Current Issue

  • EditorialFebruary 1, 2025

    Guest Editorial: The 24th International Conference on Control, Automation, and Systems (ICCAS 2024)

    Choon Ki Ahn, Kyoung Kwan Ahn, Jong Min Lee

    International Journal of Control, Automation, and Systems 2025; 23(2): 359-359

    https://doi.org/10.1007/s12555-025-9901-3

    Abstract

    Abstract : The International Journal of Control, Automation, and Systems is pleased to present this special issue, featuring selected research from the 24th International Conference on Control, Automation, and Systems (ICCAS 2024), held at Jeju Shinhwa World, Jeju, Korea, from October 29 to November 1, 2024. This conference introduced a dual submission option for the first time, allowing participants to submit extended abstracts to ICCAS while concurrently submitting full papers to IJCAS, strengthening the synergy between conference presentations and journal publications. ICCAS 2024 brought together over 400 research papers from 21 countries, fostering discussions and collaborations that advance the fields of control, automation, robotics, and systems engineering. Among the many high-quality submissions, 43 papers underwent an expedited yet rigorous review process, with 32 ultimately selected for this special issue. These works highlight both foundational theories and groundbreaking applications, covering advanced control strategies, optimization techniques, and machine learning applications across robotics, autonomous systems, and industrial automation. They address key challenges in pose estimation, predictive control, networked systems, and fault-tolerant designs, driving innovations that bridge theoretical development and practical implementation. The success of ICCAS 2024 reflects the dedication of our research community and the continued pursuit of excellence in our field. We extend our sincere gratitude to all contributors, reviewers, and attendees whose efforts made this forum possible.We invite you to explore the articles in this special issue, which present novel ideas and solutions poised to shape the future of control, automation, and systems engineering. The IJCAS editorial board remains committed to identifying and publishing outstanding research from future editions of ICCAS, further strengthening the collaboration between the conference and the journal.

  • Special Issue: ICCAS 2024February 1, 2025

    A Simple Demonstrator for Multi-agent Circular Formation Control With Collision Avoidance Using Distributed Model Predictive Control

    Seongheon Kim, Guilherme Araujo Pimentel, Yoonsoo Kim*, and Alain Vande Wouwer*

    International Journal of Control, Automation, and Systems 2025; 23(2): 360-369

    https://doi.org/10.1007/s12555-024-0542-8

    Abstract

    Abstract : This paper presents the development of a didactic robotic demonstrator to study the circular formation problem (CFP) of multi-agent systems based on distributed model predictive control (DMPC). CFP can arise in various fields, such as robotics, drones, and space applications. This study proposes a method for multiple agents to sequentially enter a circular orbit, one by one, and move along the orbit at a predefined speed while maintaining a desirable distance from each other. Due to the advantages of DMPC, the proposed method allows each agent to independently generate control inputs while considering various types of constraints related to collision avoidance with the other agents. The methodology is tested and validated with three differential drive robots, i.e., Turtlebots.

  • Special Issue: ICCAS 2024February 1, 2025

    Development of Neutral Zone-based Lumbar Support Wearable Robot Utilizing an Electroadhesive Clutch

    Garam Lee, Eunho Sung, Joowan Kim*, Jaeheung Park, and Keewon Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 370-381

    https://doi.org/10.1007/s12555-024-0518-8

    Abstract

    Abstract : This paper presents the development of an ultra-lightweight, ultra-low-power lumbar support robotic device designed to alleviate low back pain (LBP) in elderly and weak individuals. The robotic device utilizes an electroadhesive clutch, which reduces energy consumption and weight compared to conventional actuators. The proposed robotic device employs dynamic control to adjust support based on the wearer’s lumbar movement, using an inertial measurement unit (IMU) sensor to modulate intra-abdominal pressure (IAP) and enhance spinal stability. The concept of the neutral zone (NZ) is applied to determine the range of motion where the robotic device operates, ensuring that the clutch is disengaged within the NZ where no support is needed and engaged outside the NZ to provide necessary assistance. Experiments conducted on a dummy model simulating human lumbar movements demonstrated the robotic device’s effectiveness in modulating abdominal pressure and providing adaptive lumbar support. This indicates its potential as an effective assistive device compared to conventional braces.

  • Special Issue: ICCAS 2024February 1, 2025

    Robust 6D Pose Estimation Using Dual Active Marker System

    Hyeon-Ju Choi, Yeong-Bin Kim, Bum Yong Park, and Dong-Hyun Lee*

    International Journal of Control, Automation, and Systems 2025; 23(2): 382-391

    https://doi.org/10.1007/s12555-024-0437-8

    Abstract

    Abstract : This paper presents the development of a novel dual active marker system designed to address the challenges of traditional marker-based pose estimation methods. Conventional approaches relying on multiple 2D or volumetric markers often face issues such as marker loss, physical damage, and limited recognition from specific angles, especially in large workspaces. To overcome these limitations, the proposed system integrates two synchronized marker tracking modules, each equipped with an embedded computer, a 2-axis actuator, an augmented reality (AR) marker, and a camera. These modules actively track each other’s markers, enabling reliable and robust 6D pose estimation. By aggregating tracking data from both modules and applying a Kalman filter, the system achieves high accuracy in estimating relative positions and orientations. Experimental results demonstrate that the dual active marker system not only enhances pose estimation precision but also provides scalability and reliability for applications in robotics, augmented reality, and virtual reality.

  • Special Issue: ICCAS 2024February 1, 2025

    Optimized Area Partitioning for Cooperative Underwater Search Using Multiple Autonomous Underwater Vehicles

    Kyungseo Kim, Junwoo Park, and Jinwhan Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 392-404

    https://doi.org/10.1007/s12555-024-0447-6

    Abstract

    Abstract : This study introduces an area partitioning methodology aimed at effective exploration of underwater search areas using cooperative multi-robot systems, specifically autonomous underwater vehicles (AUVs). In underwater search operations, target features are primarily detected using side-scan sonar (SSS), which provides acoustic images of the seabed using sound waves. When conducting exploration using SSS, maintaining a straight-line trajectory is crucial to acquire clear seabed images. Consequently, the direction and speed of ocean currents are critical factors in determining the directions of area partitioning and search patterns. Furthermore, the quality of acoustic images generated by sonar sensors varies depending on the seabed’s reflective properties, affecting sensing performance under different environmental conditions. To address these operational characteristics and the sensing capabilities of AUVs, we introduce the concept of time-adjusted workload (TAW) as the distribution of tasks among robots based on the estimated time required for each robot to complete its designated task. This approach emphasizes the importance of evenly distributing TAW among AUVs to minimize the overall mission completion time. Focusing on cooperative strategies and operational efficiency in marine environments, this research aims to enhance the effectiveness of underwater search missions through the optimized use of multi-AUV systems. The feasibility of the proposed methodology is demonstrated through numerical simulations of underwater exploration scenarios.

  • Special Issue: ICCAS 2024February 1, 2025

    Improving Pose Graph Optimization via Efficient Graduated Non-convexity Scheduling

    Wonseok Kang, Jaehyun Kim, Jiseong Chung, Seungwon Choi, and Tae-wan Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 405-417

    https://doi.org/10.1007/s12555-024-0479-y

    Abstract

    Abstract : In this study, we propose a novel approach to graduated non-convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backends. Traditional GNC methods rely on heuristic methods for GNC schedule, updating control parameter µ for escalating the nonconvexity. However, our approach leverages the properties of convex functions and convex optimization to identify the boundary points beyond which convexity is not guaranteed, thereby eliminating redundant optimization steps in existing methodologies and enhancing both speed and robustness. We demonstrate that our method outperforms the state-of-the-art method in terms of speed and accuracy when used for robust back-end pose graph optimization via GNC. Our work builds upon and enhances the open-source riSAM framework. Our implementation can be accessed from: https://github.com/SNU-DLLAB/EGNC-PGO.

  • Special Issue: ICCAS 2024February 1, 2025

    Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in Visual Localization

    Nanda Febri Istighfarin and HyungGi Jo*

    International Journal of Control, Automation, and Systems 2025; 23(2): 418-428

    https://doi.org/10.1007/s12555-024-0487-y

    Abstract

    Abstract : Visual localization determines an agent’s precise position and orientation within an environment using visual data. It has become a critical task in the field of robotics, particularly in applications such as autonomous navigation. This is due to the ability to determine an agent’s pose using cost-effective sensors such as RGB cameras. Recent methods in visual localization employ scene coordinate regression to determine the agent’s pose. However, these methods face challenges as they attempt to regress 2D-3D correspondences across the entire image region, despite not all regions providing useful information. To address this issue, we introduce an attention network that selectively targets informative regions of the image. Using this network, we identify the highest-scoring features to improve the feature selection process and combine the result with edge detection. This integration ensures that the features chosen for the training buffer are located within robust regions, thereby improving 2D-3D correspondence and overall localization performance. Our approach was tested on the outdoor benchmark dataset, demonstrating superior results compared to previous methods.

  • Special Issue: ICCAS 2024February 1, 2025

    Discrete-time (Q, S, R)-α-dissipative Active Braking Controller Synthesis Considering Articulation Angle of Bus-trailer Systems

    Rae Cheong Kang, Woo Jin Ahn, Yong Jun Lee*, and Myo Taeg Lim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 429-440

    https://doi.org/10.1007/s12555-024-0524-x

    Abstract

    Abstract : Bus-trailer systems equipped with fuel cells have emerged as an effective solution to overcome the short driving range of electric buses and the lack of charging infrastructure. However, ensuring the safety of the fuel cell battery in the rear trailer remains a vital challenge. This paper presents a dissipative braking controller synthesis to improve the yaw stability of bus-trailer systems based on the desired articulation angle. Achieving this stability requires that the center of gravity of the rear trailer follow the trajectory of the hinge point, which sets the desired articulation angle. The proposed controller aims to minimize the error between the desired and actual articulation angles. The (Q,S,R)-α-dissipativity provides the control design flexibility by proper adjusting weighting matrices, which covers the passivity, mixed H∞/passivity, and H∞ performance (vertical loads adding). Based on the Lyapunov theory, the desired controller gain is formulated in terms of linear matrix inequalities. The effectiveness of the proposed controller is validated using dSPACE/Simulink co-simulations.

  • Special Issue: ICCAS 2024February 1, 2025

    TAG-Net: Triple Attention Guided Network for Inspecting Surface Defects on Steel Products

    Seyoung Jeong, Jimin Song, and Sang Jun Lee*

    International Journal of Control, Automation, and Systems 2025; 23(2): 441-448

    https://doi.org/10.1007/s12555-024-0550-8

    Abstract

    Abstract : Recent advances in deep learning models for object detection and segmentation have received a lot of attention in various industry applications. However, applying deep learning methods on real world problems has In steel manufacturing industry, unexpected factors cause critical effects on the quality of steel products, and it is required to inspect defects in an early stage to reduce production costs. This paper proposes TAG-Net, a novel attention-based semantic segmentation network aimed at improving the performance for inspecting surface defects on steel products. TAG-Net estimates three attention maps each for background, defects, and boundaries of defects, and we introduce an auxiliary deep supervision to guide the boundaries of defective regions. Experiments were conducted on the NEU-Seg dataset, and experimental results demonstrate that our proposed method significantly outperforms previous methods with a significant margin.

  • Special Issue: ICCAS 2024February 1, 2025

    TARG: Tree of Action-reward Generation With Large Language Model for Cabinet Opening Using Manipulator

    Sung-Gil Park, Han-Byeol Kim, Yong-Jun Lee, Woo-Jin Ahn*, and Myo Taeg Lim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 449-458

    https://doi.org/10.1007/s12555-024-0528-6

    Abstract

    Abstract : In robotics, reinforcement learning (RL) is often used to help robots learn complex tasks through interactions with their environment. A crucial aspect of RL is the design of reward functions; these functions guide the learning process by providing feedback on a robot’s actions. However, crafting these reward functions manually is time-consuming and requires extensive human expertise. In this paper, we propose a tree of action-reward generation (TARG) model that automates reward generation for a given task without the need for human fine-tuning. By using a large language model (LLM), we create a systematic action plan sequence to generate a tree of action that guides RL training. Proposed method facilitates the automatic generation of a reward tree, which stabilizes the training process. To demonstrate the effectiveness of the proposed TARG framework, we conducted experiments involving a cabinet opening task within the IsaacSim simulation environment. The results demonstrated the potential of the proposed framework to significantly improve the adaptability and performance of robots in complex settings.

  • Special Issue: ICCAS 2024February 1, 2025

    Neural Network-based Head Movement Prediction Using Electromyography Signals

    Yundong Kim, Jirou Feng, Taeyeon Kim, Gibeom Park, Kyungmin Lee, and Seulki Kyeong*

    International Journal of Control, Automation, and Systems 2025; 23(2): 459-466

    https://doi.org/10.1007/s12555-024-0555-3

    Abstract

    Abstract : This study aims to enhance assistive technologies by predicting head movement intentions in real-time using surface electromyography (sEMG) signals and machine learning algorithms. The primary motivation is to improve the responsiveness and accuracy of gaze tracking systems for individuals with physical disabilities. Six healthy adult males participated in the experiments, with their head and neck muscle activities recorded using a high-speed optoelectronic motion capture system (Vicon Vero/Nexus) and wireless sEMG sensors (Delsys Trigno). Reflective markers were positioned on the subjects’ heads and shoulders, and mini-size sEMG sensors were placed around the eyes and neck muscles. The experimental procedure involved the participants sitting 1.5 meters from a visual guide, performing head movements in four directions, and holding each position for three seconds. Four EMG feature sets were created for analysis, combining signals from different muscles and time intervals. Various machine learning models, including kernel naïve bayes, gaussian naïve bayes, bagged trees, and subspace KNN, were applied to predict head movement states. The subspace k-nearest neighbors (KNN) model applied to EMG set 3 achieved the highest classification accuracy of 78.0%. The study demonstrates the potential for sEMG combined with advanced computational techniques to significantly improve the real-time prediction of head movement intentions, offering valuable applications in human-computer interaction, virtual reality, and assistive technologies.

  • Special Issue: ICCAS 2024February 1, 2025

    Design and Control of a 5-DOF Manipulator for Medium Energy Ion Scattering Measuring a 300 mm Wafer

    Junyoung Lee, Byeonggi Yu, Kyu-Sang Yu, Wansup Kim, and Murim Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 467-478

    https://doi.org/10.1007/s12555-024-0588-7

    Abstract

    Abstract : We proposed a pioneering mechanical design and control system for a 5-DOF manipulator dedicated to use with the medium energy ion scattering (MEIS) device, marking the world’s first system capable of measuring the film thickness and composition profile of a 300 mm wafer. While the MEIS device emits the ion beam, the 300 mm wafer can be positioned and rotated at a predefined location, aligning with the path of the ion beam through the proposed manipulator. The proposed 5-DOF manipulator consists of P-R-R-R-R joints, with a kinematic structure advantageous for horizontal orientation tasks involving roll and yaw motions. Forward kinematics, inverse kinematics, and capability map in task space are derived for the proposed manipulator. The manipulator encounters time-varying nonlinearities due to operating in a vacuum condition with high temperatures. To address this challenge, we implemented a time-delay estimation technique in the task space for the manipulator. We have successfully tested the manipulator in a demonstration of handling the 300 mm wafer.

  • Special Issue: ICCAS 2024February 1, 2025

    An Internal Model Disturbance Observer Based Robust Trajectory Tracking Control for Articulated Manipulators

    Wonseok Ha, Jae-Han Park, and Juhoon Back*

    International Journal of Control, Automation, and Systems 2025; 23(2): 479-488

    https://doi.org/10.1007/s12555-024-0624-7

    Abstract

    Abstract : This paper deals with a robust trajectory tracking controller for articulated manipulators that are subject to model uncertainties and external disturbances. The proposed controller employs the disturbance observer based controller which can effectively estimate and compensate for the effect of model uncertainties and the disturbances. It is assumed that the model uncertainty is bounded with known bounds, and that the disturbance is composed of two parts; the one, called modeled disturbance, is a summation of sinusoids with known frequencies, and the other, called unmodeled disturbance, is unknown time-varying with known bounds. To deal with the modeled disturbance, we embed its internal model into the proposed controller so that the controller can reject this modeled disturbance without using the magnitude or phase. The unmodeled disturbance is approximately rejected by tuning the controller parameter using the bounds. The stability of the closed-loop system is rigorously analyzed and it turns out that all the signals are bounded and the tracking error can be made arbitrarily small by choosing the controller parameters appropriately. Simulation results on a 2-DOF manipulator are included to validate the proposed controller.

  • Special Issue: ICCAS 2024February 1, 2025

    Edge AI-driven Multi-camera System for Adaptive Robot Speed Control in Safety-critical Environments

    Nak-Won Choi, Yeong-Bin Kim, and Bum Yong Park*

    International Journal of Control, Automation, and Systems 2025; 23(2): 489-497

    https://doi.org/10.1007/s12555-024-0549-1

    Abstract

    Abstract : This paper proposes a safety system for speed control of collaborative robots using multiple cameras and edge AI. Collaborative robots share the workspace with human workers, which requires a high level of safety for the workers. ISO/TS 15066 is a safety regulation for collaborative robots, and the proposed system establishes an additional safety system beyond the built-in safety regulations of collaborative robots. Using the edge AI-based YOLOv5n-seg, the system detects workers and estimates the distance through the instance segmentation coordinates used in object detection. The distance accuracy through the RGB-D camera shows an error rate of 3.55% at 3 meters. The adjusted base joint’s speed is 0.09 rad/s in normal mode. This is about 1/6 of the original maximum speed of 0.6 rad/s. When applying the speed reduction rate of the safety system, a robot operating at maximum speed in normal mode complies with the speed and distance regulations of ISO/TS 15066. Additionally, the robot’s response time from object detection is 0.806 seconds, confirming that worker safety can be effectively ensured.

  • Special Issue: ICCAS 2024February 1, 2025

    Optimization of Dual-type Multi-drone Formations for Load-carrying Missions

    Ardian Rizaldi and Yoonsoo Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 498-509

    https://doi.org/10.1007/s12555-024-0544-6

    Abstract

    Abstract : The use of drones in logistics transportation has significantly increased recently. One application involves transporting loads from one location to a different location. For larger loads, multiple drones are employed to carry the load collectively. This study introduces a single approach by relaxing the problem formulation and dual stage heuristic approach for dual-type multi-drone systems. Comparative analysis with a simulated annealing (SA) algorithm demonstrates that our single stage (SS) and dual stage (DS) approaches yield satisfactory results with minimal deviation from SA, highlighting SS’s superior computational efficiency.

  • Special Issue: ICCAS 2024February 1, 2025

    Deep Neural Network-based Approximation of Nonlinear Model Predictive Control: Applications to Truck-trailer Control System

    Suyong Park, Duc Giap Nguyen, Yongsik Jin, Jinrak Park, Dohee Kim, Jeong Soo Eo, and Kyoungseok Han*

    International Journal of Control, Automation, and Systems 2025; 23(2): 510-519

    https://doi.org/10.1007/s12555-024-0475-2

    Abstract

    Abstract : In this work, we demonstrate the efficiency of approximating nonlinear model predictive control (NMPC) using deep neural networks (DNN). We design an implicit NMPC for forward and backward motions of the truck trailer (TT) to handle complexity of nonlinear system dynamics. However, the high computational load of implicit MPC poses challenges for real-time implementation. To address this issue, we employ a DNN-based NMPC approximation to estimate parametric functions. As a result, the DNN-based NMPC approximation can mimic the optimal control policy of implicit MPC. Additionally, the average computation times for implicit NMPC and the DNN-based NMPC approximation in hardware-in-the-loop (HIL) tests are 36.541 ms and 0.031 ms, respectively.

  • Special Issue: ICCAS 2024February 1, 2025

    Robust Fault-tolerant Tracking Control for Linear Discrete-time Systems via Reinforcement Learning Method

    Ngoc Hoai An Nguyen and Sung Hyun Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 520-529

    https://doi.org/10.1007/s12555-024-0553-5

    Abstract

    Abstract : Concentrated on the off-policy reinforcement learning method, this paper explores a model-free algorithm for addressing the robust fault-tolerant tracking problem in discrete-time linear systems with time-varying actuator faults and model uncertainties. Specifically, to determine the feedback control input, a dynamic optimization approach is developed based on measured data rather than exact information from system dynamics. Subsequently, a static optimization approach is established using solutions from the preceding dynamic optimization problem to compute the feedforward control input. Finally, numerical simulations are conducted to illustrate the feasibility and efficiency of the proposed solution.

  • Special Issue: ICCAS 2024February 1, 2025

    LUOR: A Framework for Language Understanding in Object Retrieval and Grasping

    Dongmin Yoon, Seonghun Cha, and Yoonseon Oh*

    International Journal of Control, Automation, and Systems 2025; 23(2): 530-540

    https://doi.org/10.1007/s12555-024-0527-7

    Abstract

    Abstract : In human-centered environments, assistive robots are required to understand verbal commands to retrieve and grasp objects within complex scenes. Previous research on natural language object retrieval tasks has mainly focused on commands explicitly mentioning an object’s name. However, in real-world environments, responding to implicit commands based on an object’s function is also essential. To address this problem, we propose a new dataset consisting of 712 verb-object pairs containing 78 verbs for 244 ImageNet classes and 336 verb-object pairs covering 54 verbs for 138 ObjectNet classes. Utilizing this dataset, we propose a novel language understanding object retrieval (LUOR) module by fine-tuning the CLIP text encoder. This approach enables effective learning for the downstream task of object retrieval while preserving the object classification performance. Additionally, we integrate LUOR with a YOLOv3-based multi-task detection (MTD) module for simultaneous object and grasp pose detection. This integration enables the robot manipulator to accurately grasp objects based on verbal commands in complex environments containing multiple objects. Our results demonstrate that LUOR outperforms CLIP in both explicit and implicit retrieval tasks while preserving object classification accuracy for both the ImageNet and ObjectNet datasets. Also, the real-world applicability of the integrated system is demonstrated through experiments with the Franka Panda manipulator.

  • Special Issue: ICCAS 2024February 1, 2025

    Revolutionizing Egg Quality Control: Advanced Prompt-based Models for Automated Detection of Broken Eggs Without the Need for Training

    Tomorn Soontornnapar, Natdhanai Praneenatthavee, and Tuchsanai Ploysuwan*

    International Journal of Control, Automation, and Systems 2025; 23(2): 541-551

    https://doi.org/10.1007/s12555-024-0471-6

    Abstract

    Abstract : This paper proposes an end-to-end pipeline to detect broken eggs in a holder without extensive training, employing a two-step image segmentation and processing approach using saliency scores, all without relying on a large amount of labeled data. The process begins by inputting an egg image with text prompts into Grounding DINO, which returns an egg bounding box. This is followed by the segment anything model (SAM), which extracts the egg’s segmented region. The segmented region is then divided into two crucial components for detection: a binary mask image and a background-removed egg image. The innovation in our method lies in using the saliency score of the estimated anomaly region by employing image processing techniques to effectively distinguish between intact and broken eggs. To validate our approach, we compare it to well-known models such as SVM, XGBoost, and YOLOv8, and we also conduct zero-shot experiments with CLIPSeg, Florence-2, and SAA. In our experimental setup, we utilize 50 egg holder images, each containing both intact and broken eggs. We carefully cropped and processed 30 eggs (arranged in a 6x5 grid) from each holder, resulting in a comprehensive testing dataset totaling 1,500 images. Our results demonstrate the robustness of our method, achieving an impressive 99.56% accuracy in detecting both intact and broken eggs. This breakthrough promises significant advancements in the field of broken egg detection, with broad applications across diverse industries, including food safety, quality control, and automated packaging systems.

  • Special Issue: ICCAS 2024February 1, 2025

    Relaxed Local Stabilization for Discrete-time Fuzzy Systems via Quadratically Structural Approach

    KyungSoo Kim, Yelim Kim, and PooGyeon Park*

    International Journal of Control, Automation, and Systems 2025; 23(2): 552-559

    https://doi.org/10.1007/s12555-024-0467-2

    Abstract

    Abstract : This paper aims to study the local stabilization problem of discrete-time fuzzy systems via a quadratically structural approach. The proposed methods tackle the conventional analysis deriving stabilization conditions in multiple summations by relaxed stabilization conditions in a quadratic structure. Moreover, an attempt to utilize the inherent properties of the quadratic structure is first introduced to construct a feasible problem in the form of linear matrix inequalities. These endeavors allow optimization problems to maximize a region of attraction to be achieved with relaxation. Finally, the effectiveness of the proposed methods is discussed by illustrative examples.

  • Special Issue: ICCAS 2024February 1, 2025

    Enhanced Fuzzy Logic Control for Active Suspension Systems via Hybrid Water Wave and Particle Swarm Optimization

    Hooi Hung Tang and Nur Syazreen Ahmad*

    International Journal of Control, Automation, and Systems 2025; 23(2): 560-571

    https://doi.org/10.1007/s12555-024-0513-0

    Abstract

    Abstract : Fuzzy logic controller (FLC) is renowned for its adaptability and intuitive decision-making capabilities in active suspension systems, which face challenges stemming from unpredictable disturbances and complex vehicle dynamics. In this study, we introduce a novel optimization approach termed WW-PSO, which merges particle swarm optimization (PSO) with water wave optimization (WWO), aiming to elevate the performance of an FLC-based active suspension system. WWO efficiently solves optimization problems by simulating natural water wave behaviors. The hybridization of PSO and WWO leverages their complementary exploration and exploitation capabilities, resulting in improved performance and robustness of the optimized controller. The performance of the proposed controller, which is augmented with a linear quadratic controller (LQR), is evaluated across three scenarios featuring different road profiles and compared against other recent optimization methods which include genetic algorithm, tent sparrow search algorithm (Tent-SSA), and ST-PS-SO which is a combination of PSO, sewing traineebased optimization, and symbiotic organism search. Simulation results show that the proposed WW-PSO significantly improves integral time absolute error (ITAE) for both body and wheel displacements, overshoot/undershoot (OS/US), and settling time. Specifically, the proposed method achieves a 53.37% improvement in ITAE, a 56.44% reduction in OS/US, and a 13.09% decrease in settling time for body displacements. For wheel displacements, it achieves a 52.90% improvement in ITAE, a 48.72% reduction in OS/US, and a 14.15% decrease in settling time. These enhancements demonstrate the hybrid method’s effectiveness in improving vehicle stability and passenger comfort across a range of road conditions.

  • Special Issue: ICCAS 2024February 1, 2025

    Stabilization Criterion for Continuous-time T-S Fuzzy Delayed Systems Subject to Asynchronous Fuzzy Phenomenon via Non-PDC Scheme

    Ngoc Hoai An Nguyen and Sung Hyun Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 572-580

    https://doi.org/10.1007/s12555-024-0552-6

    Abstract

    Abstract : Focusing on a non-parallel distributed compensation (non-PDC) control scheme, this paper aims to derive a stabilization criterion for continuous-time Takagi-Sugeno (T-S) fuzzy delayed systems in the presence of the asynchronous fuzzy phenomenon. To achieve this goal, the paper presents a scheme for formulating fuzzy-dependent stabilization conditions that can handle delayed states, aiming to obtain less conservative performance and reduce computational complexity. Specifically, a control synthesis method is derived that is capable of designing both the free-weighting matrix and the congruent transformation matrix, relying on the asynchronous fuzzy basis function. Furthermore, the asynchronous fuzzy phenomenon is addressed by transforming the asynchronous fuzzy basis functions into the errors between the original and asynchronous fuzzy basis functions, and incorporating these errors into the fuzzy-dependent stabilization criterion.

  • Special Issue: ICCAS 2024February 1, 2025

    A Sparse Quadratic Programming Approach and Interior Point Method to Design Supervisory Model Predictive Control of Multi-zone HVAC Systems

    David Banjerdpongchai* and Pasitnat Sasananand

    International Journal of Control, Automation, and Systems 2025; 23(2): 581-591

    https://doi.org/10.1007/s12555-024-0463-6

    Abstract

    Abstract : Power consumption has been increasing significantly due to the earth’s rising global temperatures, leading to higher energy usage in HVAC systems. Choosing the conventional setpoint temperature could reduce unnecessary power consumption, thus leading to cost savings. This paper extends the design of supervisory model predictive control (SMPC) for HVAC systems with multiple zones. The design objective aims to shave the peak demand and maintain occupants’ thermal comfort. Two methods of SMPC are developed, namely, centralized SMPC and decentralized SMPC. Previously, SMPC was developed using the standard quadratic programming (QP) solver and the active set method. In this paper, we apply the sparse QP solver using the interior point method. The results indicate that centralized supervisory control (SC) yields better outcomes, as demonstrated by a trade-off curve between total operating costs and thermal comfort. Moreover, centralized model predictive control (MPC) successfully achieved satisfactory results in both tracking the reference signal and optimizing power consumption. Utilizing the sparse QP solver can yield faster computation compared to the standard QP solver, making it more suitable for the design of SMPC.

  • Special Issue: ICCAS 2024February 1, 2025

    Novel Active Noise Control Algorithm Based on Combined Step-size Strategy Against Impulsive Noise

    Seung Hyun Ryu, Jeongmin Park, and PooGyeon Park*

    International Journal of Control, Automation, and Systems 2025; 23(2): 592-599

    https://doi.org/10.1007/s12555-024-0507-y

    Abstract

    Abstract : The study proposes the combined step-size affine projection Champernowne adaptive filter (CSSAPCMAF), addressing the back-and-forth relationship between convergence rate and steady-state misalignment in fixed step-size adaptive filtering algorithms. Additionally, the study introduces a new approach to active noise control using the combined step-size filtered-x affine projection Champernowne adaptive filter (CSS-FxAPCMAF). The emphasis of this research lies in robustness in environments with correlated input and impulsive noise. Simulation results demonstrate that the proposed algorithm can maintain a convergence rate while exhibiting smaller steady-state misalignment compared to existing algorithms, ensuring superior performance.

  • Special Issue: ICCAS 2024February 1, 2025

    Static Output Feedback H∞ Control for Discrete-time Singular Systems With Structure Constraints

    Dongyeop Kang and Chan-eun Park*

    International Journal of Control, Automation, and Systems 2025; 23(2): 600-610

    https://doi.org/10.1007/s12555-024-0466-3

    Abstract

    Abstract : This paper deals with the design of static output feedback (SOF) H∞ controller for discrete-time singular systems. The design procedure of the SOF H∞ controller without structure constraints is first proposed and then extended to the case with structure constraints where certain elements of the control gain matrix are zero. First, the extended state feedback (ESF) controller is defined and the condition for obtaining the ESF H∞ control gain is presented. Then, the initial SOF H∞ control gain is computed by utilizing the derived ESF control gain, and the H∞ performance of the obtained result is improved through iterative optimization to obtain the final controller. The iterative optimization process results in the improved SOF gain matrix for the controller, which can produce less conservative results compared to existing methods. Based on the proposed design procedure, a solution to the design problem with controller structure constraints is also provided. Numerical examples of SOF H∞ controllers both with and without structure constraints are given to demonstrate the effectiveness of the proposed method.

  • Special Issue: ICCAS 2024February 1, 2025

    Constant Reference Tracking in Data-driven Control

    Abdul Aris Umar and Jung-Su Kim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 611-619

    https://doi.org/10.1007/s12555-024-0516-x

    Abstract

    Abstract : Although the data-driven control design techniques have gotten attention recently, few results address the tracking problem using data-based control formulations. Considering this, this paper presents a data-driven control method to drive the output of a system to its desired constant reference without using any model knowledge. To this end, first, this paper shows how to take the integral action into account for the data-driven control design for the reference tracking problem. Second, a closed-loop representation of the data-driven tracking controller with integral action is derived. Then, based on the representation, linear matrix inequality (LMI) conditions are derived for gain selection. The effectiveness of the proposed method is demonstrated using the speed control of a DC motor. Thanks to the integral action property, it is shown that the tracking error remains zero under constant disturbance affecting the system.

  • Special Issue: ICCAS 2024February 1, 2025

    Sparse Identification and Nonlinear Model Predictive Control for Diesel Engine Air Path System

    Shuichi Yahagi*, Hiroki Seto, Ansei Yonezawa, and Itsuro Kajiwara

    International Journal of Control, Automation, and Systems 2025; 23(2): 620-629

    https://doi.org/10.1007/s12555-024-0452-9

    Abstract

    Abstract : This paper presents a sparse identification of nonlinear dynamic systems (SINDy) for a diesel engine air path system and nonlinear model predictive control (NMPC) with the SINDy model to attain good control performance. The air path system control is well known as a challenging problem, and many studies have been presented such as traditional model-based control design and machine learning. However, these conventional approaches still have some difficulties including the control performance and design costs. In this paper, we obtain the model of the air path system in a data-driven manner using the SINDy algorithm and construct the offset-free NMPC with the SINDy model. SINDy is a suitable modeling method for controlling a complicated air path system, owing to its characteristics of high computational efficiency, high learning efficiency, high modeling accuracy, and applicability to complex systems. Additionally, NMPC provides high control performance under constraints. The proposed offset-free NMPC with the SINDy model is verified through the simulations. The results show that the coefficient of determination of the SINDy model provided over 90%, and the controller performance of the NMPC was better than that of the traditional robust controller and satisfied the constraints.

  • Special Issue: ICCAS 2024February 1, 2025

    Stability Analysis of Delayed Neural Networks via Modified Free-matrix Based Integral Inequality

    Yongbeom Park, Ho Sub Lee, and PooGyeon Park*

    International Journal of Control, Automation, and Systems 2025; 23(2): 630-637

    https://doi.org/10.1007/s12555-024-0523-y

    Abstract

    Abstract : This paper introduces the modified free-matrix-based integral inequality (MFBII) and investigates its application in the stability analysis of delayed neural networks through the Lyapunov-Krasovskii functional (LKF) approach. In order to provide a less conservative stability criterion, the MFBII is employed with an augmented vector that contains the derivative of the system state and the nonlinear function output. A corresponding double integral of the quadratic terms related to the augmented vector is newly constructed to utilize cross-information between components in the augmented vector. Two numerical examples demonstrate the effectiveness of the proposed method.

  • Special Issue: ICCAS 2024February 1, 2025

    A Generalized Primal-dual Correction Method for Saddle-point Problems With a Nonlinear Coupling Operator

    Sai Wang and Yi Gong*

    International Journal of Control, Automation, and Systems 2025; 23(2): 638-645

    https://doi.org/10.1007/s12555-024-0453-8

    Abstract

    Abstract : The saddle-point problems (SPPs) with nonlinear coupling operators frequently arise in various control systems, such as dynamic programming optimization, H-infinity control, and Lyapunov stability analysis. However, traditional primal-dual methods are constrained by fixed regularization factors. In this paper, a novel generalized primal-dual correction method (GPD-CM) is proposed to adjust the values of regularization factors dynamically. It turns out that this method can achieve the minimum theoretical lower bound of regularization factors, allowing for larger step sizes under the convergence condition being satisfied. The convergence of the GPD-CM is directly achieved through a unified variational framework. Theoretical analysis shows that the proposed method can achieve an ergodic convergence rate of O(1/t). Numerical results support our theoretical analysis for an SPP with an exponential coupling operator.

  • Special Issue: ICCAS 2024February 1, 2025

    A Novel Lyapunov Functional for Sampled-data Synchronization of Chaotic Neural Networks With Actuator Saturation

    Hyeon-Woo Na, Seongrok Moon, and PooGyeon Park*

    International Journal of Control, Automation, and Systems 2025; 23(2): 646-654

    https://doi.org/10.1007/s12555-024-0526-8

    Abstract

    Abstract : This paper addresses the synchronization issue of chaotic neural networks under actuator saturation by designing a sampled-data controller. First, we propose a novel Lyapunov-Krasovskii functional consisting of looped-functionals with single and double integral terms for the error state and its derivative. Zero equations are employed so as to relax the positiveness conditions of the free matrices involved in the integral terms. Second, less conservative criteria is derived for the synchronization of chaotic neural networks with actuator saturation. Finally, we design a sampled-data controller for effective synchronization of the drive and response systems by solving linear matrix inequalities. The superiority of the proposed method are verified through a representative numerical example, showcasing its advantages over previous methods.

  • Special Issue: ICCAS 2024February 1, 2025

    Offline Robust Model Predictive Control Using Linear Matrix Inequality-based Optimization

    Nguyen Ngoc Nam, Tam W. Nguyen, and Kyoungseok Han*

    International Journal of Control, Automation, and Systems 2025; 23(2): 655-663

    https://doi.org/10.1007/s12555-024-0444-9

    Abstract

    Abstract : This paper proposes a new approach to handle offline robust model predictive control (RMPC) using linear matrix inequality-based (LMI-based) optimization. To address system parameter uncertainties, we consider uncertain parameters within a polytope. A set of LMIs is then utilized to determine an optimal controller gain based on the polytope. The main contribution of this paper is establishing the upper bound of the cost function as a quadratic function of the state variable. It opens the opportunity to obtain the optimal controller gain in an offline environment, significantly reducing the computation burden. With this approach, robust stability of a closed-loop system can be achieved with a broad range of model uncertainties. Furthermore, the input and output constraints are enforced to ensure the system’s operation in a specific range. To validate the efficacy of the proposed approach, our simulation results are provided and compared with the existing method.

  • Special Issue: ICCAS 2024February 1, 2025

    Initialization-free Distributed Network Size Estimation via Implicit-explicit Discretization Method

    Donggil Lee and Yoonseob Lim*

    International Journal of Control, Automation, and Systems 2025; 23(2): 664-673

    https://doi.org/10.1007/s12555-024-0535-7

    Abstract

    Abstract : This paper proposes a distributed algorithm for estimating the network size, which refers to the total number of agents in a network. Our approach is based on an optimization problem, where the solution corresponds to the network size and the objective function can be decomposed into individual agents’ objectives. This enables the use of distributed methods such as the primal-dual gradient method. We focus on a continuous-time primal-dual gradient method and adapt it using an implicit-explicit scheme to run in discrete time. This approach eliminates the need for small step sizes and ensures rapid convergence. Unlike existing methods that require specific initial values, our method can provide the network size regardless of the initial values, making it robust to network changes.

  • Special Issue: ICCAS 2024February 1, 2025

    Position-sensorless Control of Switched Reluctance Motors With Converter Faults Using Adaptive Sliding Mode Observers

    Huibeom Youn, Gyuwon Kim, and Jaepil Ban*

    International Journal of Control, Automation, and Systems 2025; 23(2): 674-682

    https://doi.org/10.1007/s12555-024-0547-3

    Abstract

    Abstract : Switched reluctance motors (SRMs) have gained widespread attention across various industries due to their inherent advantages, including simple construction, high efficiency, and the absence of permanent magnets. However, a critical aspect of SRM operation is the need for position sensors. Consequently, extensive research has been conducted on position-sensorless control techniques for SRMs. However, unexpected faults are critical for achieving position-sensorless control performance. This paper proposes a fault-tolerant position-sensorless control method for SRMs based on an adaptive sliding mode observer (ASMO) that specifically addresses the issue of converter faults. The proposed method estimates the faulty phase voltage in real-time, enabling the design of a dynamic model of the SRM that accurately reflects the fault conditions. The paper presents a theoretical analysis of the convergence condition of the estimation error. As a result, the proposed method can accurately estimate the faulty phase voltage and the rotor position using only phase current measurements, without the need for a dedicated position sensor. Simulation results are provided to demonstrate the performance of the proposed control method and its superiority compared to the existing sliding mode observer-based position-sensorless control method.

IJCAS
February 2025

Vol. 23, No. 2, pp. 359~682

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