• 한국정밀공학회지
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• 제22권4호
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• pp.128-135
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• 2005

Overhead crane systems consist of trolley, girder, rope, objects, trolley motor, girder motor, and hoist motor. The dynamic system of these systems becomes a nonlinear state equations. These equations are obtained by the nonlinear equations of motion which are derived from transfer functions of driving motors and equations of motion for objects. From these state equations, Lyapunov functions of overhead crane systems are derived from integral method. These functions secure stability of autonomous overhead crane systems. Also constraint equations of driving motors of trolley, girder, and hoist are derived from these functions. From the results of computer simulation, it is founded that overhead crane systems is secure.

• 전기학회논문지
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• 제56권5호
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• 한국정밀공학회지
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• 제17권4호
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• pp.142-147
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• 2000

An overhead crane system which transports an object by girder motion, trolley motion, and hoist motion becomes a nonlinear system because the length of a rope changes. To develope the position control algorithm for the nonlinear crane systems, we apply a nonlinear optimal control method which uses forward and backward difference methods and obtain optimal inputs. This method is suitable for the overhead crane system which is characterized by the differential equation of higher degree and swing motion. From the results of computer simulation, it is founded that the position of the overhead crane system is controlled, and the swing of the object is suppressed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.395-398
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• 1996

In this study, when the nonlinear overhead crane which allows simultaneously travel and traverse motion moves a desired transport route, the object suspended the end of rope does undesirable swing motion. Nonlinear overhead crane pertubes in the vicinity of an operating point, therefore the nonlinear overhead crane is modified to linear overhead crane for the operating point. The linear overhead crane was controlled to swing angles of the object by the ratio of torque inputs to motors of the girder and the trolley. As a basis for the result of the linear overhead crane, the nonlinear overhead crane was controlled swing angles of the object and positions of the overhead crane without collision with environmental equipment by partial state feedback control.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.630-639
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• 1999

To achieve fast loading and unloading of containers from a container ship, quick suppression of the remaining sway motion of the container at the end of each trolley stroke is crucial. Due to the pendulum motion of the container and disturbances like sind, residual sway always exists at the end of trolley movement. In this paper, the sway-control problem of a container crane is investigated. A two-stage control is proposed. The first stage is a time optimal controlfor the purpose of fast trolley traveling. The second stage is a nonlinear control for the quick suppression of residual sway, which starts right after the first stage while lowering the container. The nonlinear control is investigated in the perspective of controlling an underatuated mechanical system, which combines partial feedback linearization to account for the known nonlinearities as much as possible, and variable structure control to account for the unmodeled dynamics and disturbances. Simulation and experimental results are provided.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2474-2479
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• 2005

The control objectives in this paper are to move the gantry of a container crane to its target position and to suppress the transverse vibration of the payload. The crane system is modeled as an axially moving nonlinear string equation, in which control inputs are applied at both ends, through the gantry and the payload. The dynamics of the moving string are derived using Hamilton's principle. The Lyapunov function method is used in deriving a boundary control law, in which the Lyapunov function candidate is introduced from the total mechanical energy of the system. The performance of the proposed control law is compared with other two control algorithms available in the literature. Experimental results are given.

• Journal of Mechanical Science and Technology
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• 제20권5호
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• pp.591-601
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• 2006

In this paper, we present a new control methodology for perturbed crane systems. Nonlinear crane systems are transformed to linear models by feedback linearization. An inverse dynamic equation is applied to compute the system PD control force. The PD control parameters are selected based on a nominal model and are therefore suboptimal for a perturbed system. To achieve the desired performance despite model perturbations, we construct a neural network auxiliary controller to compensate for modeling errors and disturbances. The overall control input is the sum of the nominal PD control and the neural auxiliary control. The neural network is iteratively trained with a perturbed system until acceptable performance is attained. We apply the proposed control scheme to 2- and 3-degree-of-freedom (D.O.F.) crane systems, with known bounds on the payload mass. The effectiveness of the control approach is numerically demonstrated through computer simulation experiments.

• Journal of Advanced Research in Ocean Engineering
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• 제1권2호
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• pp.85-93
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• 2015

The nonlinear time-domain analysis method was implemented to carry out a series of integrated simulations for a deep-water crane vessel system composed of four sub components, including a floating vessel, lifted equipment, hoisting cable and dynamic positioning (hereinafter DP) system. The analysis of the coupled dynamics consists of the crane vessel and equipment connected using the crane wire, and the DP is modeled according to the wind, wave and current conditions. The DP systems were numerically implemented using a classical PD feedback controller, and various simulations of the deepwater installation were conducted using different conditions in order to evaluate the global performance of the floating crane vessel combined with the DP system.

• 한국항해항만학회지
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• 제31권6호
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• pp.523-530
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• 2007

크레인 시스템은 항만 터미널 등의 산업현장에서 무거운 물체를 이송하는데 사용되는 장비로서 그 정확성과 신속성을 동시에 만족시키기 위한 연구가 활발히 진행되고 있다. 본 논문은 적응제어기의 일종인 모델매칭 기법을 이용하여 복잡한 3 자유도 비선형 크레인의 제어 시스템에 대한 연구를 제안한다. 피드백 선형화(feedback linearization)를 통해 비선형 크레인 모델을 선형화한 후 PD 제어기를 적용하여 선형 공칭 모텔을 구한다. 이 모델은 시스템 섭동을 갖는 실시간 시스템 모델과 함께, 리아푸노브(Lyapunov) 이론을 적용하여 실시간 섭동에 의해 발생되는 제어오차를 감소하기 위한 보조 제어규칙의 산출에 이용된다. 또한 리아푸노브 안정성이론을 적용하여 구성한 크레인 제어시스템의 안정성 해석을 실시한다. 컴퓨터 시뮬레이션을 통해 제안한 알고리즘의 타당성을 검증하며 기존의 제어방식과 비교 분석하여 그 우수성을 입증한다.

• 수산해양기술연구
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• 제56권1호
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• pp.61-70
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• 2020

In this study, system modeling and dynamic analysis of crane are conducted. Especially, among many different kinds of a crane system, the issues on crane operating problems installed on the vessel are considered. As well known, marine systems including cranes are exposed to various disturbances such as vessel motions, hydrodynamic forces, wave and wind attack, etc. In order to analysis the system dynamic with environmental conditions, the authors derived the nonlinear dynamic model of offshore crane and derived a linear model which is used for designing the control system. Using the obtained nonlinear and linear models, simulations were conducted to evaluate the usefulness of the obtained models. By simulation and result evaluation, the usefulness of the linear model, which presents the dynamics, is effectively verified.