• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.367-374
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• 2017

The goal of this study was to develop control algorithms to improve the steering performance of a 120-ton all-terrain crane. To accomplish this, a hydraulic steering system for the crane was modeled using AMESim software, and a PID steering control algorithm was designed in the MATLAB/Simulink environment. The performance of the designed controller was verified through multiphysics co-simulations based on a real-time simulator.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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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.

• Journal of Drive and Control
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• v.19 no.4
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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.

• 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.

• Journal of Drive and Control
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• v.14 no.2
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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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• v.37 no.8
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• pp.962-968
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• 2013

Problem of greenhouse gases associated with global warming and the world rise in fuel oil prices due to the depletion of fossil fuel has attracted attention. For this reason, maritime transport business, has shown interest in green-ship technology to reduce the consumption of fuel and reduce greenhouse gas for environmental protection. Power system of the ship is one of the most important factors for safe operation. Therefore, at design of ship power system, most of existing vessel used comparative large capacity generator in order to respond peak load such as bow thruster, crane and etc. In the navigation of ship, marine generators most would be operated at low load operation. In the low load operation of the generation rate of 50% or less, the operation efficiency of the generator it deteriorated, to consume more fuel oil. It also, it means that adversely effect the life of the generator. In this paper, studied how to apply for a secondary battery in container ship that relatively frequent arrival and departure in port. As a result, in order to apply the secondary battery to increase the operating efficiency of the generator during the voyage, it was confirmed that it is possible to reduce fuel consumption.

• Journal of Drive and Control
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• v.14 no.2
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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.