• 대한기계학회논문집A
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• 제41권5호
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• pp.367-374
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• 2017

본 연구의 목적은 120톤급 전지형 크레인의 조향성능 향상을 위한 제어 알고리즘의 개발이다. 이를 위해 AMESim 소프트웨어를 이용하여 전지형 크레인의 유압조향시스템을 모델링하고, PID 기반의 조향제어용 제어기를 MATLAB/Simulink 환경에서 설계하였다. 설계된 제어기의 성능은 실시간 시뮬레이터를 활용한 유압 및 제어 모델간 연성해석을 통하여 검증하였다.

• 한국자동차공학회논문집
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• 제25권2호
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• pp.227-235
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• 2017

In this paper, a systematic 5-mode(road steering, all-wheel steering, crab steering, reduced swing out mode and independent steering) steering algorithm design process for an all-terrain mobile crane with 5 axles and all steerable wheels is proposed. Steering angles for each steering mode are designed based not only on basic theory but also on vehicle specification, design limitation and requirements. A multi-body dynamic analysis is carried out to investigate the feasibility of the steering algorithm. According to the results, the proposed steering algorithm meets the objective of each steering mode.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.

• 드라이브 ㆍ 컨트롤
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• 제14권2호
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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.

• 드라이브 ㆍ 컨트롤
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• 제14권2호
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• pp.30-36
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• 2017

This paper presents a sliding mode observer-based fault detection algorithm for steering inputs of an all-terrain crane. All-terrain cranes with multi-axles have several steering modes for various working purposes. Since steering angles at the other axles except the first wheel are controlled by using the information of steering angle at the first wheel, a reliable signal of the first axle's steering angle should be secured for the driving safety of cranes. For the fault detection of steering input signal, a simplified crane model-based sliding mode observer has been used. Using a sliding mode observer with an equivalent output injection signal that represents an actual fault signal, a fault signal in steering input was reconstructed. The road steering mode of the crane's steering system was used to conduct performance evaluations of a proposed algorithm, and an arbitrary fault signal was applied to the steering angle at the first wheel. Since the road steering mode has different steering strategies according to different speed intervals, performance evaluations were conducted based on the curved path scenario with various speed conditions. The design of algorithms and performance evaluations were conducted on Matlab/Simulink environment, and evaluation results reveal that the proposed algorithm is capable of detecting and reconstructing a fault signal reasonably well.

• Journal of Advanced Marine Engineering and Technology
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• 제37권8호
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• pp.962-968
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• 2013

화석연료의 고갈에 따른 연료유의 가격상승과 세계온난화에 따른 그린하우스가스(Green House Gas, GHG)등의 문제가 주목받고 있다. 이러한 이유로 해상운송사업은 연료의 소비를 줄이고 환경보호를 위해 온실가스를 줄이기 위한 친환경선박(Green ship) 기술에 대하여 관심을 보이고 있다. 선박의 전력시스템은 안전한 운항을 위한 가장 주요한 요소 중 하나이다. 이러한 이유로 선박 설계에 있어 대부분의 선박은 바우 스러스터(Bow thruster, B/T), 크레인 등의 장비사용에 따른 피크부하로 인하여 큰 용량의 발전기를 사용하고 있다. 이러한 이유로 선박의 항해에 있어 대부분의 선박의 발전기는 저부하 운전구간에서 운전되게 된다. 50%또는 그 이하의 운전율인 저부하 운전에서는 발전기의 운전효율이 저하하게 되어 많은 연료유를 소비하며 발전기 수명에 좋지 않은 영향을 미치게 된다. 본 논문에서는 입출항이 비교적 많이 있는 컨테이너선박에 이차전지 적용방안에 대하여 연구하였다. 그 결과 이차전지를 적용하여 항해 중 발전기 운전율을 높이므로 연료 소비를 줄일 수 있음을 확인하였다.

• 드라이브 ㆍ 컨트롤
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• 제14권2호
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.