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IAES International Journal of Robotics and Automation (IJRA)
Published by Institute of Advanced Engineering and Science
ISSN : 20894856     EISSN : 27222586     DOI : -
Core Subject : Engineering,
Automotive Engineering Electrical & Electronics Engineering
Robots are becoming part of people's everyday social lives and will increasingly become so. In future years, robots may become caretaker assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. The IAES International Journal of Robotics and Automation (IJRA) is providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the latest achievement, future challenges and exciting applications of intelligent and autonomous robots. IJRA is aiming to push the frontier of robotics into a new dimension, in which motion and intelligence play equally important roles. Its scope includes (but not limited) to the following: automation control, automation engineering, autonomous robots, biotechnology and robotics, emergence of the thinking machine, forward kinematics, household robots and automation, inverse kinematics, Jacobian and singularities, methods for teaching robots, nanotechnology and robotics (nanobots), orientation matrices, robot controller, robot structure and workspace, robotic and automation software development, robotic exploration, robotic surgery, robotic surgical procedures, robotic welding, robotics applications, robotics programming, robotics technologies, robots society and ethics, software and hardware designing for robots, spatial transformations, trajectory generation, unmanned (robotic) vehicles, etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 512 Documents
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Robust and computationally efficient single-input fuzzy logic‑enhanced nonlinear PID control for a pneumatic servo system 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
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp397-414

Abstract

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.
Development of a low pressure Pneu-Nets actuator using room temperature vulcanizing silicon rubber Nur Rahmah Abdullah; Sylvi Febriana Rachmawati Irnadiastputri; Mohammad Ikhsan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp267-280

Abstract

Soft robotics offers potential advantages in achieving safer human-robot interaction compared to conventional rigid robots, making it relevant for stroke rehabilitation applications. A major challenge in developing soft actuators lies in selecting materials that balance mechanical performance and practical fabrication. This study investigates room-temperature vulcanizing (RTV) silicone rubber as an alternative to platinum-cured silicone rubber for Pneumatic-Networks (Pneu-Nets) actuators fabrication. The actuator was developed through mold casting with 3D-printed molds and characterized by its contact force and bending angle. This actuator produced a maximum force of 0.93 N and a bending angle of 244.5° at 52 kPa. Finite element analysis (FEA) was performed to simulate its mechanical behavior and validate experimental results. The simulation errors were quantified as 8.3% for contact force and 19.3% for bending angle at 30 kPa, confirming the feasibility of using condensation-cured silicone rubber for efficient soft actuator production.
Optimized mapping in 2D and 3D network on chip using Bat algorithm Maamar Bougherara; Rafik Amara; Amina Guidoum
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp488-502

Abstract

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.
Cascaded generalized predictive control for induction drives under constraints using particle swarm optimization Rachid Amrouche; Noureddine Boumalha; Farid Ykhlef; Djilali Kouchih
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp445-457

Abstract

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.
Fuzzy integral fault-tolerant control of an activated sludge process Ahmed Sami Hamana; Mounir Bekaik; Messaoud Ramdani
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp473-487

Abstract

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.
Integrating artificial intelligence and Internet of Things for solid waste management: a review Aditya Karle; Tejas Ramdas Pagare; Mohammed Ashaz Arkati; Prathmesh Prafull Tarapurkar; Praveen Kumar Bhojane
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp388-396

Abstract

The increasing pace of urbanization and industrial growth has intensified the challenges of solid waste management, demanding intelligent, data-driven, and sustainable solutions. This review explores how the combined application of artificial intelligence (AI) and the Internet of Things (IoT) is revolutionizing conventional waste management practices into intelligent, automated, and responsive systems. Through a comprehensive review of 43 scholarly publications, case analyses, and technical studies, this paper emphasizes how AI-based methods—such as learning algorithms, image recognition, and data-driven prediction—improve waste sorting precision, recycling performance, and material recovery efficiency-enhance waste segregation accuracy, recycling efficiency, and resource recovery. Simultaneously, IoT-based systems employing sensors, cloud platforms, and smart bins enable real-time waste monitoring, dynamic routing, and optimized collection logistics. Emerging technologies like blockchain for waste traceability, robotics for automated sorting, and advanced analytics for decision-making are also examined. Despite these advancements, challenges related to scalability, interoperability, cost, and data privacy persist. This review identifies current research gaps, proposes future directions, and emphasizes the importance of integrating AI and IoT with circular economy principles under Industry 5.0 to achieve sustainable, efficient, and human-centric waste management solutions.
Hybrid LUT–CORDIC architecture on FPGA for efficient and accurate trigonometric computation in robot manipulators Nia Gella Augoestien; Jazi Eko Istiyanto; Ahmad Ashari; Andi Dharmawan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp377-387

Abstract

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.
Tree diameter at breast height measurement based on computer vision Mohamad Razmil Abdul Rahman; Ishak Suleiman; Mohammed Al Haek; Yee Kit Chan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp458-472

Abstract

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.
SMAC: System for monitoring and automatic control of water, nutrients, and pH in hydroponic nutrient film technique Frengki Simatupang; Istas Pratomo Manalu; Eka Stephani Sinambela; Marojahan Mula Timbul Sigiro; Gerry Italiano Wowiling
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp365-376

Abstract

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.
Vector logic for robotic system on chip design and test Vladimir Hahanov; Svetlana Chumachenko; Eugenia Litvinova; Andrii Voronov; Oleh Demchenko; Nataliya Maksymova
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp415-426

Abstract

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.

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