Dynamics and swing control of double-pendulum bridge cranes with distributed-mass beams

Huang, Jie; Liang, Zan; Zang, Qiang

doi:10.1016/j.ymssp.2014.09.005

Cited by 73 publications

(32 citation statements)

References 27 publications

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“…With various kinds of behaviour, such as their visual observation, collaborative predatory detection, sense of smell, psychological induction, and body language, an intelligent sensor network of real-time communication with each other is established. Their communication methods are concise and efficient [23], and the wolf pack has strong abilities for seeking, coordinating, and controlling. These habits and characteristics of wolves are beneficial for a search algorithm based on the behaviour of wolves.…”

Section: Introductionmentioning

confidence: 99%

An improved wolf colony search algorithm based on mutual communication by a sensor perception of wireless networking

Chenghai

Qin

et al. 2018

J Wireless Com Network

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Intelligence optimization algorithms have an important application in engineering calculation. Swarm intelligence optimization algorithms belong to the class of intelligent algorithms, which are derived from the simulation of natural biological evolution or foraging behaviours. But these algorithms have sometimes slow convergence, and for multi-peak problems, they are easy to fall into the local optimal solution in the later period of the algorithms.To solve these problems, in this paper, the advantages of these algorithms and the wolf colony search algorithm based on the strategy of the leader (LWCA) algorithm are used, to propose an improved wolf colony search algorithm with the strategy of the leader algorithm (ILWCA). ILWCA is based on mutual communication by the sensor perception of wireless networking, adding a global update strategy and a step acceleration network. In addition, by introducing the concept of individual density to depict the distribution density of the wolf, the problem of excessive input parameters of traditional wolf group algorithm is solved. Moreover, by adding the way of mutual migration, the algorithm increases the communication between wolves, and strengthens the overall performance of the optimization process. Finally, the experimental results show that the ILWCA algorithm achieves higher solution accuracy and better robustness compared with the accuracy and robustness of particle swarm optimization (PSO), gravitational search algorithm (GSA), swarm optimization algorithm (SOA), grey wolf optimizer (GWO), and genetic algorithms (GA).

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Section: Introductionmentioning

confidence: 99%

An improved wolf colony search algorithm based on mutual communication by a sensor perception of wireless networking

Chenghai

Qin

et al. 2018

J Wireless Com Network

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show abstract

“…Cha et al (2010) have studied extra-heavy cargo lifting by two floating cranes with a barge ship connected between the two cranes [6]. Huang et al (2015) have proposed a new doublependulum mechanical model, describing the transportation of a large payload with distributed mass, which is attached to one trolley by one suspension cable and two rigging cables [8]. Huang et al (2015) have suppressed payload vibration during transportation by control of trolley acceleration [8].…”

Section: Background and Introductionmentioning

confidence: 99%

“…Huang et al (2015) have proposed a new doublependulum mechanical model, describing the transportation of a large payload with distributed mass, which is attached to one trolley by one suspension cable and two rigging cables [8]. Huang et al (2015) have suppressed payload vibration during transportation by control of trolley acceleration [8]. Mechanical disadvantage of Huang's model is a usage of only one suspension cable.…”

Section: Background and Introductionmentioning

confidence: 99%

Research into 2D Dynamics and Control of Small Oscillations of a Cross-Beam during Transportation by Two Overhead Cranes

Perig¹,

Stadnik²,

Kostikov³

et al. 2017

Shock and Vibration

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A new mathematical model of a 3DOF 2D mechanical system “transported cross-beam, two moving bridge cranes” has been proposed. Small system oscillations have been derived through the introduction of Lagrange equations. The numerical estimation of 3DOF system motion has been carried out with equation-based Modelica language. The present article uses the Lagrange method and numerical and optimization methods, realized with JModelica.org and Optimica freeware. The absolute swaying of the cross-beam with respect to the displacement of the two moving bridge cranes was estimated. The phase portraits of the 3DOF system for linear and angular coordinates were presented. An open loop optimal control problem was posed for the motion of the bridge cranes. A “bang-bang” control strategy was implemented for the derivation of an optimal control solution, which enables the travel of two bridge cranes at a prescribed distance for minimum time and minimum swaying of a heavy cross-beam. The derived results of the numerical simulation can be easily practically realized by crane operators with good agreement with simple engineering estimations. The proposed control strategy enables synchronous motion of two bridge cranes with a cross-beam that practically solves the posed problem of unwanted excessive oscillations of a heavy cross-beam during transportation.

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“…are used have been applied to cranes [2,3]. Open loop control schemes such as input shaping [4][5][6], adaptive input shaping [7] and distributed-mass payload dynamics [8][9][10] Although the open loop control techniques are cheaper and easy to implement since they do not require the use of sensors, they are very sensitive towards external disturbances since the control input does not account the changes of the system. Several types of closed loop control techniques have been implemented to crane systems for motion and swing control.…”

Section: Introductionmentioning

confidence: 99%

Anti-Swing Control of an Overhead Crane by Using Genetic Algorithm Based LQR

Önen

2017

ijecs

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Dynamics and swing control of double-pendulum bridge cranes with distributed-mass beams

Cited by 73 publications

References 27 publications

An improved wolf colony search algorithm based on mutual communication by a sensor perception of wireless networking

An improved wolf colony search algorithm based on mutual communication by a sensor perception of wireless networking

Research into 2D Dynamics and Control of Small Oscillations of a Cross-Beam during Transportation by Two Overhead Cranes

Anti-Swing Control of an Overhead Crane by Using Genetic Algorithm Based LQR

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