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Journal of Intelligent Systems and Control
JIMD
Journal of Intelligent Systems and Control (JISC)
JOSA
ISSN (print): 2957-9805
ISSN (online): 2957-9813
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2023: Vol. 2
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Journal of Intelligent Systems and Control (JISC) is a notable platform in the realm of intelligent systems and control technology, offering a unique blend of peer-reviewed, open-access content. This journal is dedicated to advancing research in both theoretical and practical applications of intelligent systems, highlighting its significant role in shaping technological advancements and practical solutions in automation and control. JISC sets itself apart by providing in-depth analyses and discussions not just on theoretical models but also on real-world implementations, appealing to both academic researchers and industry practitioners. Emphasizing innovative methodologies and current developments, JISC distinguishes itself from other journals in its field. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief(1)
he chen
Hebei University of Technology, China
chenh@hebut.edu.cn | website
Research interests: Double Pendulum Cranes; Offshore Cranes; Dynamic Modeling; Trajectory Planning; Fuzzy Control; Adaptive Control

Aims & Scope

Aims

Journal of Intelligent Systems and Control (JISC) is a groundbreaking open-access journal dedicated to exploring the frontiers of intelligent systems and system control. Its mission is to foster innovative research and deepen understanding in the operation and control of diverse systems ranging from economics to engineering, management, and technology. JISC welcomes a variety of original submissions, including reviews, research papers, short communications, and special issues focusing on novel theories and practical advancements that enhance system performance and control.

JISC's objective is to provide a platform for detailed theoretical and experimental research, with no limits on paper length, ensuring comprehensive communication and reproducibility of results. Unique features of JISC include:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

JISC covers a broad spectrum of topics, setting it apart from similar journals by its comprehensive approach:

  • Artificial Intelligence in Systems: Delving into the development and application of AI across various systems, exploring its impact on efficiency and innovation.

  • AI-Powered Internet of Things (IoT): Investigating how AI enhances IoT devices and networks, with a focus on smart systems integration and real-time data analysis.

  • Robotics Enhanced by AI: Examining the integration of AI in robotics, including autonomous systems, machine learning algorithms for robotics, and AI's role in robotic advancements.

  • Artificial Neural Networks (ANNs): In-depth studies on the design, implementation, and application of ANNs in simulating human decision-making processes.

  • Computer Vision and Pattern Recognition: Research on advanced techniques in computer vision, image recognition, and pattern analysis using AI.

  • Data Mining and Big Data Processing: Analyzing how AI-driven data mining and big data processing techniques transform information management and decision-making.

  • Advancements in Deep Learning and Machine Learning: Exploring cutting-edge developments in deep learning and machine learning, focusing on their applications in complex system analysis and prediction.

  • Learning Methodologies: Detailed examination of various AI learning methodologies, including supervised, semi-supervised, and unsupervised learning, and their applications.

  • Human-Centric AI Computing: Studies on the interaction between humans and AI systems, including human-computer interaction (HCI), human-robot interaction (HRI), and the implications for user experience and system design.

  • Social and Affective Computing: Exploring the impact of AI on social computing, including emotional recognition, sentiment analysis, and AI's role in social media and communication platforms.

  • Image and Video Processing Techniques: Advanced research in AI-driven image and video processing, analysis, and interpretation.

  • Development of Multi-Agent Systems: Investigations into the creation and management of multi-agent systems, focusing on coordination, cooperation, and competition among intelligent agents.

  • Intelligent Information Systems: Insight into the design and application of intelligent systems for information retrieval, processing, and management.

  • Control Theory and System Control Applications: Detailed exploration of modern control theories, including linear and nonlinear control, and their practical applications in various industries.

  • Innovations in Robotics and Automation: Studies on the latest trends in robotics and automation, including the integration of AI and IoT in automated systems.

  • Ethical and Social Implications of Intelligent Systems: Addressing the ethical, legal, and social implications of AI and intelligent system deployment in various sectors.

Articles
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Abstract

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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.
Open Access
Research article
Comparative Analysis of PID and Fuzzy Logic Controllers for Position Control in Double-Link Robotic Manipulators
nor maniha abdul ghani ,
aqib othman ,
azrul azim abdullah hashim ,
ahmas nor kasrudin nasir
|
Available online: 11-28-2023

Abstract

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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.

Open Access
Research article
Robust Speed Control in Nonlinear Electric Vehicles Using H-Infinity Control and the LMI Approach
farid oudjama ,
abdelmadjid boumediene ,
khayreddine saidi ,
djamila boubekeur
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Available online: 09-27-2023

Abstract

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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.

Abstract

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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.

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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.

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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.

Open Access
Research article
Detection and Interpretation of Indian Sign Language Using LSTM Networks
piyusha vyavahare ,
sanket dhawale ,
priyanka takale ,
vikrant koli ,
bhavana kanawade ,
shraddha khonde
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Available online: 07-19-2023

Abstract

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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.

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Unmanned Aerial Vehicles (UAVs), in the form of ornithopters, which emulate avian flight through wing flapping, have been the focus of this investigation. The remarkable maneuverability of birds and insects, often lacking in conventional aircraft, is harnessed to advance the control and stability of flapping wing flight. The need for such exploration is driven by the potential benefits to both scientific inquiry and societal applications. This investigation tackles the task of tailoring the ornithopter's design and component choice to cater to performance expectations derived from the flight attributes of birds, such as superior maneuverability, agility, low-speed flight capabilities, and high propulsive efficiency. The primary goal is to ensure a sustained airborne state through the generation of lift equivalent to the ornithopter's weight. Commonly available materials have been employed in the construction of the ornithopter. SolidWorks flow simulator was utilized to simulate aerodynamics. A 1000mm length of the wing was subjected to a 3m/s air stream at a 5-degree angle of attack for the simulation. The simulated result, which represents a 2kg ornithopter, exhibited a lift force of 0.8N and a drag force of 0.2N. Further simulations were conducted at varying attack angles (from 0 to 35 degrees) to gauge the range of lift and drag coefficients. The investigation concludes that the constructed ornithopter should generate an upward thrust of 2.7N at a speed of 5m/s, even without wing flapping, ensuring controlled and stable flight.

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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.

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This study undertakes a comprehensive review of control techniques applicable to DC-DC power converters, categorized into Traditional Control (TC) methods and those based on Artificial Intelligence (AI). Succinct descriptions of prevalent strategies in both classifications are furnished, shedding light on their fundamental principles. Further, the current progress in the field is evaluated, anchoring the discussion in the provided categorization. In assessing the merits and potential drawbacks of each method, specific emphasis is laid on the target converter topology. Predominant topologies such as the elementary buck, boost, bidirectional buck-boost, and dual-active-bridge (DAB) are scrutinized. To furnish a thorough analysis and facilitate comparison of principal control methods, simulations of four fundamental off-the-shelf algorithms are undertaken, employing a 1 MHz switching frequency.

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This study presents an evaluation of a mathematical model designed for altitude and attitude control in quadcopters, employing Sliding Mode Control (SMC) in conjunction with the Kalman Filter algorithm. The developed mathematical model focuses on controlling the quadcopter's height along the z-axis and its attitude, encompassing roll, pitch, and yaw. Simulation results demonstrate that the quadcopter achieves stable control within a time span of 2 to 4 seconds. The designed control system has been simulated, implemented on a mini-quadcopter, and tested for the occurrence of chattering events. The incorporation of the SMC-Kalman Filter control system effectively mitigates chattering, resulting in enhanced stability for the quadcopter. This work show cases the potential of the proposed mathematical model in achieving precise and stable control in quadcopters, thus expanding the applicability of such systems in various applications.
Open Access
Research article
Dynamic Characteristic Analysis of Tri-Stable Piezoelectric Energy Harvester with Double Elastic Amplifiers
dawei man ,
yingying bai ,
qingnan hu ,
huaiming xu ,
gaozheng xu ,
liping tang
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Available online: 04-02-2023

Abstract

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In order to further improve the vibration energy harvesting efficiency of piezoelectric energy harvester under low frequency environmental excitation, this paper, based on the traditional magnetic tri-stable piezoelectric energy collector model, proposes a tri-stable piezoelectric energy harvester (TPEH+DEM) model with two elastic amplifiers which are installed between the U-shaped frame and the base and between the fixed end of the piezoelectric cantilever beam and the U-shaped frame respectively. Based on Hamilton principle, the motion equation of electromechanical coupling of TPEH+DEM system is established, and the analytical solutions of displacement, output voltage and power of the system are obtained by harmonic balance method. The effects of the mass of elastic amplifier, spring stiffness, magnet spacing and load resistance on the dynamic characteristics of energy harvesting of TPEH+DEM system are analyzed. The result shows that there are two peaks in the response output power of TPEH+DEM system in the operating frequency range. By adjusting the mass and stiffness of the elastic amplifier reasonably, the system can move into the inter-well motion under low external excitation intensity, and produce high output power. Compared with the traditional model which only has an elastic amplifier on the base of piezoelectric energy harvester, TPEH+DEM model has better energy harvesting performance under low frequency and low intensity external excitation.

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With the help of vector equations and MATLAB software, this paper studied the kinematics and kinetostatics of toggle-type transmission mechanism (hereinafter referred to as “toggle mechanism” for short) and attained the analytical expressions of displacement, speed, and acceleration of slider punch, and the force and moment balance equations of each component in the toggle mechanism with their inertia force taken into consideration. Then, the toggle mechanism was compared with conventional crank-link mechanism and their kinematic characteristics were comparatively analyzed. The proposed kinematics analysis method of toggle mechanism could figure out the kinematic characteristics of the target mechanism and reveal its operating advantages on the basis that its functional requirements are met, in this way, the research purpose of optimizing the design of the mechanism could be realized, and the attained conclusions could provide useful evidence for the design of other types of transmission mechanisms.

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Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

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