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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 3 Documents
 1 
Search results for , issue "vol 4, no 3 (2026)" : 3 Documents clear
Vision-Based SCARA Robot System for Automated Gear Sorting Using Missing-Tooth Detection and Weight Measurement Tran-Thanh-Thuy Nguyen; Nguyen-Phung-Hung Tu; Thanh-Phuc Nguyen; Trung-Hieu Tran; Huu-Thang Dang; Tu-Duc Nguyen; Hoang-Lam Le; Thi-Hong-Lam Le; Thi-Ngoc-Thao Nguyen; Van-Hiep Nguyen; Thanh-Binh Nguyen; Trong-Bang Tran; Binh-Hau Nguyen; Van-Dong-Hai Nguyen; Duc-Huy Do
Control Systems and Optimization Letters Vol 4, No 3 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i3.301

Abstract

This paper implements an automated gear sorting framework that combines machine vision with in-line mass testing on a SCARA robot platform for industrial quality inspection. Gear flaws like missing tooth profiles or weight variations compromise transmission reliability, often leading to unexpected mechanical breakdown. The authors address this by pairing a vision-based defect algorithm - which catches missing teeth - with a load cell that flags hidden structural voids or density loss. Unlike single-camera systems that miss internal flaws, our approach brings optical and weight metrics together inside one SCARA architecture for multi-modal inspection. The cell ties image processing and kinematic models directly to end-effector control, driving real-time pick-and-place sorting. Benchtop testing over 50 consecutive runs showed 100% sorting accuracy under a ±1 g weight tolerance, maintaining consistent repeatability. Adding physical mass validation to visual checks significantly lowers the chance of passing defective internal parts. Using an Epson LS3-401S SCARA robot with ±0.02 mm repeatability, the prototype confirmed its operational stability in controlled tests, proving its potential for production-line integration.
Application of SSCNET III Network in Design and Interpolation Control of a 3-Axis AC Servo for Logo Drawing Nguyen Thanh Dat; Tu-Duc Nguyen; Ngoc-Vu Tran; Minh-Khang Ly; Hai-Thanh Nguyen; Ho-Dang-Khoa Pham; Cao-Thien-Tuong Nguyen; Thanh-Tung Nguyen; Huu-Phuc Pham; Tien-Dung Vu
Control Systems and Optimization Letters Vol 4, No 3 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i3.315

Abstract

In the current context, automated control systems play an essential role in enhancing the productivity and flexibility of production lines. Particularly in fields such as precision mechanics, CNC machining, and industrial robotics, the simultaneous and accurate control of multiple motor axes is a decisive factor in product quality. AC Servo motors are widely utilized in motion control systems due to their exceptional capability to control position, speed, and torque with outstanding precision. Achieving synchronous control of multiple servo axes—especially for complex interpolation tasks—requires a system with fast signal processing, low latency, and highly stable communication. Mitsubishi's SSCNET III, a high-speed communication network dedicated to servos, effectively addresses these requirements by seamlessly connecting multiple servo axes to a central controller. This paper presents an experimental study on a 3-axis interpolation model integrated with Programmable Logic Controller (PLC) and Human-Machine Interface (HMI) technologies. This integration aims to automate operations and alternate the operation of the lead screw axes, enabling system monitoring without the need for manual or semi-automatic intervention. Based on experimental and evaluation methods, the research demonstrates the potential to enhance the accuracy of interpolation control.
Hybrid Deep Learning Architectures for Robust Lane Detection in Autonomous Driving: A Comprehensive Review Asif Uzzaman; Benchang Wei
Control Systems and Optimization Letters Vol 4, No 3 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i3.287

Abstract

Lane detection plays a crucial role in autonomous driving systems, as it directly affects path planning, steering control, and overall driving safety. Early solutions based on conventional computer vision techniques offered initial progress, but their sensitivity to changing lighting, weather, and road conditions restricted their reliability in real-world scenarios. With the rise of deep learning, semantic segmentation approaches have significantly enhanced detection accuracy. However, models relying on a single network structure often struggle to balance broad contextual understanding with precise spatial detail. To overcome these limitations, recent research has increasingly focused on hybrid architectures that combine multiple complementary design strategies.This review systematically examines hybrid lane detection methods that integrate elements such as segmentation and detection pipelines, attention mechanisms, multi-scale feature extraction, and temporal modeling. We introduce a structured classification of hybrid frameworks, review key representative approaches, and analyze performance patterns across commonly used benchmark datasets. Ongoing challenges—including accurate detection of thin lane markings, robustness under occlusion, and generalization across diverse driving environments—are also discussed. To provide practical insight, a case study of a context-aware and spatially focused hybrid model is presented. Finally, future research directions are explored, including transformer-based designs, multi-modal sensor fusion, and computationally efficient solutions for real-time deployment. This review seeks to unify scattered research efforts and support the development of more reliable and robust lane detection systems for autonomous vehicles.

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