In the era of low-carbon travel, maglev cars emerge as a high-speed, environmentally sustainable solution, leveraging their frictionless, smooth operation. This study introduces a nonlinear dynamic model for the longitudinal dynamics of maglev cars, constructed via a data-driven approach. A nonlinear model predictive control (NMPC) strategy, incorporating rotational speed constraints, is developed to address the inherent instability of the open-loop system. The dynamic relationship between the driving force and the rotational speeds of magnetic wheels was quantified using the least squares method (LSM) based on tests conducted across varied rotational speeds. A single-degree-of-freedom model, integrating stiffness and damping characteristics, was subsequently formulated to describe the longitudinal motion of the maglev car. The model’s validity was confirmed through comparison with experimental outputs under varying conditions. Further, the stiffness and damping coefficients were derived from experimental data, enhancing the model’s precision. Control simulations and real-world experiments under diverse operational conditions demonstrated the efficacy of the NMPC in ensuring robust longitudinal tracking. This investigation substantiates the NMPC approach as an effective control strategy for enhancing the stability and performance of maglev transportation systems.
The quality of state estimation in uncertain systems exerts a significant impact on the performance of control systems. Within these uncertain systems, set-valued mappings introduce output uncertainties, complicating the design of observers. This study maps the output error of uncertain systems to the nonlinear terms of a framer , thereby extending the Luenberger framer. An interval observer design method for uncertain systems is proposed, leveraging monotone system theory to analyze the coherence of the error system. The effectiveness of the algorithm is validated through simulation examples.
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%.
One significant benefit of the Maclaurin symmetric mean (MSM) is that it is a generalization of many extend operators and can consider the interrelationships among the multi-input arguments, such as multi-attributes or multi-experts in the multi-attribute group decision making (MAGDM). In the information fusion process, the Schweizer-Sklar T-norm (TN) and T-conorm (TCN), an important class of the TN and TCN, have more flexibility. We define SS operational rules of SFNs and extend SSTN, SSTCN to Spherical fuzzy values (SFVs) in order to fully utilize the advantages of SSTN, SSTCN, and MSM. Next, by combining the MSM with SS operational rules, we propose the spherical fuzzy Schweizer-Sklar weighted Maclaurin symmetric mean (SFSSWMSM) and spherical fuzzy Schweizer-Sklar Maclaurin symmetric mean (SFSSMSM) operators. This research examines their advantages and creates a novel approach based on these operators for particular MAGDM issues. Then, by comparing the suggested technique with current approaches in practical settings, its benefits and viability are demonstrated. Lastly, a few real-world examples are provided to demonstrate the applicability and benefits of the suggested approach in comparison to a few other approaches already in use.
This study presents the two-stage cubature Kalman filter (TSCKF), which is a sophisticated technique designed to address the issue of variations in system models in real-life scenarios, and utilises nonlinear two-stage transformations to reorganise covariance matrices into a block-diagonal structure, effectively overcoming the limitations of conventional augmented methods. This technique effectively eliminates the need to calculate the cross-covariance between state variables and biases. This leads to a substantial reduction in computational load and facilitates seamless operation of the filter. The TSCKF design is underpinned by a robust theoretical framework, which ensures optimal computational efficiency while also ensuring precise estimations. This work demonstrates the mathematical equivalence between the TSCKF and the standard cubature Kalman filter (CKF) by utilising updated information equivalent transformations, and empirically verifies the equivalence through trajectory tracking experiments conducted on two-wheeled robotic systems subjected to random perturbations, thus affirming the greater accuracy and dependability of the TSCKF in tracking scenarios. Moreover, comparison evaluations offer further proof of the same performance between both methodologies. This study introduces a highly efficient approach in the domain of nonlinear systems and provides a dependable remedy for scenarios where traditional filtering procedures may be inadequate due to deficiencies in the system model.
In the field of industrial motor control, the inherent design complexity and operational challenge of dual star induction motor (DSIM) have made it a focus of research for many scholars. This study attempts to innovatively propose a refined control approach for DSIM, by deploying two pulse width modulation (PWM) voltage sources combining with indirect field-oriented control (IFOC). Core of our innovation is the integration of a super twisting algorithm (STA) controller, which is a strategy specifically designed to enhance the motor's speed control capability. The paper introduced the technical details of DSIM, with the focus placed on the distinctive configuration of two isolated neutral three-phase windings, set apart by a 30-degree electrical phase shift. Such design has posed certain control challenges, and the STA approach has skillfully addressed these challenges. With the help of Matlab/Simulink simulations, the efficacy of STA controller is evaluated and compared with the common Proportional-Integral (PI) controller, and the simulation results are indicative of the STA controller's superiority, showing a significant improvement in reducing torque ripples and stator current fluctuations. The analysis given in the paper quantifies the improvement, showing substantial reductions in steady-state error and response time, as well as an enhanced disturbance rejection capability. These findings are instrumental in showcasing the STA controller's comparative advantage. Concludingly, the adoption of the STA-based control methodology in DSIM applications not only fosters enhanced speed control and efficiency but also holds the promise of broad applicability across various industrial scenarios. This research, therefore, marks a pivotal advancement in the field of DSIM control, potentially revolutionizing its application in diverse industrial settings. The consistency in the use of professional terminology throughout the paper ensures a coherent and comprehensive understanding of the subject matter.
LLC resonant converters own high power efficiency and density, and are widely used in electric vehicles, intelligent and communication power sources, and other fields. The converters cannot obtain accurate mathematical models and their nonlinear characteristics are complex. Therefore, traditional proportional-integral (PI) control cannot achieve control effect well. The dynamic matrix control (DMC) strategy was applied to the converter model, aiming to improve the system’s dynamic response and reduce overshoot. In addition, the DMC algorithm was used in this study to achieve precise system control. The algorithm is robust, and can improve the system’s stability and reliability. At the same time, the system can be flexibly controlled through parameter adjustment. Furthermore, a voltage prediction closed-loop controller was designed to enhance the system’s dynamic performance. In addition, a simulation model was built based on this to verify the feasibility and effectiveness of the scheme. The simulation results showed that the DMC algorithm suppressed overshoot and improved dynamic response effectively.
This study explores dynamic simulation and integrated control in a space robotic arm system characterized by a fully-flexible arm and an elastic base. The elastic base is modeled as a lightweight spring, and the modal shapes of a simply-supported beam are selected via the assumed mode method to represent the bending vibrations of the flexible arm. Dynamic equations for the system are formulated by integrating Lagrangian mechanics with momentum conservation principles. The approach involves reducing the system into two lower-order subsystems using a dual-time-scale singular perturbation method. The first subsystem, exhibiting slow variation, accounts for the joint's rigid motion, while the second, fast-varying subsystem addresses the vibrations of the base and arm. Estimation of joint velocities is conducted through a Luenberger observer, complemented by the use of an Radial Basis Function (RBF) neural network to approximate parameter uncertainties within the system. This facilitates the control of rigid motion in the slow-varying subsystem. Subsequently, the fast-varying subsystem's vibration is actively controlled based on linear system optimal control theory. Numerical simulations validate the integrated control approach's effectiveness in managing both motion and vibration, demonstrating its potential in enhancing the operational precision and stability of space robot systems.
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.