Predictive Robust Control Based on Higher-Order Sliding Mode for Passive Heave Compensator With Hydraulic Transformer

Passive heave compensation (PHC) system is widely applied in offshore equipment because of its superiority in energy conserving and reliability. However, it has poor adaptability to changing sea conditions and the compensation accuracy is low. Hydraulic transformer (HT), working as a pressure-flow control element, can potentially solve the problems mentioned above. In this paper, an HT based PHC (HTPHC) system is proposed for the first time, and a compensation algorithm based on higher-order sliding mode (HOSM) together with a prediction algorithm for the heave motion of the vessel is derived to get good compensation effect using the new PHC system. The prediction algorithm is proved to be effective according to the measured data of sea trials, and reduces the difficulty of designing and parameter tuning process compared with the existing ones. The effectiveness of the proposed control algorithm is evaluated with simulation, moreover, the effectiveness can still be maintained under changing sea conditions which is also verified by simulation.

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Higher-order temporal network effects through triplet evolution

Scientific Reports ◽

10.1038/s41598-021-94389-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Qing Yao ◽

Bingsheng Chen ◽

Tim S. Evans ◽

Kim Christensen

Keyword(s):

Real World ◽

Link Prediction ◽

Higher Order ◽

Prediction Algorithm ◽

Interaction Patterns ◽

Temporal Networks ◽

World Systems ◽

Order Interaction ◽

Space And Time ◽

Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

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