Various types of faults can occur in a Permanent Magnet Synchronous Machine (PMSM) system, including bearing faults, electrical short/open circuits, eccentricity faults, and demagnetization faults (DFs). A DF occurs when the magnetic strength of the PMSM's permanent magnets weakens, resulting in reduced output torque, which is undesirable in electric vehicles (EVs). This fault can be attributed to physical damage, high-temperature stress, reverse magnetic fields, and aging. Motor current signature snalysis (MCSA) is a traditional method for detecting motor faults, relying on the extraction of signal features from the stator current. In this study, a simulation model of the PMSM was developed to represent both partial and uniform DFs, allowing for the simulation of varying degrees of demagnetization. Harmonic analysis using fast Fourier transform (FFT) demonstrated that the fault diagnosis method based on harmonic wave analysis is effective only for partial DFs in PMSMs, and not applicable to uniform DFs.

This paper addresses a critical challenge in the field of attitude control of fixed-wing Unmanned Aerial Vehicles (UAVs), focusing on the leader and multi-followers′ problem. It introduces Twin Delayed Deep Deterministic Policy Gradient (TD3) based on Cascade-forward ANN networks approach to control the leader and multiple followers in autonomous navigation and path tracking. The TD3 component is initially trained off-line and then applied in real-time. The control system incorporates a novel adaptive Laplacian consensus protocol using an undirected communication graph model that adjusts inter-UAV connection weights in real time based on relative positions. This approach was implemented on a leader-follower formation consisting of one leader and three follower UAVs. The paper includes a stability analysis of the proposed method, demonstrating the system′s overall stability. The effectiveness of this approach is validated through a MATLAB simulation which demonstrates that the TD3 based on ANN (Cascade-forward networks), which is used to train the actor and the twin critics networks, has superior performance with low tracking error, good formation keeping during aggressive maneuvers, and reduced control surface oscillations. The application exhibited improved adherence to the prescribed distance during sharp turns, with fewer formation deviations at the trajectory end point. The findings result verify that combining strategies leads to the formation integrity. The TD3 based on ANN which uses Cascade-forward networks implementation offers also significant improvement in stability, accuracy, and control efficiency for practical UAV formation control applications.

This article aims to develop a fuzzy adaptive SMC technique that incorporates a variable boundary layer and adaptive switching gain for speed control of a three-phase induction motor (IM) drive. The boundary-layer characteristics are chosen to offer the best compromise between control robustness and chattering elimination or reduction. In addition, this technique offers many advantages for uncertain dynamic systems. The chattering phenomenon, a well-known problem in classical SMC, arises when the switching function attracts the state trajectory toward the sliding surface. Several methods and approaches have been tried to eliminate this phenomenon. We propose here an approach that involves fuzzy logic to avoid this undesirable effect so that the stability condition according to Lyapunov is verified. This fuzzy adaptation system associated with the SMC (FASMC) towards the obtained results forms a robust tool for chattering reduction. Finally, an experimental prototype setup tests the robustness of the fully developed control structure. The experimental results obtained from the FASMC for speed IM drives demonstrate the highest effectiveness and robustness when compared to the conventional SMC controller.

To make the process of evaluating complicated hierarchical systems more tractable, it is beneficial to represent them through models derived from a unified class of frameworks that are conducive to subsequent analytical endeavors. This method of interchanging the original model with ones from predetermined categories possessing desirable characteristics is referred to as Mathematical Remodeling. Among remodeling classes there can be mentioned Neural Networks as structures being universal approximators and which are acceptable to be analyzed. The question related to the importance of model inputs (Sensitivity Analysis) has a great practical meaning, for example, this information can be used for model reduction, control of the system, etc. There are a wide variety of methods of Sensitivity Analysis, which can be classified both by the mathematical approaches used and by the types of models to which they are relevant. However, there are almost no unified approaches to assessing sensitivity in the case of hierarchical systems. The paper introduces the approach to estimate sensitivity measures for hierarchical system obtained by applying remodeling concept. The proposed method is based on Analysis of Finite Fluctuations built on the Lagrange mean value theorem. The proposed approach provides both end-to-end analysis (investigation of the influence of inputs of sub-systems on the output of the main system) and analysis of the components of the hierarchical system. The paper also contains a numerical example which demonstrates the ability of the proposed approach to deliver sensitivity measures of hierarchical system.

Assisting elderly individuals in indoor environments is crucial, as they often encounter mobility and safety challenges. Autonomous Unmanned Aerial Vehicles (UAVs) navigation offers a transformative solution, significantly enhancing safety, efficiency, and quality of life. These UAVs can perform tasks such as monitoring, delivering essential items, and responding to emergencies, providing invaluable support and promoting greater independence for elderly individuals. To achieve effective assistance, autonomous UAV navigation relies on robust path planning with obstacle avoidance to ensure optimal performance. A newly developed path planning, based on a new hybrid Water Cycle Algorithm (WCA), stands out by addressing the challenge of avoiding obstacles while adhering to non-holonomic constraints and conserving energy in complex, cluttered indoor environments. In Population-Based Algorithms (PBAs), the initial population plays a crucial role, as it greatly impacts the algorithm’s efficiency in exploring the search space and its rate of convergence. The new developed hybrid algorithm, called WCA-HS, is based on using the Harmony Search (HS) algorithm to identify the optimal initial population for the WCA. The results indicate that the hybrid WCA-HS outperforms classical WCA and other metaheuristic algorithms, such as HS, Firefly Algorithm (FA), Cuckoo Search (CS), Genetic Algorithm (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), over 20 independent runs, highlighting the effectiveness of the population initialization technique. Additionally, the developed indoor path planning system was evaluated in highly cluttered and low-light environments, showcasing its robustness and real-world applicability, making it highly effective for assisting elderly individuals in complex indoor environments.

This paper presents a fault-tolerant control strategy for a three-phase, seven-level Cascaded Multilevel Inverter (CMI) in grid-connected photovoltaic (PV) systems. The proposed approach enhances state-of-the-art CMI fault-tolerant control by integrating advanced compensation techniques, combining fault isolation and compensation, and conducting a comprehensive system analysis under fault conditions. The study focuses on improving power quality and system robustness, making it particularly suitable for modern grid-tied renewable energy applications. Compared to previous works, this research offers notable advances in power quality, system reliability, and fault resilience. Key innovations include optimizing the use of renewable energy in large-scale solar installations through multilevel inverter technology and addressing operational faults to ensure uninterrupted power delivery. The findings underscore the significance of robust fault-tolerant strategies in improving the performance and stability of renewable energy systems.

Contemporary computer architecture comprises multicomputer settings. Multiple computers provide the facility of high performance to implement multiple threads that are faster and concurrently with different processors that can execute tasks simultaneously. The challenges are as follows: cost and high performance. Certain firms have to provide funding to establish data centers that need locations, hardware, and technicians. After the passage of a period of time, such equipment requires maintenance (updating and upgrading) to take place on it. This paper tackles these challenges by passing the drawbacks of data centers. It provides an algorithm that simulates a virtual machine, as one client is to be connected with eight servers to implement two applications namely convolution and mathematical operations. To represent different topologies, the algorithm simulates supercomputer systems by applying multistage omega network topology and parallel processing. Two types of topologies execute linear systolic and star, and is implemented on transmission control protocol (TCP) and user datagram protocol (UDP) protocols. The goal of this paper is to provide a kernel that draws near a cloud computing system by exploiting the JAVA programming language with its techniques (threading, socket, socket server, Datagram socket, and datagram packet). The results offer the connection between the client and servers to be successfully simulated by using multistage omega network. The linear systolic and star topologies are simulated in virtual parallel processing.

Railway interlocking systems can be implemented as distributed systems, where each part of a station is handled by a separate logical unit. The logical units of such systems form a network and communicate by interchanging messages. Such distributed architectures are well known in large industrial control systems. There are several design practices and also algorithmic task partitioning methods that are applicable in distributed control systems. Some of such methods can also be adapted in designing of railway interlocking systems as well. In the case of such systems, the communication time between components must be kept low. Namely each separate controller in a given route must be able to exchange their internal state within a limited time in order to permit the train movement authorization. This limitation could cause high traffic load, if every logical unit would be interconnected with each other. Therefore, the main goal of the minimization is to reduce the number of connections between logical units. This can achieved by distributing and assigning the topological railway objects to certain logical units.

The objective of this study is to evaluate the dependability of double-star induction machines by employing meticulous monitoring and intelligent handling of potential electrical malfunctions that may arise during their functioning. Specifically, the research delves into the examination of three distinct fault types: rotor bar breakage, stator phase opening, and inter-turn short circuit. Utilizing a mathematical model developed in the natural reference frame (abc), our aim is to comprehensively delineate both normal and faulty operational scenarios. To categorize and gauge the gravity of identified faults, we employ a methodology rooted in spectral analysis. In order to ensure continuous machine operation, especially in instances of stator phase opening, our proposed mitigation approach entails intentionally opening a secondary phase positioned at a 90-degree angle to the faulty phase. This strategic alteration transforms the machine into a dual two-phase configuration. Through rigorous simulation analyses, our findings underscore the efficacy and pragmatic viability of the devised fault detection technique, offering valuable insights into the domain of electrical machine reliability and fault management.

A proposed multiband drone shaped fractal antenna (Antenna-1) is introduced in this paper that targets the most required frequencies used for radio frequency energy harvesting applications, including WLAN-2.4G, WLAN-5G, WLAN-6G, Mobile DCS1700, Mobile LTE and WIMAX services. A novel fractal shape is designed and combined with a standard square geometrical shape in order to implement the front patch of the antenna. The antenna is simulated with CST Studio Suite software and fabricated on FR-4 substrate. Different antenna stages with parametric study and optimization were implemented to get the best performance from the drone shape fractal antenna. The multiband (Antenna-1) supports six resonate frequencies (1.7155 GHz, 2.424 GHz, 3.36 GHz, 3.789 GHz, 5.843 GHz, 6.886 GHz) with peak gain of (2.83 dBi) at the frequency 2.45 GHz and a maximum bandwidth of (0.4281 GHz) at the frequency 5.8 GHz that make it a suitable antenna for radio frequency energy harvesting applications.