiority stems from the inherent robustness of SMC against disturbances, the extra degree of freedom offered by FOPID controllers and the high accuracy of the modified optimization algorithm in tuning the coefficients.</p>http://ijece.org/Article/51861Sensorless field-oriented control permanent magnet synchronous motor fractional-order PID controller modified particle swarm optimization algorithmper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-03233198208articleZ. Moradi Gheisvandimoradi72717@gmail.com1A. Kamrankamran@razi.ac.ir2Elec. and Comp. Eng. Dept., Razi University, Kermanshah, IranElec. and Comp. Eng. Dept., Razi University, Kermanshah, Iran<p style="text-align: justify;">In this study, we propose a test generation method for combinational digital circuits that leverages an index derived from an approximate critical path tracing technique to identify effective test vectors. Evaluation on benchmark circuits shows that this approximate index strongly correlates with the exact fault coverage index while requiring significantly lower computational effort. Using the proposed method, test sets were generated for several benchmark circuits and compared with other test generation approaches that employ either the exact fault coverage index or alternative approximate indices, such as probabilistic indices or simulation-based indices obtained via fault sampling. The results demonstrate that the proposed method efficiently achieves high fault coverage, reduces the number of test vectors, and lowers test generation time.</p>http://ijece.org/Article/51260Fault simulation automatic test pattern generation fault coverage test pattern approximate critical path tracingper Iranian Research Institute for Electrical Engineeringفصلنامه مهندسی برق و مهندسی کامپيوتر ايران16823745168237452026-03233209216articleA. Khojasteh NejadAt.khojasteh@eng.uk.ac.ir1M. Mollaie Emamzadehmolaie@uk.ac.ir2M. مغفوری فرسنگیmmaghfoori@uk.ac.ir3Dept. of Elec. Eng., Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, IranDept. of Elec. Eng., Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, IranDept. of Elec. Eng., Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran<p class="MsoNormal">Iterative learning control algorithm (ILC) is a smart and effective method to improve the transient response of systems that work repeatedly in a certain time interval. Although control theory provides several design tools to improve the response of a dynamic system, it is not always possible to achieve the desired result due to the presence of unmodeled dynamics or parameter uncertainties. ILC is a design tool that can be used to overcome the shortcomings of traditional controller design, even when the model is uncertain or unknown and we have no information about the system and its nonlinearity. the optimal solution can be reached if, the structure of the control law and its parameters have been selected correctly. One of the most important effective factors in the control law structure is the time delay between input and output. In this paper, a method is proposed that uses the impulse response to select the optimal delay in the ILC law, and then the coefficients are determined. The desired method was used to control several dynamic systems and its efficiency was investigated in simulations and it can be seen that the best results in convergence are obtained for the proposed set delay.</p>http://ijece.org/Article/47967Iterative learning control impulse response delayed model convergence speed.