Manned water-powered aerial vehicles have been implemented into specialized missions around water bodies, such as firefighting and rescue. However, the dual requirement of vehicle motion control and performing tasks challenges operators. Moreover, in the presence of a low visibility, dense smoke, and extreme temperature, they always face potential risks. Motivated by these difficulties, this paper proposed an unmanned water-powered aerial vehicle using a nozzle rotation mechanism. This mechanism allows the vehicle to have a wide range of forces and torques in multiple directions under constant mass flowrate condition. A simple controller was designed to investigate the fundamental flight motions and verify dynamic properties of the vehicle in practical testing. To come up with the control law, the following steps were taken. Firstly, a mathematical model was derived to reflect the vehicle’s dynamic characteristics. Secondly, a well-known proportional-derivative-integral controller incorporating gravity compensation was deployed to regulate the 3-degree-of-freedom motion system. Thirdly, experiments were conducted to confirm the flight ability of the proposed vehicle. Results demonstrated that the control system preserved stability and the vehicle could fly following the desired altitude.
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Intelligent Robust Motion Control of Aerial Robot Cao-Tri Dinh, Thien-Dinh Nguyen, Young-Bok Kim, Thinh Huynh, Jung-Suk Park Actuators.2025; 14(4): 197. CrossRef
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The key components of smart manufacturing, a central concept in the era of the 4th Industrial Revolution, consist of digital twin technology, AI, and computer vision technology. In this study, these technologies were utilized to govern the Poppy robot, a humanoid robot designed for educational and research purposes. The digital twin creates a virtual environment capable of real-time simulation, analysis, and control of the robot’s motions. The digital twin of the robot was constructed using Unity, a 3D development program. Motion data was captured while simulating the physical structure and movements of the virtual robot. This data was then fed into a Tensorflow-based deep neural network to generate a regression modelthat predicts motor rotation based on the position of the robot’s hand. By integrating this model with a Python-based robot control program, the robot’s movements could be effectively managed. Additionally, the robot was controlled using Openpose, a computer vision algorithm that predicts characteristic points on a human body. Position data for human joint points was collected from 2D images, and the motor angle was calculated based on this data. By implementing this approach on an actual robot, it became possible to enable the robot to replicate human movements.
The fourth industrial revolution led to advanced servo systems, enhancing productivity across industries. However, designing these systems remains challenging due to the performance-stability trade-off. This paper presents a model-based motion control of a linear motor motion stage in frequency domain. A user-code for the PowerPMAC commercial controller was developed to excite motion control system so that we could get a frequency response. The theoretical frequency response of the servo algorithm was compared with the experimental frequency response. Based on this, a tuning graphical user interface (GUI) was developed to predict performance when the servo loop gain is changed. Especially, to compensate for residual vibrations caused by high acceleration and deceleration and to improve tracking error, DOB (Disturbance Observer) and ILC (Iterative Learning Control) control techniques were applied in the frequency domain. Through the design of the frequency domain motion controller, the control performance of the linear motor motion stage could be predicted with over 96% accuracy, resulting in a 54.32% improvement in tracking error and a 93.56% improvement in settling time, 85.29% in RMS error.
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Fuzzy Neural Network Control for a Reaction Force Compensation Linear Motor Motion Stage Kyung Ho Yang, Hyeong-Joon Ahn International Journal of Precision Engineering and Manufacturing-Smart Technology.2024; 2(2): 109. CrossRef
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Recently, industrial manufacturing has developed into additive manufacturing, benefiting from multi-item small-sized production and effective manufacturing. Importantly, Wire Arc Additive Manufacturing, which uses metal wires, is attracting worldwide attention for its high-quality metal product technology. Technological innovation that combines virtual physics with reality through big data communication, such as process variables along with Wire Arc Additive Manufacturing, is an essential task for implementing smart manufacturing technology. Due to the characteristic of Wire Arc Additive Manufacturing, numerous variable conditions exist, making it difficult to standardize robot"s process path data generation algorithms and data application methods, and this data generation method is being studied as a core element technology. The present study generated foundation process implementation, simulation, and generated path data for robots in virtual space using RoboDK, which provides robot libraries from multiple manufacturers, and Python, which is a universal programming language. To implement the experimental data in practice, ABB"s industrial six-axis robots IRB-6700 and Fronius TPS500i were used to control the arcing plasma heat source, and the process path worked the same as simulation. Based on the underlying experimental results, this process can be applied to generation of additive manufacturing in the Wire Arc Additive Manufacturing process for 3D models.
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Artificial Intelligence Technologies and Applications in Additive Manufacturing Selim Ahamed Shah, In Hwan Lee, Hochan Kim International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2463. CrossRef
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In Korea, water spraying to suppress the dust during building dismantling operations has been done manually by human laborers, considered extremely dangerous since it often causes fatal accidents. Abroad, however, water spraying machines have been developed and used in construction sites instead of workers to prevent such serious industrial accidents. In this study, the first domestic water spraying machine is suggested. Since the spraying machine should have a novel dust tracking function, an optimal structure and mechanism should be designed to guarantee its motion performance. The motion for target tracking is achieved by the 2 DOF (Degrees of Freedom) structure comprising a linear and a rotary actuator. Then, the geometric analysis was performed to provide a sufficient kinematic workspace. Through the dynamic performance simulation, the optimal actuator capacities could be selected to generate an appropriate acceleration. The geometric and dynamic performance was evaluated by the extensive motion experiments. With this study, it is expected that an advanced water spraying machine can be developed only with domestic technologies to protect construction laborers from potentially dangerous accidents.
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Excavator Posture Estimation and Position Tracking System Based on Kinematics and Sensor Network to Control Mist-Spraying Robot Sangwoong Lee, Hyunbin Park, Baeksuk Chu IEEE Access.2022; 10: 107949. CrossRef
UVW Stage is widely used in manufacturing processes of PCB, LCD, OLED, and semiconductor industries. The precision of UVW Stage is closely associated with the quality of products. Two approaches for kinematics of UVW Stage are proposed for comparative analysis. Program of proposed kinematics algorithm is developed for motion control and applied to UVW Stage driving. The position of the stage for each algorithm is sequentially measured by laser interferometer. Both virtual stage and real stage are used for accuracy analysis. The performance of each algorithm is evaluated based on this accuracy analysis.
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A correction algorithm for determining the motor pivot point coordinates of UVW platforms based on a kinematic motion model Yunchao Zhi, Qunfeng Liu, Mingming Zhang, Jiarui Zhang Computational and Applied Mathematics.2025;[Epub] CrossRef
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