• Journal of Forest and Environmental Science
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• v.38 no.4
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• pp.239-248
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• 2022

Forestry crane work in a forest harvester or forwarder is regarded as one of most hard work requiring a very high level of operation skill. The operator must handle two or more multi-axes joysticks simultaneously to control the multiple manipulators for maneuvering the crane-tip to its intended location. This study has been carried out to develop a crane-tip controller which can intuitively maneuver the crane-tip, resulting in improving the productivity by decreasing the technical difficulty of control as well as reducing the workload. The crane-tip controller consists of a single 2-axis joystick and a control algorithm run on microcontroller. Lab-scale forestry crane was constructed using electric cylinders. The crane-tip control algorithm has the crane-tip follow the waypoints generated on the given path considering the dead band region using LBO (Lateral Boundary Offset). A speed control gain to change the speed of relevant cylinders relatively is applied as well. By the P (Proportional) control within the control interval of 20 msec, the average error of crane-tip control on the predefined straight path turned out to be 14.5 mm in all directions. When the joystick is used the waypoints are generated in real time by the direction signal from the joystick. In this case, the average error of path control was 12.4 mm for straight up, straight forward and straight down movements successively at a certain constant speed setting. In the slant movement of crane-tip by controlling two axes of joystick simultaneously, the movement of crane-tip was controlled in the average error of 15.9 mm when the crane-tip is moved up and down while moving toward forward direction. It concluded that the crane-tip control was possible using the control algorithm developed in this study.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1241-1248
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• 2004

A controller of wheeled mobile robot(WMR) based on Lyapunov theory is designed and a Fuzzy-Neural Network algorithm is applied to this system to adjust controller gain. In conventional controller of WMR that adopts fixed controller gain, controller can not pursuit trajectory perfectly when initial condition of system is changed. Moreover, acquisition of optimal value of controller gain due to variation of initial condition is not easy because it can be get through lots of try and error process. To solve such problem, a Fuzzy-Neural Network algorithm is proposed. The Fuzzy logic adjusts gains to act up to position error and position error rate. And, the Neural Network algorithm optimizes gains according to initial position and initial direction. Computer simulation shows that the proposed Fuzzy-Neural Network controller is effective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.280-294
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• 2023

Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of communication systems and supporting high data rates. However, short wavelengths, owing to the high millimeter-wave frequencies can cause problems, such as signal attenuation and path loss. To address these limitations, research on high directional beamforming technologies continue to garner interest. Furthermore, owing to the mobility of the UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. This study presents a beam tracking method based on the unscented Kalman filter using hybrid beamforming. The simulation results reveal that the proposed beam tracking scheme improves the overall performance in terms of the mean-squared error and spectral efficiency. In addition, by expanding analog beamforming to hybrid beamforming, the proposed algorithm can be used even in multi-user and multi-stream environments to increase data capacity, thereby increasing utilization in new-radio multiple-input multiple-output orthogonal frequency-division multiplexing systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.4
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• pp.188-194
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• 2014

V2I(Vehicular to Infrastructure) is a one kind of communication systems which is used between the base stations and mobile objects. In V2I communication system, it is difficult to obtain the desired communication performance. Beamforming technology is to find the optimal path. and it can be improved the communication performance. MUSIC algorithm can be estimated the direction of arrival. The directional vector of received signals and the eigenvector has orthogonal property. MUSIC algorithm uses this property. In V2I communication environment, real time optimal path is changed. By the high computational complexity of the MUSIC algorithm, the optimal path estimation error is generated. In this paper, we propose a method of computation reduction algorithm for MUSIC algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.6
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• pp.506-514
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• 2012

This paper represents a method for designing of path following Line-of-Sight(LOS) guidance law based on vehicle kinematics. In general, a LOS guidance law which is composed of gains and approach length as design parameters is designed by empirical or trial-and-error method. These approaches cannot guarantee a precision tracking performance of guidance law consistently. Also, the design parameters should be redesigned with variations of vehicle maneuverability and flight velocity. Based on a vehicle kinematics with its velocity, the proposed method for designing of parameters not only minimizes the number of design parameters, also has a reliable and consistent tracking performance using variable guidance gain changed in accordance with flight velocity. This is validated by nonlinear simulation with $1^{st}$ order attitude response dynamics and flight experiments with given linear and circular path.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.540-543
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• 1997

In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of the path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force, and external disturbance are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the usage of vision system makes mobile robot compensate the cumulative location error which exists when relative sensor like encoder is used to locate the position of mobile robot. The effectiveness of the proposed control scheme is shown through computer simulation.

• Journal of Radiation Protection and Research
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• v.44 no.1
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• pp.43-52
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• 2019

Background: The mid-term performance of clinical linear accelerator (LINAC) during volumetric modulated arc therapy (VMAT) treatment period is not performed in clinical practice and usually replaced with one-time plan quality assurance (QA). In this research we aim to monitor daily reproducibility of VMAT delivery from tracking individual leaf movement error and dosimetric error to evaluate the mid-term quality of the machine used. Materials and Methods: First, multileaf collimator (MLC) information was imported into MATLAB program to determine which of the MLC leaves in the leaf bank had the maximum RMS position error (maxRMS). We estimated where the maximum positional errors (maxPE) of the chosen leaf occur along its path length and tracked its daily variations over the entire treatment period. Secondly, picture information of dosimetric error from portal dosimetry was imported into MATLAB where representative high gamma index region (HGR) was determined as HGR with length of > 1 cm and their centers were daily tracked. Results and Discussion: The maxPEs in the brain and tongue cases were distributed broader than in other cases, but all data were found located within ${\pm}0.5mm$. From first day to last day all of five cases show the similar visual pattern of HGRs and Centers of the longest HGRs remained within ${\pm}1mm$ of that in first day. These findings prove excellent mid-term performance of the LINAC used in VMAT treatments over a full course of treatment. Conclusion: Tracking the daily location changes of leaf movement and dosimetric error can be a good indicator of predicting the daily quality like stability and reproducibility of beam delivering in VMAT treatment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.108-116
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• 2008

In this paper, we propose an algorithm that can estimate the distance using disparity based on stereo vision system, even though the obstacle is located in long ranges as well as short ranges. We use sub-pixel interpolation to minimize quantization errors which deteriorate the distance accuracy when calculating the distance with integer disparity, and also we use enhanced adaptive fuzzy strong tracking Kalman filter(EAFSTKF) to improve the distance accuracy and track the path optimally. The proposed method can solve the divergence problem caused by nonlinear dynamics such as various vehicle movements in the conventional Kalman filter(CKF), and also enhance the distance accuracy and reliability. Our simulation results show that the performance of our method improves by about 13.5% compared to other methods in point of root mean square error rate(RMSER).

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.81-88
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• 1994

The CNS(Car Navigation System) is used more generally in driver aid system than ALV(Auto nomous Land Vehicle) research area. In this paper we developed a new position tracking algorithm for the Global Path Planning in the CNS. In japan, CNS is already well developed and, thesedays they sell CNS products about $400{\sim}500$ thousands per year, and USA and European Communications(EC), too. In Korea, studies of the first generation CNS, which finds current location of a navigating vehicle and displays its location in a Digital-Map with real-time are progressing but still in the beginning step. Therefore a new position tracking algorithm is presented, which reduces vehicle position error dramatically by fusing GPS and dead-reckoning sensors. And the validity of our algorithm is demonstrated by the experimental results with the real car.

• Journal of Drive and Control
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• v.14 no.2
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• pp.9-15
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• 2017

The steering systems of all-terrain cranes have been developed with various control strategies for the stability and drivability. To optimally control the input steering angle, an accurate mathematical model that represents the actual crane dynamics is required. The derivation of an accurate mathematical model to optimally control the steering angle, however, is difficult since the steering-control strategy generally varies with the magnitude of the crane's longitudinal velocity, and the postures of the crane's working parts vary while it is being driven. To address this problem, this paper proposes an automatic steering-control algorithm that is based on the MPC (model predictive control) with a disturbance observer for all-terrain cranes. The designed disturbance observer of this study was used to estimate the error between the base steering model and the actual crane. A model predictive controller was used for the computation of the optimal steering angle, along with the use of the base steering model with an estimated uncertainty. Performance evaluations of the designed control algorithms were conducted based on a curved-path scenario in the Matlab/Simulink environment. The performance-evaluation results show a sound reference-path-tracking performance despite the large uncertainties.