Amirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Using a Tuning Parameter to Compromise Computation Time and Shipping Cost in an MDVRP323332532810.22060/eej.2022.21404.5474ENMohammad AminZajkaniDepartment of Electrical Engineering, Sharif University of Technology, Tehran, IranRezaRahimi BaghbadoraniDepartment of Electrical Engineering, Sharif University of Technology, Tehran, Iran0000-0001-6058-2485MohammadHaeriDepartment of Electrical Engineering, Sharif University of Technology, Tehran, Iran0000-0001-8509-4525Journal Article20220519Vehicle routing in last-mile delivery plays a decisive role in the new world of people’s <br />lifestyles. At present, a growing number of people order their needs online, and this forces companies to employ innovative delivery logistics to reduce their last-mile shipping costs. The goal is to minimize the cost of travel that depends on the Euclidean distance between customers. Companies require solving vehicle routing problems (VRP) in a reasonable time. In this paper, a new approach is introduced that solves the multi-depot vehicle routing problem (MDVRP) in real-time. We propose a new method by clustering and decomposing the main problem into smaller ones using a tuning parameter α . This approach could reduce the solution time noticeably (up to 95%) while the shipping cost is still reasonable.https://eej.aut.ac.ir/article_5328_59de0175dc17bcf0f4fb5ae6ae3f444f.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Experimental Validation of Adaptive Sliding Mode Fuzzy Controller for an Inertially Stabilized Platform333350512810.22060/eej.2023.22068.5510ENMohammadNikkhooKish International Campus of Sharif University of Technology, Kish, IranHASSANSAYYADISchool of Mechanical Engineering, Centre of Excellence in Hydrodynamics and Dynamics of Marine Vehicles, Sharif University of Technology, Tehran, Iran.0000-0001-5855-8881Mohammad SaeidSeifSchool of Mechanical Engineering, Centre of Excellence in Hydrodynamics and Dynamics of Marine Vehicles, Sharif University of Technology, Tehran, Iran.Journal Article20221228The adaptive fuzzy control algorithm using the novel membership function was designed to suppress chattering phenomena in the performance of the three-axis Inertially Stabilized Platform (ISP) applied to the stabilization and tracking of the line of sight in optical cameras mounted on a moving boat. The stability of the nonlinear controller was proven through the Lyapunov method. For the theoretical evaluation of the controller performance, a series of numerical simulations were performed. The nonlinear kinematic and dynamic equations of the ISP were derived for this purpose. Due to the coupling between ISP frames, direct implementation of the suggested controller was not feasible. To this end, four simplified assumptions were applied to the ISP design. To evaluate the performance of the proposed control algorithm, both numerical simulation and experimental methods were used on the three-axis ISP, and the results of both methods were compared and validated. Further, the results of the proposed nonlinear control algorithm were compared with the optimal PID linear algorithm. Besides, experimentally obtained angular velocities of a boat were used for the base motion of the ISP in the numerical simulations. Despite the existence of uncertainties in dynamic system modeling, the outcomes of the implementation of the control algorithm and experimental tests indicated that the adaptive fuzzy sliding mode algorithm stabilized the line of sight with acceptable accuracy and improved its performance .https://eej.aut.ac.ir/article_5128_4928e7510f45da6575b04a28519c09ed.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm351364519310.22060/eej.2023.22232.5525ENSeyedHamedMirMohammadAli RoudakiAmirkabir University of Technology, Tehran, Iran0000-0002-3143-4106MehrdadAbediAmirkabir University of Technology, Tehran, Iran0000-0002-1795-2951IrajPourkeivaniAmirkabir University of Technology, Tehran, Iran0000-0001-7765-2945Journal Article20230304The efficient operation and planning along with security of power systems have always occupied an important position. The power system becomes increasingly complex due to the rapidly growth in energy demand. Such a system requires a real-time approach to monitoring and control. Therefore, State Estimation (SE) tools are necessary, especially for nonlinear power grids. Most of network applications use the real-time data provided by the state estimator. Therefore, an optimal performance of state estimation output is the ultimate concern for the system operator. This need is particularly more in focus today due to deregulated and congested systems and smart grid initiatives. The output of the state estimator nearly represents a true state of the system. The present paper, describes the general framework of state estimation in power networks. Also, in the present study linear state estimation method accompanied by optimal placement for Phasor Measurement Unit (PMU) for complete observability and artificial neural network (ANN) trained by Whale Optimization Algorithm (WOA) is employed. The trained model can be used to estimate voltage magnitudes and phase angles as power system states. The proposed method increases accuracy and execution speed while the complication in the formulation will be reduced considerably. A seasonal load profile is considered to measure the accuracy of the state estimation and make the simulation more realistic. Finally, the minimum estimation error will be shown for IEEE 14 and 30 buses benchmark.https://eej.aut.ac.ir/article_5193_84d5711e9bf5547001b765878e7b0157.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Three-Dimensional RF Source Localization Using Reflection and an Improved Particle Filter365376519410.22060/eej.2023.22102.5512ENSaeidHaidariDepartment of Electrical Engineering, Faculty of Engineering, University of Zabol, Zabol, IranAlirezaHoseinpourDepartment of Electrical Engineering, Faculty of Engineering, University of Zabol, Zabol, IranJournal Article20230117This study uses an obstacle map for three-dimensional radio frequency (RF) source localization with reflection. The received signal strength indicator (RSSI) and the angle of arrival (AOA) are the observation requirements for the three-dimensional localization. In the first step of the localization, an unmanned aerial vehicle (UAV) is used to obtain AOA and locate the three-dimensional reflection using a two-dimensional map. Then, the path loss function is used and the reflection angle alongside the distance between the receiver and RF source is estimated based on RSSI. This information <br />is integrated with the information from the two-dimensional map to estimate the RF source location in the three-dimensional space. The possible RF source locations in three-dimensional space are obtained, and it is shown that the possible locations of the RF source for one reflection in the three-dimensional make a circle, so three reflections are required for three-dimensional RF source localization. An improved particle filter is used to estimate RF source location while using the Kullback-Leibler distance (KLD) criteria and local search to improve the method performance with proper estimation speed and accuracy. The simulation results show that the improved particle filter has an adequate estimation with optimal particle number and higher execution speed than the initial particle filter.https://eej.aut.ac.ir/article_5194_2cc73b24083105c3d488cc61b606d4f0.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Fast ℒaasso- 𝓜pc Using Extended Kalman Filter for Robot-Assisted Rehabilitation: with Optimal Impedance377392521910.22060/eej.2023.22092.5514ENHosseinAhmadianDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, IranHeidar AliTalebiDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran0000-0001-6038-2109ImanSharifiDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, IranJournal Article20230121Elderly people may lose the ability to walk normally as their muscles weaken, or it may be difficult for them to maintain balance while walking. In addition to aging, nerve damage such as stroke, trauma, infectious diseases, accidents, etc. may cause loss of balance in walking and weakening of muscles. Wearable robots are crucial for helping patients with lower limb diseases, particularly those with trouble walking since their numbers are rising. These robots assist patients in walking, provide comfort, and aid in recuperation. In this study, the model predictive control based on the Lasso regression theory (Lasso-MPC) and the extended Kalman filter (EKF) was used to make a novel controller that helps the patient walk by adjusting the impedance so that, in addition to regular walking, the patient has to put out the most effort when walking. In order to evaluate and the effectiveness of the proposed method, first, using \textit{OpenSIM} software, the required torque data was determined for healthy, disabled and sick people. Then, using these obtained data, the model fitting parameters were determined. At the end, experiments including a healthy person and a modular lower limb exoskeleton were performed. The findings show that the proposed method successfully estimates the patient's torque and correctly adjusts the robot's assistance level to the user's behavior, thereby maximizing his activity during treatment.https://eej.aut.ac.ir/article_5219_9e7ba617ad9e69b39bd0c29335b79629.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201A New Nearest Neighbors Data Association Approach Based on Fuzzy Density Clustering393404522210.22060/eej.2023.22328.5539ENMousaNazariDepartment of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran0000-0001-6856-7888Mohammad. MAlyanNezhadiDepartment of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran0000-0003-2780-7069Seyed Mohammad RezaHashemiFaculty of Computer Engineering, Shahrood University of Technology, Shahrood, IranSeyed MasoudMirrezaeiFaculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran0000-0001-8493-2072Journal Article20230514The problem of valid measurement’s associations with true targets called “data association” is an essential challenge in multi-target tracking. Previous works often use the nearest neighbor or all neighbor approaches for updating the position of the targets, which are unsuccessful in complex environments and real-time applications, respectively. This paper provides a novel and effective solution to the data association problem in multi-target tracking, offering promising advancements in heavily cluttered environments. The proposed method uses important measurements that are determined based on fuzzy membership degrees. We selected and used valid measurements with a high fuzzy membership degree for updating the position of the targets. In this paper, we used two approaches for the selection of important measurements. The first strategy selects the k measurements with the highest degree of membership among the valid measurements. A second strategy is to give up measurements with very low membership degrees. The ability to solve the data association problem for both approaches under different levels of selecting measurements is evaluated. The proposed method is examined under two scenarios: linear crossing and maneuvering targets. The results show that the proposed technique performs better than FNN, JPDAF, MEF-JPDAF, and Fuzzy-GA methods based on the RMSE criterion .https://eej.aut.ac.ir/article_5222_23e846638607adcbc730817a77581220.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Adaptive Output-Feedback Control for Switched Nonlinear Systems with Unknown Control Directions405416525610.22060/eej.2023.22317.5532ENElhamOvaysiDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran0000-0003-4774-7796MarziehKamaliDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, IranJournal Article20230406This article deals with the design of an adaptive controller for switched non-strict feedback nonlinear systems. In the studied system, the switching signal is arbitrary, the states are not measurable, and the signs of the control gain functions that describe the control directions are completely unknown. First, the unknown nonlinear functions in the switched system are approximated using the universal approximation theorem. Then, the unmeasured states are estimated using the linear state observer, and the controller is designed through an adaptive back-stepping design procedure. Due to the appropriate change of coordinates, 1) neither fuzzy nor radial basis function is used in the design of the controller, 2) only one adaptation law is designed to estimate the unknown parameters in the switched non-strict feedback nonlinear system, and 3) there is no Nussbaum function in the proposed adaptive controller so, the large control signal in the initial stages and the consequent damage to the actuators can be prevented. These features can lead to the simplicity of controller design and the reduction of computational burden. Therefore, the proposed method can be used for practical systems. The stability of the closed-loop system is proved using Lyapunov stability theory. It is shown that, in addition to the semi-globally uniformly ultimately boundedness of all closed loop signals, the tracking error converges to a small neighborhood around zero. In the end, the efficiency of the proposed control method is confirmed through the simulation results of an example.https://eej.aut.ac.ir/article_5256_8819159f9246232ed1299a7414448ab4.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291055Issue 3 (Special Issue)20231201Parallel Real-time Nonlinear Model Predictive Control of DFIG-based Wind Turbines over All Operating Regions417440527710.22060/eej.2023.22276.5528ENMohammadSoleymaniDepartment of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran0000-0001-6945-7410MehdiRahmaniDepartment of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran0000-0002-4560-9018NooshinBigdeliDepartment of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran0000-0001-5536-4491Journal Article20230315Nonlinear model predictive control (NMPC) is a viable solution for control problems in the industry. In this paper, a real-time NMPC approach is proposed for the control of wind turbine (WT) over operating regions. Using wind speed predictions, the NMPC achieves the right compromise between maximizing power and reducing WT fatigue loads while limiting the generator torque activity and the blade pitch angle and smoothing out the electrical power. The control scheme is tested in a simulation environment with a set of standard high turbulence wind profiles and coherent gusts, utilizing complete aeroelastic modeling of the WT and an all-nonlinear model of the doubly fed induction generator (DFIG) over the whole operation region. Besides, the NMPC has been implemented in a parallel Newton-type approach to make it more efficient and implementable. A wide range of simulation scenarios, as well as statistical analysis, were also performed to demonstrate the performance and robustness of the proposed controller against model parameter uncertainties. In addition, finite-time convergence of the controller is guaranteed by employing terminal constraints. The results show 1.7% increase in power extraction, 11% decrease in shaft load, and 12% decrease in tower load while reducing the activity of control inputs and smoothing the generator power.https://eej.aut.ac.ir/article_5277_770f8e448d07586afbf77bb59f698587.pdf