Dynamic amplification factors for a system with multiple-degrees-of-freedom

Zhang, Chao; Hong, Hyun-Ki; Bi, Kaiming; Yan, Xueyuan

doi:10.1007/s11803-020-0567-9

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…) where ζ = (0.05 − 0.2) is the damping ratio of the system. Define DAF as the ratio of the dynamic response to the static response, the displacement DAF of an damped SDOF system subjected to a step load is therefore [35]:…”

Section: B Dynamic Amplification Factor In Transfer Function Of Kinem...mentioning

confidence: 99%

“…To fairly compare the simulation results obtained, PSO-SAFC and other solutions apply the same number of training parameters and vessel structure modelling. The performance of PSO-SAFC is compared with that of the FPID [35] for JS control. In this study, we use the PSO searching parameters as same as Table 6 for implementing the proposed system.…”

Section: ) Pso-safc Controller Designmentioning

confidence: 99%

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Self-Adaptive Fuzzy Control Approach for Jack-up Rig Jacking System Based on Particle Swarm Optimization

Dang

Tran

et al. 2022

IEEE Access

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Many offshore projects, such as offshore oil and gas exploration and offshore wind farm development, have required controlling the position of elevating the hull in a stable and balanced manner in recent years. The Jack-up Rig (JuR) jacking control systems are a revolutionary innovation that is already being employed in offshore drilling and other maritime structures. The system is utilized automatically to control and stabilize the position of the JuR during sea state disturbances to keep platforms from being displaced. As a result, developing an improved control theory for the Jacking system (JS) of a JuR is very important. In this paper, First, we investigate how to adapt to the effects of external forces and hydrodynamic amplification using a particle swarm optimization strategy based on a fuzzy controller. Then, using the fuzzy controller as the foundation, we proposed the PSO -Self Adaptive Fuzzy Controller (PSO-SAFC) guaranteed by Lyapunov criteria and compares it with other fuzzy-based algorithms to show the benefit of adaptively and stability of the system. To verify the proposed algorithm, embedded STM32F746VG central processor used to build the algorithm and run Simulink models on Matlab, the collected data on the positions of the legs and the tilt compared it to the setting command values to move the Rig body and provided control signals to the Drive system. Eventually, the advantages of the proposed approach are demonstrated by the simulation and experiment outcomes.

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Section: B Dynamic Amplification Factor In Transfer Function Of Kinem...mentioning

confidence: 99%

Section: ) Pso-safc Controller Designmentioning

confidence: 99%

Self-Adaptive Fuzzy Control Approach for Jack-up Rig Jacking System Based on Particle Swarm Optimization

Dang

Tran

et al. 2022

IEEE Access

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“…Because of this, it is simple to derive the equations of motion for the JuR by first determining the equations of motion for each leg and the upper hull separately, then integrating them, while enforcing the constraints of deformation compatibility and force equilibrium at the intersections of the legs and the hull. Dynamic equations of motion-related topics are discussed in detail in [19][20][21][22][23][24], where the dynamic motion model is impacted by internal elements, including stress and stiffness [25], as well as environmental elements [26]. There are six degrees of freedom at each nodal point:…”

Section: Mathematical Model Of the Jacking Control Systemmentioning

confidence: 99%

Jacking and Energy Consumption Control Over Network for Jack-Up Rig: Simulation and Experiment

Dang

Tran

et al. 2022

Polish Maritime Research

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Oil and gas projects differ from regular investment projects in that they are frequently large-scale, categorised as vital national projects, highly technological, and associated with significant risks. Drilling rigs are a crucial component of the oil and gas sector and the majority of the systems and equipment aboard drilling rigs are operated automatically. Consequently, it is crucial to address the topic of an advanced control theory for off-shore systems. Network technology connected to control is progressively being used to replace outdated technologies, together with other contemporary technologies. In this study, we examine how to adapt a networked control jacking system to the effects of internal and external disturbances with a time delay, using a Fuzzy controller (FC)-based particle swarm optimisation. To demonstrate the benefit of the proposed approach, the developed Fuzzy Particle Swarm Optimisation (FPSO) controller is compared with the fuzzy controller. Finally, the results from simulations and experiments utilising Matlab software and embedded systems demonstrate the suitability of the proposed approach.

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“…In the absence of detailed information about pterosaur feeding strategies, it is hard to identify a reliable value for this load multiplier. Nonetheless, it appears reasonable to consider the same value of the dynamic amplification factor associated with a dynamic system with a suddenly applied load, i.e., 2 (Chao et al, 2020). In this condition, Equation ( 6) gives a maximum mass for the prey ranging between 9 kg and 13 kg.…”

Section: Implications For Azhdarchid Feedingmentioning

confidence: 99%

Helically arranged cross struts in azhdarchid pterosaur cervical vertebrae and their biomechanical implications

et al. 2021

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Dynamic amplification factors for a system with multiple-degrees-of-freedom

Cited by 9 publications

References 17 publications

Self-Adaptive Fuzzy Control Approach for Jack-up Rig Jacking System Based on Particle Swarm Optimization

Self-Adaptive Fuzzy Control Approach for Jack-up Rig Jacking System Based on Particle Swarm Optimization

Jacking and Energy Consumption Control Over Network for Jack-Up Rig: Simulation and Experiment

Helically arranged cross struts in azhdarchid pterosaur cervical vertebrae and their biomechanical implications

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