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30,609 Article Results

Business intelligence and its impact on organizational decision-making: a systematic review

10.11591/ijict.v15i2.pp741-749
Cesar Patricio-Peralta , Hernan Peña Carnero , Jesús Mondragon , Adan Eugenio Contreras Angeles , Marina Vargas Vega , Walter Patricio Peralta , Marco Mayor Ravines , Juan Mayor Gamero , Cesar Paccha Rufasto
This research examines in detail how business intelligence (BI) supports and guides organizations in decision-making for their plans. The paper warns that the BI tool must be adapted to users' real needs. It's super crucial to keep all the important info in one spot. This optimizes resources and boosts the system's capabilities. The study used a set approach to tackle its main question. This included much searching through big science lists. Scopus and Web of Science were on the list. The search term was a particular word used to pinpoint documents. The review looked at studies from 2019 to 2025. Initially, we found 77 papers. Rules were then applied to include or exclude papers. These descartes criteria take into account the kind of paper, the language used, and how relevant it is to the subject. In the end, 24 papers went through the peer review process. These were reviewed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The findings indicate that the application of BI considerably improves the group’s ability to attain superior goals. Some research showed a 93% boost in productivity. Profits went up by 65%, too. These results come only from articles written in English, Spanish, and Portuguese. They mainly focus on explaining the functioning in wealthier nations. The results really show off the main perks of BI. It facilitates informed decision-making more easily for all organisations.
Volume: 15
Issue: 2
Page: 741-749
Publish at: 2026-06-01

Robust and computationally efficient single-input fuzzy logic‑enhanced nonlinear PID control for a pneumatic servo system

10.11591/ijra.v15i2.pp397-414
Khairun Najmi Kamaludin , Lokman Abdullah , Syed Najib Syed Salim , Zamberi Jamaludin , Mohd Nazmin Maslan , Mohd Shahrieel Mohd Aras , Mohd Fua’ad Rahmat , Arief Suardi Nur Chairat
Precision and robustness are essential for any automation actuator. Due to the nonlinear characteristics of the pneumatic actuator, advanced nonlinear control algorithms provide exceptionally precise control but are sensitive to disturbances. Owing to this factor, an adaptive element is embedded into the control structure to obtain a robust strategy by integrating single input fuzzy logic (SIFL) with the nonlinear hyperbolic PID controller (T NPID). SIFL characterizes a variable rate in the function while reducing computational complexity against an equivalent classical fuzzy logic (FL) by up to 36.5%. The signed distance SIFL selection is also a novel structure that has never been applied in the pneumatics control field. The robustness of the controller is analysed via dynamic stiffness and validated by applying multiple load disturbances. The improvement gained for the T NPID+SIFL’s transient rise time and multi-step IAE index under no load disturbance is 71.381% and 68.854%, respectively, compared with a classical sliding mode controller (SMC). Under a maximum 9 kg load disturbance (limited within the scope of this research), the T NPID+SIFL’s IAE index performance obtained an improvement of 68.638%. When compared with a baseline nonlinear hyperbolic PID (NH PID) strategy under no load disturbance, the steady state error and overshoot also improved by 74.797% and 15.385%, respectively. The results show outstanding performance compared with a robust controller as well as a similar baseline nonlinear PID control. Asymptotic stability analysis, such as the asymptotic tracking region (ATR), will be able to consolidate the trajectory tracking performance together with the experimental validation of a smooth trajectory, simulating a real-time robotic actuator under movement control.
Volume: 15
Issue: 2
Page: 397-414
Publish at: 2026-06-01

Fuzzy logic-based MPPT control for solar PV fed three-phase induction motor drive in water pumping applications

10.11591/ijape.v15.i2.pp573-580
K. Chitanya , Arjyadhara Pradhan , Babita Panda
The reliance on traditional energy sources for the agricultural water pumping framework leads to higher operational costs and environmental concerns. The partial shadings severely reduce the solar photovoltaic based water pumping framework efficacy. Owing to partial shading conditions, the output power of the photovoltaic array degrades, which reduces the water pumping output. Therefore, this article presents a low-cost solar photovoltaic fed three phase induction motor driven system, which helps for the rural water pump application. Here, maximum power point tracking of solar photovoltaic panel is done by fuzzy logic. Usually, the solar panel gives direct current power is stored in recharging battery and then setups and acts as source for voltage source inverter in the standalone model. A novel single step battery-low power transformation is used by developing a fuzzy maximum power track with buck-boost chopper that makes the total setup cost to reduced significantly. Mamdani fuzzy system is selected for maximum power point tracking (MPPT) controller because it has more intuitive and easier to understand If-Then rule bases. The suggested method is worked as a model having photovoltaic array, maximum power tracking with the buck-boost chopper, voltage source inverter, and three phase induction motor drive. The inverter is used sinusoidal pulse width modulation as the control algorithm. To check the simulation results the total set up is carried out in MATLAB software. The test results observed that the proposed prototype is useful. It’s efficacy i.e., 75% with in response time (1 sec) for different insulations and temperatures.
Volume: 15
Issue: 2
Page: 573-580
Publish at: 2026-06-01

Integration of wind energy with a single-ended primary inductor converter and a brushless DC motor for water pumping system

10.11591/ijpeds.v17.i2.pp1096-1104
Hassan Abdi Abi , Abdullahi Mohamed Isak , Suleiman Abdullahi Ali , Yakub Hussein Mohamed , Sowdo Mursal Abdi , Abdirisakh Khalif Osman
This paper explores a simulation-based study on a renewable energy system that integrates wind energy with a single-ended primary inductor converter (SEPIC) to drive a brushless DC (BLDC) motor for water pumping applications. The proposed system addresses the challenge of regulating the variable output of wind turbines by employing a SEPIC converter to provide a stable direct current (DC) voltage supply to the BLDC motor. The novelty of this work lies in the combined modeling and performance analysis of the wind turbine, SEPIC converter, BLDC motor, and electronic commutation in MATLAB/Simulink, optimized for energy-efficient off-grid pumping. Simulation results demonstrate that the SEPIC converter effectively stabilizes the wind-generated voltage, ensuring reliable motor operation under varying wind conditions. The proposed system exhibits high efficiency, stable dynamic response, and low maintenance requirements, making it a practical solution for water pumping in wind-rich regions where solar irradiance is limited, particularly for off-grid water pumping applications.
Volume: 17
Issue: 2
Page: 1096-1104
Publish at: 2026-06-01

Resilient EV charging station network design using AI algorithms

10.11591/ijpeds.v17.i2.pp1543-1552
Deepa Somasundaram , N. Krishnamoorthy , J. Vijay Anand , R. Priyanka , T. Santhana Krishnan , Kirubakaran Dhandapani
This paper proposes an AI-driven resilient network design framework for optimal electric vehicle (EV) charging station placement under stochastic demand and dynamic grid constraints. The proposed approach uniquely integrates long short-term memory (LSTM) based spatiotemporal demand forecasting with a hybrid genetic algorithm-particle swarm optimization (GA-PSO) model for multi-objective station placement. In addition, a deep reinforcement learning (DRL) agent is incorporated to enhance adaptive resilience under real-time grid disturbances. The framework minimizes installation cost, reduces user travel distance, and improves grid stability while ensuring equitable accessibility. The model is evaluated under multiple scenarios, including peak demand, station outages, renewable intermittency, and grid capacity reduction. Results demonstrate that the proposed hybrid AI framework achieves a resilience index of 0.92, reduces travel distance by 54%, and lowers installation cost by up to 16% compared to conventional approaches such as linear programming (LP) and K-means clustering. The integration of renewable energy further reduces peak grid dependency by 18%. The proposed methodology provides a scalable and practical solution for designing sustainable and resilient EV charging infrastructure in smart urban environments.
Volume: 17
Issue: 2
Page: 1543-1552
Publish at: 2026-06-01

Moth flame optimization based super twisting sliding mode MPPT controller for grid connected PV system

10.11591/ijape.v15.i2.pp703-711
Ujwala Gajula , Gouthami Eragamreddy , N. Malla Reddy , Remala Geshma Kumari , Veeranjaneyulu Gopu
Maximizing energy extraction while maintaining the stability of solar photovoltaic (PV) systems requires an effective and robust control strategy. This study proposes a novel control approach by integrating a super twisting sliding mode controller (STSMC) with the moth-flame optimization (MFO) algorithm to enhance battery energy management, power quality, and maximum power point tracking (MPPT) in grid-connected PV systems. The proposed MFO-STSMC controller combines the robustness of sliding mode control with the adaptive optimization capabilities of MFO, resulting in improved MPPT accuracy, reduced oscillations, and enhanced resilience to environmental disturbances and nonlinearities. Simulation results validate that the proposed method significantly outperforms conventional MFO-PI controllers, achieving accurate MPPT tracking under varying irradiance and temperature conditions, and ensuring stable operation. Moreover, the total harmonic distortion (THD) is reduced to 0.17% with MFO-STSMC, compared to 0.72% with MFO-PI, highlighting substantial improvement in power quality. The system is modeled and validated using MATLAB/Simulink, confirming the effectiveness of the proposed strategy in enhancing energy efficiency and grid stability.
Volume: 15
Issue: 2
Page: 703-711
Publish at: 2026-06-01

Adaptive telematics integration for enhanced EV fleet management and data acquisition

10.11591/ijape.v15.i2.pp808-817
Kavitha Kumaraswamy , Pasumarthi Usha , S. Ashok Kumar , Deekshitha Arasa , Suganthi Neelagiri
Telematic control units (TCUs) and on-board diagnostics (OBD-II) systems are commonly used to monitor vehicles and enable real-time communication. However, traditional OBD-II systems provide limited data, making it difficult to accurately detect faults and analyze performance, especially in hybrid, flex-fuel, and electric vehicles. A TCU is an embedded system installed in vehicles that enables wireless communication with external networks. This paper introduces a standalone device designed to seamlessly integrate with electric vehicles (EVs) by utilizing TCU capabilities to enhance data acquisition. The TCU uses a combination of sensors to collect important real-time vehicle data, such as GPS location, battery charge level, and voltage levels. The collected data is processed to generate meaningful insights that support decision-making and system optimization. The proposed system uses the TCU as a core component to transmit real-time data to a fleet management system (FMS). By providing enhanced data to the FMS, the system improves diagnostic accuracy, strengthens EV safety monitoring, and enables more efficient fleet management across diverse vehicle types. This approach allows deeper monitoring of EVs and improves overall fleet efficiency. The framework offers a cost-effective and scalable solution for advanced monitoring and optimization of electric vehicle fleets.
Volume: 15
Issue: 2
Page: 808-817
Publish at: 2026-06-01

Predictive modeling and optimization of paper mill using hybrid machine learning techniques

10.11591/ijape.v15.i2.pp692-702
Abhijit Singh Bhakuni , Sandeep Kumar Sunori , Pradeep Juneja
The paper has played a vital role in the life of humans from ancient times covering a vast range of applications such as writing, packaging, and printing. The present paper is presenting a comprehensive review of various optimization and control methodologies, ranging from conventional to advanced ones, pertaining to the paper mill. The final goal of these control strategies is to upgrade the mill’s production and quality in presence of multiple technical challenges such as nonlinear and multivariable nature of the involved processes, various disturbance parameters, and time delays. In this work, the integration of machine learning with paper mill process is illustrated. For any manufacturing process, the final product quality is the key goal. There are various traditional techniques which have already been practiced for final produced paper quality in paper mills. This paper highlights the capability of support vector machine (SVM) algorithm to assess the produced paper quality, capturing the two crucial inputs viz. the pulp consistency and the headbox level. The basic goal of this research is twofold, firstly it presents an exhaustive literature survey exploring various strategies which are practiced currently in the domain of control and optimization of various paper mill processes. Secondly, it intends to develop and evaluate various SVM and SVM-RF hybrid models using MATLAB for assessment of quality of final product on basis of two parameters- pulp consistency and head box level. Finally, genetic algorithm has been employed in MATLAB for multivariate optimization.
Volume: 15
Issue: 2
Page: 692-702
Publish at: 2026-06-01

LiDAR-based sensor fusion and navigation for indoor autonomous mobile robots in warehouse environments

10.11591/ijra.v15i2.pp295-306
Rifda Hakima Sari , Jazi Eko Istiyanto , Oskar Natan , Zaidan Hakim , Danang Lelono , Andi Dharmawan
An indoor navigation system for an autonomous mobile robot was developed using LiDAR-based perception and multi-sensor fusion. The system combines 2D LiDAR, inertial measurement unit (IMU), and wheel encoder measurements within a simultaneous localization and mapping (SLAM) framework to support real-time localization, while the ROS2 Nav2 stack manages global path planning and local obstacle avoidance through A*-based planning and costmap-driven control. Evaluation in a warehouse-like environment showed that the robot maintained stable localization with low drift and completed autonomous navigation missions with a success rate of 93.33%. During operation, the robot was able to avoid static obstacles consistently and adjust its trajectory in response to simple dynamic obstacles through online replanning. These results indicate that the proposed system is suitable for practical indoor logistics scenarios requiring reliable navigation in structured environments. At the same time, the findings suggest the need for further improvement to handle environments with higher dynamics and denser obstacle configurations.
Volume: 15
Issue: 2
Page: 295-306
Publish at: 2026-06-01

Design and implementation of NMPC for a two-DOF robotic arm using CasADi

10.11591/ijra.v15i2.pp307-318
Lahcen Boulbalah , Faiza Dib , Nabil Benaya , Khaddouj Ben Meziane
Achieving accurate joint-space tracking in multi-link robotic arms is complicated by strong configuration-dependent nonlinearities and mandatory actuator limits that classical controllers are structurally unable to enforce. This paper presents a nonlinear model predictive control (NMPC) scheme for a two-degree-of-freedom (2-DOF) serial robotic arm, implemented within the CasADi symbolic computing environment to leverage automatic differentiation and sparse interior-point solving. The complete set of Lagrangian equations of motion-inertia, Coriolis, and gravity terms-is incorporated directly into the optimizer's prediction model through fourth-order Runge-Kutta (RK4) integration, eliminating the need for linearization. Torque, velocity, and angle bounds are imposed as native hard inequality constraints at every step of the finite-horizon optimization. Systematic simulations pit the proposed NMPC against a Ziegler-Nichols-tuned decentralized PID at two distinct sampling periods. The NMPC achieved a 95% reduction in peak tracking error relative to PID (0.0058 rad vs. 0.1347 rad for Joint 1), with mean error decreases of 64.65% and 57.58% for Joints 1 and 2 respectively, at an average solver time of 0.053 s-comfortably within the 0.1 s control cycle. The findings demonstrate that online NMPC with unabridged nonlinear dynamics is computationally practical for real-time joint control on standard computing hardware.
Volume: 15
Issue: 2
Page: 307-318
Publish at: 2026-06-01

Constrained model predictive control for enhanced trajectory tracking in multi-DOF robotic manipulators

10.11591/ijra.v15i2.pp331-340
Shyamalagowri Murugesan , Gomathi Periyavattam Shanmugam , Mohammadha Hussaini Mohammed Ibrahim , Ramesh Ponnusamy
Controlling a multi-degree-of-freedom (multi-DOF) robotic manipulator is complicated by nonlinear dynamics, coupled joints, and constraints such as joint limits, actuator saturation, and collision avoidance. The focus of this proposed work is the development and implementation of constrained model predictive control (MPC) algorithms for robotic manipulators. The key features of this proposal include the use of the dynamics of the manipulator in the process of prediction and the ability for the controller to take optimal actions over a fixed time horizon, while ensuring that the full range of physical and safety constraints is satisfied. The proposed MPC framework incorporates a discrete-time state-space model of the robotic manipulator that can be optimized using quadratic programming (QP), which allows for the model to be expressed in a general stable form to enable optimization. Linear and nonlinear MPC approaches will be considered, but the emphasis will be on the feasibility of real-time implementation and robustness of the controller to modelling errors and disturbances from the environment. The algorithm can be used in simulation and on a physical multi-DOF robotic arm in applications ranging from trajectory tracking to obstacle avoidance and precision positioning of the end-effector. Compared to traditional control techniques like PID, and computed torque control proves the superiority of MPC in controlling dynamic constraints and increasing control accuracy. The research also discusses implementation techniques involving reduced-order models and efficient solvers to address real-time computational needs, enabling safe and effective deployment in sophisticated robotic devices.
Volume: 15
Issue: 2
Page: 331-340
Publish at: 2026-06-01

Design of beefsteak tomato harvesting robot system in greenhouse

10.11591/ijra.v15i2.pp353-364
Thien An Dinh , So Nam Phung , Tri Cong Phung
One challenge for tomato harvesting robots is that some of the tomato stems were not detectable because they were hidden behind the leaves or other obstacles. The primary objective of this research is to design, simulate, and experiment with a tomato harvesting robot and propose an improved detection algorithm to overcome the above problem. The suggested detection algorithm is designed to first detect the tomato fruit itself, and if the stem is not visible, the system will automatically adjust the camera's viewing angle to provide a better perspective and uncover the hidden stem. Simulation and experimental tests were carried out in a real tomato greenhouse to evaluate the cutting and holding mechanism, as well as the camera-based detection algorithm. These experimental results confirmed the effectiveness of the gripper and detection system and revealed several challenges in the harvesting algorithm. By integrating advanced algorithms for tomato detection and harvesting, this robot will reduce damage to the tomatoes, ensuring higher quality and yield.
Volume: 15
Issue: 2
Page: 353-364
Publish at: 2026-06-01

SMAC: System for monitoring and automatic control of water, nutrients, and pH in hydroponic nutrient film technique

10.11591/ijra.v15i2.pp365-376
Frengki Simatupang , Istas Pratomo Manalu , Eka Stephani Sinambela , Marojahan Mula Timbul Sigiro , Gerry Italiano Wowiling
Manual regulation of nutrient solution parameters in hydroponic systems often causes instability and delayed corrective actions. This study presents an Internet of Things (IoT) based system monitoring and automatic control (SMAC) for a nutrient film technique (NFT) hydroponic system to automatically regulate pH, total dissolved solids (TDS), and temperature. The proposed system integrates real-time sensors, automatic actuators, and dual-microcontroller architecture, in which an Arduino Uno performs local control while an ESP32 enables wireless IoT monitoring. An experimental systems engineering approach was applied for system design and performance evaluation. Automatic temperature compensation (ATC) was incorporated into pH and TDS measurements to improve reliability under varying thermal conditions. Experimental results indicate that the temperature sensor achieved an average error of 0.13 °C. The control algorithm corrected pH deviations gradually by approximately ±0.31 pH units (pH Up) and ±0.38 pH units (pH Down) per cycle without overshoot. Nutrient concentration control increased TDS by about 75 ppm per cycle under low-TDS conditions. Stability testing confirmed that pH and TDS remained within optimal ranges after disturbances, while safety mechanisms operated reliably under abnormal temperatures. The results demonstrate that the proposed SMAC system provides accurate, stable, and adaptive control suitable for precision and sustainable hydroponic applications.
Volume: 15
Issue: 2
Page: 365-376
Publish at: 2026-06-01

A path generation and control framework for 6-DOF robot in precision writing and drawing

10.11591/ijra.v15i2.pp281-294
Khoi Hoang Dinh , Khanh Tran Duy , Thien Bui Thanh , Quy Vo Quoc
Robots are becoming increasingly integrated into everyday life, not only in industrial applications but also in creative, educational, and entertainment contexts. With recent advancements, collaborative robots are now lighter, safer, and easier to deploy alongside humans, making them well-suited for tasks that require precision and adaptability. This paper presents an integrated control framework for the ABB GoFa 6-DOF collaborative robot, enabling it to autonomously perform precise writing and drawing tasks. The system leverages CAD-based path design in SolidWorks and ABB RobotStudio’s AutoPath tool to generate motion trajectories from a library of modeled characters, symbols, and figures. A socket-based communication interface connects the robot controller with a user-friendly human-machine interface (HMI), allowing users to input custom text or select predefined figures in real time. The framework has been implemented and validated on the physical ABB GoFa robot, demonstrating high accuracy, repeatability, and usability for applications such as public exhibitions and educational settings.
Volume: 15
Issue: 2
Page: 281-294
Publish at: 2026-06-01

Vector logic for robotic system on chip design and test

10.11591/ijra.v15i2.pp415-426
Vladimir Hahanov , Svetlana Chumachenko , Eugenia Litvinova , Andrii Voronov , Oleh Demchenko , Nataliya Maksymova
Artificial Intelligence and vector logic of computing do not contradict but cooperate and enrich each other. Logic is the law of existence and development of emerging computing. Logic is functions and structures, models and algorithms, phenomena and processes. Any computing, including artificial intelligence, is logic and nothing else. Emerging computing devices today have hundreds of systems on a chip and memory blocks, which are interconnected by thousands of connecting wires. This encompasses all the logic, functionalities, and structures, which are subject to testing by system methods. To achieve this, a logic vector serves as a generic form for describing functions, structures, and buses in modeling for the simulation of test sets and logic faults as address. Chip-let Interconnect bus is also a logical functionality or structure. They must be tested to diagnose defects by system logic mechanisms. The latter involves modeling to automatically obtain data structures, followed by good-value simulation and simulation of all fault combinations, such as addresses, on the buses segment. For this purpose, vector logic is used to describe functionalities and structures, models and algorithms, faults and tests. Mechanisms and application that assume a harmonious relationship between the model and the algorithm for their processing are considered.
Volume: 15
Issue: 2
Page: 415-426
Publish at: 2026-06-01
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