Unmanned Aerial Vehicles (UAVs), have recently sparked attention due to its versatility in a wide range of real-life uses. They require to be controlled so as to conduct different operations and widen their typical roles. This study proposes an optimal robust deadbeat controller for the roll angle motion of tail-sitter vertically take-off and land vehicles, taking into consideration the systems’ intrinsic sensitivity to outside influences and fluctuation of their dynamics. Primarily, several assumptions are used to develop an appropriate transfer function that reflects the system physical attributes. The suggested controller is then formed in two sections: the first section addresses the nominal system’s unstable dynamics, and the second element imposes the desired deadbeat performance and robustness. The control system variables are optimized using the creative and efficient Incomprehensible but Time-Intelligible Logics optimization technique, ensuring that the specified robustness demand is satisfied correctly. Finally, simulation is used to evaluate the developed controller effectiveness, revealing beneficial stability and performance indicators for both nominal and uncertain regulated system featuring uniform, bounded, and feasible closed-loop outputs. The control unit performs well, with a rising time of 0.0965 seconds, a settling time of 0.1134 seconds, and an overshoot of 0.167%.

This study presents a comprehensive evaluation of linear and non-linear control systems, specifically Proportion Integration Differentiation (PID) and fuzzy logic controllers, in the context of position control within double-link robotic manipulators. The effectiveness of these controllers was rigorously assessed in a simulated environment, utilizing MATLAB Simulink for the simulation and SOLIDWORKS for the model design. The PID controller, characterized by its Kp, Ki, and Kd components, was implemented both in the simulation and on the hardware. However, due to the constraints of the microcontroller's RAM and processor, which facilitate the hardware's connection with MATLAB, the application of the Fuzzy Logic concept to hardware was not feasible. In the simulated environment, the fuzzy logic controller demonstrated superior stability in comparison to the PID controller, evidenced by a lower settling time (1.0 seconds) and overshoot (2%). In contrast, the PID controller exhibited a settling time of 0.2 seconds and an overshoot of 32%. Additionally, the fuzzy logic controller showcased a 44% reduction in steady-state error relative to the PID controller. When applied to hardware, the PID controller maintained stable results, achieving a settling time of 0.6 seconds and an overshoot of 2%. The steady-state errors for Link 1 and Link 2 were recorded as 3.6° and 1.4°, respectively. The findings highlight the fuzzy logic controller's enhanced stability, rendering it more suitable for ensuring the accuracy and protection of the manipulator system. As a non-linear controller, the fuzzy logic controller efficiently addresses various potential errors through its intelligent control mechanism, which is embedded in its fuzzy rules. Conversely, the PID controller, a linear controller, responds rapidly but may lack flexibility in complex scenarios due to its inherent linearity. This study underscores the importance of selecting an appropriate controller based on the specific requirements of robotic manipulator systems, with a focus on achieving optimal performance and stability.

In this investigation, the robust H$\infty$ control of nonlinear electric vehicles (EVs), powered by permanent magnet synchronous motors (PMSM), was examined. Emphasis was placed on enhancing the accuracy and robustness of the vehicle speed regulation by incorporating a meticulous H$\infty$ method, supplemented by the proficient integration of Linear Matrix Inequality (LMI). A solution predicated on the LMI approach was devised, encompassing two distinct H$\infty$ controllers for both current and speed control. Subsequent to an extensive analysis of the mathematical and control model of the EV, weighting functions were judiciously selected to optimize stability and performance. The proposed methodology offers significant advancements in the domain of EV control strategies and proffers insights into the application of robust control methods. Through comprehensive simulations, the effectiveness of the outlined method was validated, revealing impeccable speed control and ensuring steadfast performance when applied to the dynamic model of an EV equipped with a PMSM motor. This research elucidates the progressive strides made in the realm of EV control tactics and offers profound understandings of robust control methodologies.

Robotic Process Automation (RPA), employing software robots or bots, has emerged as a pivotal technological advancement, automating repetitive, rule-based tasks within business operations. This leads to enhanced operational efficiency and substantial cost reductions. In this systematic review, information was extracted from 62 pertinent research articles on RPA published between 2016 and 2022. The findings elucidate the fundamental principles of RPA, predominant trends, and leading RPA frameworks, alongside their optimal industry applications. Moreover, the necessary procedural steps for RPA implementation in industries are delineated. The objectives of this study encompass highlighting contemporary RPA research directions and evaluating its potential in streamlining diverse business processes.

In this study, the challenges of load variations, input voltage fluctuations, and reference voltage deviations for a DC-DC buck converter system are addressed. A composite voltage controller, founded on a model predictive control (MPC) integrated with a reduced-order state observer (RESO), is introduced to ameliorate the tracking performances of such converters. Disturbances, both matched and mismatched, are conceptualized as total disturbances within an innovatively proposed error tracking model. Subsequently, a RESO is meticulously developed to estimate and attenuate these disturbances. In parallel, an MPC is crafted to ensure enhanced system robustness and superior steady-state performances. Comparative simulations indicate that this innovative composite controller exhibits a rapid settling time and smoother response curve compared to traditional MPC. Furthermore, it is observed that when exposed to disturbances, the proposed methodology demonstrates heightened disturbance rejection capabilities, accelerated voltage tracking, and improved steady-state performance.

Sign language plays a crucial role in communication for individuals with speech or hearing difficulties. However, the lack of a comprehensive Indian Sign Language (ISL) corpus impedes the development of text-to-ISL conversion systems. This study proposes a specific deep learning-based sign language detection system tailored specifically for Indian Sign Language (ISL). The proposed system utilizes Long Short-Term Memory (LSTM) networks to detect and recognize actions from dynamic ISL gestures captured in videos. Initially, the system employs computer vision algorithms to extract relevant features and representations from the input gestures. Subsequently, an LSTM-based deep learning architecture is employed to capture the temporal dependencies and patterns within the gestures. LSTM models excel in sequential data processing, making them well-suited for analyzing the dynamic nature of sign language gestures. To assess the effectiveness of the proposed system, extensive experimentation and evaluation were conducted. A customized dataset was curated, encompassing a diverse range of ISL sign language actions. This dataset was created by collecting video recordings of native ISL users performing various actions, ensuring comprehensive coverage of gestures and expressions. These videos were meticulously annotated and labelled with corresponding textual representations of the gestures. The dataset was then split into training and testing sets to train the LSTM-based model and evaluate its performance. The proposed system yielded promising results during the validation process, achieving a training accuracy of 96% and a test accuracy of 87% for ISL recognition. These results outperformed previous approaches in the field. The system's ability to effectively detect and recognize actions from dynamic ISL gestures, facilitated by the deep learning-based approach utilizing LSTM networks, demonstrates the potential for more accurate and robust sign language recognition systems. However, it is important to acknowledge the limitations of the system. Currently, the system's primary focus is on recognizing individual words rather than full sentences, indicating the need for further research to enhance sentence-level interpretations. Additionally, variations in lighting conditions, camera angles, and hand orientations can potentially impact the system's accuracy, particularly in the context of ISL.

A significant surge in the installation of Vertical Axis Wind Turbines (VAWTs) in areas of spatial constraints and fluctuating wind directions has been observed, attributable to the omission of a yaw mechanism, which otherwise would require orientation towards wind direction. Among VAWTs, the Savonius variant, characterized by an S-shaped rotor, assumes a particular interest due to its operational advantages in the drag-based regime and its self-starting capability. Given their ability to generate electricity under low-wind-speed conditions, these turbines are markedly suited for urban locales. This investigation deploys Computational Fluid Dynamics (CFD) analysis, utilizing ANSYS CFX software, on VAWTs of varying blade heights, facilitating the measurement of torque generation under distinct air velocities. The wind turbine models for this analysis were designed using Creo software. Concurrently, an exploration into the feasibility of VAWTs for hydrogen production through electrolysis is undertaken using analytical methods. Results highlight the substantial influence of turbine height on power generation, which subsequently has direct repercussions on hydrogen production efficiency via the electrolyzer. A 600 mm height VAWT yielded the maximum hydrogen production of 1.05 kg, whereas an 800 mm height VAWT resulted in the minimum production of 0.339 kg. The research findings underscore the potential of VAWTs in hydrogen generation, emphasizing the critical role of wind turbine design optimization in augmenting power generation and, thus, hydrogen production.

This study introduces a new ten-term 5-D hyperchaotic system, derived from the 3-D Sprott C system. The proposed system has coexisting two attractors: the self-excited and hidden attractors. This system exhibits a rich array of characteristics, taking inspiration from various forms of equilibrium points, stable focus-nodes, saddle-focus, and non-hyperbolic unstable points. These features are shown to be dependent on parameter adjustments. The coexistence of chaotic and hyperchaotic attractors within a 5-D system coupled with three types of equilibrium points is an intriguing phenomenon. A spectrum of numerical methodologies, including phase portraits, computation of Lyapunov exponent, estimation of Lyapunov dimension, and multistability analysis, have been employed to effectively illustrate the diverse attractors. The stability theory is utilized for investigating the synchronization problem, a topic that is elucidated in depth. An assortment of dynamical behavior, such as hyperchaotic, hyperchaotic with 2-tours, chaotic, and chaotic with 2-tours, is recognized. Validation of the primary findings is conducted via theoretical and numerical simulations, fortifying the theoretical conclusions, with numerical simulations executed using MATLAB2021.