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Hybrid LUT–CORDIC architecture on FPGA for efficient and accurate trigonometric computation in robot manipulators

10.11591/ijra.v15i2.pp377-387
Nia Gella Augoestien , Jazi Eko Istiyanto , Ahmad Ashari , Andi Dharmawan
Although computational resources on robots are often limited, real-time, accurate computation of trigonometric functions is essential in robot manipulators, particularly for forward and inverse kinematics, dynamic analysis, trajectory planning, and motion control. The LUT method requires a large number of LUTs to improve accuracy. The accuracy of the CORDIC method is highly dependent on the number of computational latencies, which affects the computation speed. This paper combines two general approaches for computing trigonometric functions on robot manipulators that improve accuracy without increasing resource utilization and computational latencies. The design uses a 10-bit format (0.125° input resolution and 2-10 output precision) and is implemented in VHDL on a Xilinx Artix-7 XC7A100T-CSG324 FPGA. Compared with a CORDIC-only baseline, the maximum absolute error is reduced from 0.083007812 to 0.009801151 for sine and from 0.079101563 to 0.008901377 for cosine, while MSE drops from 2.4031×10-4 and 2.32974×10-4 to 5.87754×10-6 and 5.87862×10-6, respectively. The hybrid core also reduces slice usage from 81 to 69 and shortens computation time from 35.271 ns to 30.627 ns, making it suitable for resource-constrained real-time robotic control.
Volume: 15
Issue: 2
Page: 377-387
Publish at: 2026-06-01

Fuzzy integral fault-tolerant control of an activated sludge process

10.11591/ijra.v15i2.pp473-487
Ahmed Sami Hamana , Mounir Bekaik , Messaoud Ramdani
This paper presents a fuzzy integral fault-tolerant controller (FIFTC) for robust regulation of substrate and dissolved oxygen in activated sludge processes (ASP). The nonlinear dynamics of the process are represented using an augmented Takagi–Sugeno (TS) fuzzy model, which includes an additional vector representing the integral state to improve tracking accuracy. A fuzzy proportional-integral (PI) observer is employed to estimate states and detect actuator faults, particularly in the aeration system. Controller and observer gains are computed by solving linear matrix inequalities (LMIs), while an H∞ performance criterion, defined by the parameter, ensures effective disturbance attenuation and bounds the error energy. In the simulation, we considered actuator faults of the loss of effectiveness (LOE) type. Simulation results demonstrate that FIFTC significantly outperforms classical linear quadratic regulator (LQR) in terms of tracking accuracy, robustness, and fault tolerance, even under partial actuator failures and external disturbances. The proposed FIFTC control strategy, which leverages fuzzy modeling, robust observers, and LMI-based optimization, provides significant benefits, primarily by improving efficiency, reducing energy consumption, and enhancing robustness.
Volume: 15
Issue: 2
Page: 473-487
Publish at: 2026-06-01

Cascaded generalized predictive control for induction drives under constraints using particle swarm optimization

10.11591/ijra.v15i2.pp445-457
Rachid Amrouche , Noureddine Boumalha , Farid Ykhlef , Djilali Kouchih
This paper presents a cascaded generalized predictive control (CGPC) strategy for induction motor drives under operational constraints, optimized through particle swarm optimization (PSO). In the proposed scheme, the outer loop regulates the motor speed, while the inner loop controls torque and flux, ensuring accurate multi-level regulation. PSO is employed to optimally tune the prediction horizon and weighting factors, enhancing robustness, transient response, and disturbance rejection. Unlike conventional GPC–PSO approaches that neglect explicit constraint handling, and linear matrix inequalities (LMI)-based model predictive controller (MPC) methods that impose high computational costs, the proposed CGPC–PSO achieves both constraint management and real-time efficiency. Moreover, compared with Neural-MPC strategies that require retraining for each system, the proposed method provides generalizable and adaptive control without sacrificing computational performance. Simulation results validate the effectiveness of the approach, demonstrating superior trajectory tracking, robustness against parameter variations, and improved dynamic performance compared with RST, LMI, and neural-MPC controllers. These findings position CGPC–PSO as a promising candidate for advanced induction motor drive applications.
Volume: 15
Issue: 2
Page: 445-457
Publish at: 2026-06-01

Vector-logic models of digital circuits for simulation and rendering

10.11591/ijra.v15i2.pp319-330
Vladimir Hahanov , Svetlana Chumachenko , Eugenia Litvinova , Andrii Voronov , Oleh Demchenko , Nataliya Maksymova
Vector-logical in-memory computing for solving modelling for simulation (MOSI) problems by using read-write transactions free of processor instructions is proposed. A parser mechanism has been developed for converting the HDL code of the logical circuit into the internal vector-logical data structures of the MOSI service, addresses of logical vectors of elements. Deductive vectors are generated from the vector-logic model of the digital circuit for fault as address simulation of the input test sets. A mechanism for modelling a fault simulation matrix as the addresses of the deductive vector bits of each element on the test set has been created. The results of good-value and fault as address simulation are rendered and synchronized in the good-value simulation, fault as address simulation, fault simulation matrices on the input set, and on the lines of the logical circuit displayed on the monitor. Modeling and simulation mechanisms encoded and verified using examples of logical circuits of the ISCAS library. The scientific novelty is represented by vector-logical models of digital circuit elements, good-value simulation of test set as address and fault as address simulation of a digital circuit, and fault as address simulation of the input set, using a quadratic simulation matrix.
Volume: 15
Issue: 2
Page: 319-330
Publish at: 2026-06-01

Tree diameter at breast height measurement based on computer vision

10.11591/ijra.v15i2.pp458-472
Mohamad Razmil Abdul Rahman , Ishak Suleiman , Mohammed Al Haek , Yee Kit Chan
Diameter at breast height (DBH) is a crucial metric in forestry, serving as a key input for estimating timber volumes and biomass, assessing forest health, and aiding in biodiversity and climate change studies. However, traditional measurement methods practiced today are time-consuming and labour-intensive, while many advanced methods introduced in recent years require high upfront costs, limiting wide adoption by small-scale institutions and projects. This research paper aims to explore innovative approaches to DBH measurement that balance accuracy with cost-effectiveness, ultimately contributing to the broader goals of sustainability and environmental protection. In this paper, the authors propose an automated DBH measurement method, extracting the value from smartphone RGB images through the utilization of computer vision techniques and mathematical algorithms. By incorporating tree distance data in Phase 3 of the study, the proposed method achieved accuracy comparable to manual tape measurements while significantly reducing the time and resources required for fieldwork. Specifically, 74 out of 143 trees (51.7%) had an estimated DBH that fell within 1 cm of the actual measurements, resulting in an absolute mean error (MAE) of 1.10 cm, root mean square error (RMSE) of 1.80 cm, and relative root mean square error (RRMSE) of 6.0%. Thus, this hybrid approach offers a promising solution for forestry applications, enhancing both the efficiency and accessibility of DBH data collection.
Volume: 15
Issue: 2
Page: 458-472
Publish at: 2026-06-01

A comprehensive review of sound source localization methods for robotics

10.11591/ijra.v15i2.pp257-266
Muhammad Akmal Aliff , Emerson Joseph Raja
Sound source localization (SSL) is a key technology in robotics that allows machines to detect and locate auditory cues in real time. This review provides a thorough examination of SSL techniques classified into classical, artificial intelligence (AI), and hybrid methods. Classical methods, which account for 44% of reviewed studies, excel in computational efficiency and reliability under controlled conditions but have limitations in dynamic environments. AI methods, which account for 16% of studies, use deep learning to adapt to complex scenarios, but they require large datasets and computational resources. Hybrid methods, which combine classical signal processing and AI, are the most robust and accurate, with an average accuracy of 97.45%. The review also looks at the role of microphone arrays in SSL performance, revealing that systems with ten or more microphones achieve the highest accuracy of 99.23%, while single- and dual-microphone systems still perform competitively (97.60% and 97.21%, respectively). These findings suggest that hybrid methods combined with larger microphone arrays are the most effective SSL solution in robotics, balancing precision and adaptability. This paper discusses current SSL trends, challenges, and future research directions, providing insights for the development of advanced auditory systems capable of reliable performance in dynamic, real-world environments.
Volume: 15
Issue: 2
Page: 257-266
Publish at: 2026-06-01

Optimized mapping in 2D and 3D network on chip using Bat algorithm

10.11591/ijra.v15i2.pp488-502
Maamar Bougherara , Rafik Amara , Amina Guidoum
Communication within system-on-chip (SoC) architectures has evolved significantly to keep pace with the growing complexity of modern applications. To overcome the limitations of traditional interconnects, network-on-chip (NoC) has emerged as a scalable and efficient communication solution. Although early NoC designs relied heavily on 2D architectures, their physical and performance constraints have led to the rise of 3D NoC architectures, which offer better spatial integration and improved performance. In order to automate the NoC design process, a number of electronic design automation (EDA) tools and optimization algorithms are employed to help designers achieve efficient and high-performance designs. Within this EDA framework, one of the most critical stages is the core placement or application mapping phase, where computational tasks are allocated to the processing elements of the architecture. This step is very hard due to its combinatorial nature, and its optimization is essential since it directly impacts communication cost, energy consumption, and overall system performance. To address this challenge, numerous heuristic and metaheuristic algorithms have been explored for both 2D and 3D NoCs. In this paper, we propose an adaptation of the bat algorithm to solve the mapping problem in both 2D and 3D NoC architectures, with the objective of minimizing communication cost. The proposed approach is evaluated and compared against other optimization methods to assess its effectiveness in enhancing NoC performance within the EDA framework.
Volume: 15
Issue: 2
Page: 488-502
Publish at: 2026-06-01

Residual reinforcement learning for disturbance-resilient control under modeling uncertainties

10.11591/ijece.v16i3.pp1175-1187
Abolanle Adetifa , Rexcharles Enyinna Donatus , Daniel Udekwe
Modern control systems must operate reliably in the presence of modeling uncertainties and external disturbances, conditions under which conventional fixed-gain controllers often exhibit performance degradation. This paper proposes a residual reinforcement learning framework for disturbance-resilient pitch-rate control of an aircraft longitudinal model. A classical proportional-integral-derivative (PID) controller is employed as a stabilizing baseline, while a deep deterministic policy gradient (DDPG) agent learns a bounded residual control signal to compensate for unmodeled dynamics and external perturbations. To promote favorable transient behavior, the learning process incorporates transient-aware and reference-model-based reward shaping, while actuator constraints are enforced within the environment dynamics. Simulation results demonstrate that the proposed residual controller achieves a superior balance between response speed, overshoot, and tracking accuracy compared with both the standalone PID controller and a pure DDPG-based controller. In particular, the residual architecture significantly reduces overshoot and tracking error while preserving fast transient response and providing robust disturbance rejection under large pitching moment disturbances. These results indicate that residual reinforcement learning offers a practical and effective approach for enhancing robustness and performance in safety-critical flight control applications.
Volume: 16
Issue: 3
Page: 1175-1187
Publish at: 2026-06-01

Evaluation of machine learning approach in modelling and forecasting real gross domestic product growth: a comparative study

10.11591/ijece.v16i3.pp1339-1349
Moiz Qureshi , Muhammad Ismail , Nawaz Ahmad , Ibrar Hussain , Abbas Ali Ghoto , Jolita Vveinhardt
This study aims to provide an efficient and accurate machine-learning approach for modelling and forecasting the real gross domestic production (GDP) in the context of Pakistan. The study forecasts Pakistan's GDP growth rate using different forecasting models, such as naïve, seasonal naïve (SNaive), smoothing, and k-nearest neighbors (k-NN). Machine learning algorithms provide additional advice for data-driven decision-making. According to the findings, the k-NN-based forecasting gives minimum mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) compared to the other three models. Economic policymakers can use accurate models to measure significant economic activity and formulate plans. The results indicate that the model produced accurate projections of future GDP levels for Pakistan.
Volume: 16
Issue: 3
Page: 1339-1349
Publish at: 2026-06-01

Utilizing phase congruency technique in reception performance optimization of UWB signals in multipath fading channels

10.11591/ijece.v16i3.pp1272-1285
Nadir Mohamed Abdelaziz
Ultra-wideband (UWB) technology enables high-data-rate communications and centimeter-accurate indoor localization but suffers severe degradation in multipath fading channels due to dense multipath components, narrowband interference (NBI), and low signal-to-noise ratios (SNR). Conventional energy-based detection methods, including Rake receivers, fail under these conditions due to amplitude sensitivity. This paper introduces a phase congruency (PC)-based selective Rake (S-Rake) receiver that exploits phase alignment across frequencies rather than signal magnitude for robust feature detection. The proposed method computes PC metrics via Hilbert transforms and sub-band decomposition to identify phase-aligned multipath components, guiding S-Rake finger selection (4, 8, and 128 fingers) and time-of-arrival (TOA) estimation. Simulations using 6th-derivative Gaussian pulses over IEEE 802.15.3a CM4 channels (NLOS, 4-10 m) with AWGN and IEEE 802.11a interference (SIR=-30 dB to 0 dB) demonstrate that PC-based S-Rake achieves 4 dB SNR gain at BER=10⁻⁴ over conventional Rake under high interference. DS-UWB with PC outperforms TH-UWB by 3× lower BER at SIR=-30 dB. Increasing Rake fingers from 4 to 128 reduces BER by >40% and improves TOA accuracy by 62% (RMSE: 1.8 ns → 0.68 ns). PC maintains BER=10⁻³ at SIR=0 dB where conventional methods fail. Results establish PC as a transformative paradigm for interference-resilient UWB applications including IoT localization and 5G-coexistent communications.
Volume: 16
Issue: 3
Page: 1272-1285
Publish at: 2026-06-01

Study on the design and comparison of permanent magnet synchronous motors for electric vehicle applications

10.11591/ijece.v16i3.pp1107-1117
Pham Ngoc Sam , Tran Duc Chuyen
In this research, the authors present a study analysis and compares two types of embedded internal permanent magnet synchronous motors (IPMSM) with U-type and V-type magnet configurations using finite element method (FEM) modeling to apply these motors to the currently popular electric vehicle industry. Parameters such as magnetic flux density, torque, cogging torque, back electromotive force (back-EMF), torque oscillation, and harmonic components were analyzed and compared; thereby identifying the advantages and disadvantages of the two IPMSM structures. Specifically, the V-type IPMSM motor offers higher efficiency, more stable torque, and a higher quality back electromotive force waveform with lower losses, making it suitable for high-performance applications such as electric vehicles and industrial automation. Meanwhile, the U-type structure has lower cogging torque, suitable for low-speed applications or those requiring high precision. Simulation results from the ANSYS Maxwell software show that the IPMSM motor is energy-efficient, has high power density, and operates smoothly, allowing for rapid acceleration, long range, compact configuration, and low maintenance; it uses permanent magnets on the rotor to eliminate losses, making electric vehicles lighter and more efficient than traditional motors.
Volume: 16
Issue: 3
Page: 1107-1117
Publish at: 2026-06-01

Integrating Sustainable Development Goals into educational information systems: toward a theoretical model for sustainable school management

10.11591/ijece.v16i3.pp1350-1359
Veri Arinal , Miswanto Miswanto , Kiki Setiawan , Agus Tanti Rahayu
This research addresses the critical challenge of implementing Sustainable Development Goal (SDG) 4, "Quality education," in Indonesian secondary schools. While national policies exist, schools lack a systematic digital tool to plan, monitor, and evaluate sustainability-based activities against concrete SDG indicators. To bridge this gap, this study employs a six-cycle design science research (DSR) methodology to develop a theoretical model for a sustainable education information system. The model is designed to integrate SDG principles into school management, enabling systematic data handling, adaptive curriculum functions, and real-time monitoring. A web-based prototype was developed using a React.js frontend and Node.js backend and evaluated through a mixed-methods approach. Data from interviews with 15 administrators and surveys of 97 teachers (yielding a usability satisfaction score of 4.34/5) validated the model’s effectiveness in making educational administration more efficient, transparent, and quality-oriented. The resulting artifact serves as a foundational technical and managerial reference for schools, education offices, and policymakers to leverage information technology in fostering a sustainable, participatory learning culture aligned with the SDGs.
Volume: 16
Issue: 3
Page: 1350-1359
Publish at: 2026-06-01

Radar-based gesture recognition simulation for unmanned aerial vehicles command interpretation

10.11591/ijece.v16i3.pp1227-1235
Denny Dermawan , Freddy Kurniawan , Yenni Astuti , Paulus Setiawan , Lasmadi Lasmadi , Uyuunul Mauidzoh , Bambang Sudibya
Radar-based gesture recognition has emerged as a robust alternative to vision-based systems, particularly in environments where lighting and privacy pose challenges. This study presents a simulation approach for recognizing hand gestures to control unmanned aerial vehicles (UAVs) using radar signals. Five discrete gestures, i.e., TakeOff, Land, MoveForward, TurnLeft, and stop, were defined and modeled in MATLAB to generate synthetic radar signals. From each sample, four time-frequency domain features were extracted: duration, maximum amplitude, dominant frequency, and root mean square (RMS). A dataset of 500 samples (100 per class) was classified using three supervised learning models: support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree. The k-NN classifier achieved the highest accuracy of 96%, demonstrating the feasibility of lightweight classifiers for gesture recognition using low-complexity features. These results highlight the potential of radar-based interfaces to replace traditional remote controls in UAV operation. The proposed simulation framework contributes to the development of intuitive, non-contact human-machine interaction systems.
Volume: 16
Issue: 3
Page: 1227-1235
Publish at: 2026-06-01

Flashover of a polluted high voltage insulator under electric field distribution

10.11591/ijece.v16i3.pp1097-1106
Zainab Abdullah , Izham Zainal Abidin , Miszaina Osman , Nurulazmi Abd. Rahman , Muhammad Shafiq
This study investigates the effect of surface pollution on a single-unit 11 kV glass suspension insulator using two-dimensional (2D) axisymmetric simulations in COMSOL Multiphysics. The developed model incorporates the electrical properties of glass, cement, steel electrodes, surrounding air, and a uniform pollution layer, with an applied AC voltage of 11 kV under quasi-static conditions. Simulation results demonstrate pronounced electric field intensification in the polluted configuration, particularly at the air–glass–cap triple junction region, where localized electrical stress is significantly higher compared to the clean condition. While the clean insulator operates within IEC 60383 recommended limits, the polluted model exhibits elevated peak electric field magnitudes, indicating increased flashover vulnerability. The findings highlight the strong influence of surface contamination, material permittivity, and geometric configuration on electric field distribution along the creepage path. This study establishes a reliable and computationally efficient predictive framework for optimizing insulator design, improving maintenance strategies, and enhancing the long-term reliability of high-voltage transmission systems, especially in pollution-prone environments.
Volume: 16
Issue: 3
Page: 1097-1106
Publish at: 2026-06-01

Machine learning-driven analysis of user bandwidth allocation and performance in 5G heterogeneous network: a survey

10.11591/ijece.v16i3.pp1236-1248
Pang Wai Leong , Raymond Chia , Phang Swee King , Goh Hui Hwang , Chan Kah Yoong , Chung Gwo Chin
A key foundation of 5G heterogeneous networks (HetNets) is the use of network slicing, which divides bandwidth into multiple logical networks and accounts for each function’s requirements. Currently, various machine learning (ML) models are being implemented into the network slicing algorithm to allocate bandwidth dynamically. The network slicing algorithm analyzes the traffic and allocates bandwidth based on the current services using a network-centric approach. However, limited work is found on further studying the impact of user-centric algorithms in bandwidth allocation. This paper presents the network slicing used in 5G and the limitations of these algorithms. A detailed review of user-centric bandwidth allocation algorithms is presented, along with a critical review of ML algorithms for traffic prediction and resource allocation decisions. Finally, the technology gaps and opportunities of the existing works are reported, and the direction for further research of ML in user-centric bandwidth allocation algorithms is tabulated.
Volume: 16
Issue: 3
Page: 1236-1248
Publish at: 2026-06-01
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