A generalized approach to anti-sway control for shipboard cranes

Martin, Iain A.; Irani, Rishad A.

doi:10.1016/j.ymssp.2020.107168

Cited by 27 publications

(8 citation statements)

References 29 publications

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“…The controller of PID is easy to use and robust. 29 However, the PID control has poor anti-interference performance, and the accuracy of regulation under complex models cannot reach the requirement. The specific effect of using PID control for cable constant tension control in this paper is unknown.…”

Section: Pid Control Of the Traction Winchmentioning

confidence: 99%

BP fuzzy neural network PID based constant tension control of traction winch

Zhu

Zhang

Zhao

et al. 2022

Measurement and Control

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As part of the ocean winch, the traction winch mainly plays the role of attenuating the load tension. It generally uses simple speed control. For the ship-based collection and release system, complex sea conditions make the cable tension constantly changes. The traditional control method will make the cable in the traction winch too slack or too tight. Meanwhile, it can result in the wrong rope, bite rope and slippage, and other phenomena. Eventually, it is leading to cable damage or breakage and equipment loss. Moreover, with the introduction of expensive photoelectric composite cables, there is a greater need for a good control algorithm to extend the life of the cable and ensure the safety of retrieval and release. Therefore, this paper focuses on the constant tension control of the traction winch cable. In this paper, the NARX neural network-based cable tension compensation value prediction model is established as the feedforward part based on historical ship heave displacement data and cable tension compensation data as training data. On this basis, the simulation model of cable constant tension winch based on common PID control, fuzzy PID control, and BP fuzzy neural network PID control are established. By comparing the tension of the cable with the above controller, the results show that the tension control of the cable based on the NARX neural network predicted tension compensation value plus BP fuzzy neural network PID control has high accuracy, fast dynamic response, smooth system, and no overshoot.

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Section: Pid Control Of the Traction Winchmentioning

confidence: 99%

BP fuzzy neural network PID based constant tension control of traction winch

Zhu

Zhang

Zhao

et al. 2022

Measurement and Control

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“…The bridge crane system is a typical underactuated system with strong coupling and nonlinear characteristics. In the process of lifting, lowering, and transporting payloads by the bridge crane, the payload of the crane will be affected by wind and mechanical inertia, resulting in a phenomenon similar to pendulum oscillation, which seriously affects the working efficiency of the crane system and the safety of workers [2] .…”

Section: Introductionmentioning

confidence: 99%

Energy Coupling Control of Bridge Crane Based on Adaptive Fuzzy Strategy

Ye³

2023

J. Phys.: Conf. Ser.

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Aiming at the problem that the traditional PID algorithm omits the nonlinear characteristics of the bridge crane system and the control effect is limited, this paper puts forward an energy coupling control strategy based on an adaptive fuzzy strategy. The coupling generalized semaphore for the Lyapunov controller is obtained by solving the dynamic equation of the bridge crane with energy, and the controller parameters are adjusted by using an adaptive fuzzy strategy. The simulation results show that the designed controller has a better anti-sway effect than the traditional PID controller.

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“…Several feedback control approaches have been proposed for various types of crane to enable the cranes to regulate its performance, by ensuring that the actual output is closer to the desired response. These include adaptive control [8], nonlinear control [9][10][11], sliding mode control [12], time optimal control [13], generalised trajectory modification strategy [14], and fuzzy logic control [15]. For control of the DPOC, feedback control strategies that have been proposed were adaptive [7], linear [16], nonlinear [17][18], and intelligent [19] control approaches.…”

Section: Introductionmentioning

confidence: 99%