Design of motion control of dam safety inspection underwater vehicle

Sun, Yougang; Wan, Lei; Gan, Yong; Wang, Jianguo; Jiang, Chunmeng

doi:10.1007/s11771-012-1171-6

Cited by 21 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…B ECAUSE of advances in deep learning in recent years, physics-based simulation fields such as character animations [13], [14], [29] and fluid simulations [6], [15], [24], [30] have also been remarkably improved because of deep learning. Accuracy and efficiency in fields such as style transfer [5], [16], [27], character motion control [17], [26], and numerical analysis [18] have also improved. However, in certain fields, it still suffers from the need for a large amount of computation.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Smoke Simulation by Super-Resolution With Deep Learning on Downscaled and Binarized Space

et al. 2021

View full text Add to dashboard Cite

In this paper, we propose a highly efficient method for synthesizing high-resolution(HR) smoke simulations based on deep learning. A major issue for physics-based HR fluid simulations is that they require large amounts of physical memory and long execution times. In recent years, this issue has been addressed by developing deep-learning-based super-resolution(SR) methods that convert lowresolution(LR) fluid simulation results to HR(High-resolution) versions. However, these methods were not very efficient because they performed operations even in areas with low density or no density. In this paper, we propose a method that can maximize its efficiency by introducing a downscaled and binarized adaptive octree. However, even if it is divided by octree, because the number of nodes increases when the resolution of the simulation space is large, we reduce the size of the space by multiscaling and at the same time perform binarization to preserve the density that may be lost in this process. The octree calculated in this process has a structure similar to that of a multigrid solver, and the octree calculated at coarse resolution is restored to its original size and used for HR expression. Finally, we apply the SR process only to those areas having significant density values. Using the proposed method, the SR process is significantly faster and the memory efficiency is improved. The performance of our method is compared with that of an existing SR method to demonstrate its efficiency.

show abstract

Section: Introductionmentioning

confidence: 99%

Accelerated Smoke Simulation by Super-Resolution With Deep Learning on Downscaled and Binarized Space

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The S-plane control method presented in [1] combines fuzzy control ideology with structure of PD control, it not only simplified controller design, but also enhance nonlinear control effect. To improve the working performance of UV, [2] refers to error back propagation algorithm of neural network, and adds self-learning function for S-plane controller. [3] adopts immune generic algorithm to optimize control parameters of S-plane controller, in order to reduce the uncertainty within the design process.…”

Section: Introductionmentioning

confidence: 99%

“…The methods mentioned above verified by sea trials and good control effect was obtained, however, there also exists something to be improved. Take [2] for example, precise teacher signal is difficult to get; based on the requirements of hardware velocity and real-time performance, the method in [3] is difficult to put into practice. Because of the deep study of S-plane control, and plenty of experience is accumulated [4], therefore, expert technology [5] can be brought in to enhance the control performance.…”

Section: Introductionmentioning

confidence: 99%

Expert S-plane Control of Underwater Vehicle

Jiang¹,

Wan²,

Sun³

2015

Advances in Intelligent Systems Research

Self Cite

View full text Add to dashboard Cite

Study of motion control of underwater vehicle is deeply undertaken here. S-plane control is verified to be effective and validity in the control of UVs, but there are still problems in steady precision and parameter adjustments. To obtain higher steady precision, intelligent integral method is brought in, and expert S-plane control is introduced to adjust the control parameters on-line. To prevent output jumping, fuzzy neural network is adopted to fit the production rules in knowledge base. Experiments are finished, and the results demonstrate that the expert S-plane controller has better robustness than S-plane controller.

show abstract

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Sun

Zhang

et al. 2016

J. Cent. South Univ.

Self Cite

View full text Add to dashboard Cite

Autonomous underwater vehicles (AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model (estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

show abstract

Design of motion control of dam safety inspection underwater vehicle

Cited by 21 publications

References 8 publications

Accelerated Smoke Simulation by Super-Resolution With Deep Learning on Downscaled and Binarized Space

Accelerated Smoke Simulation by Super-Resolution With Deep Learning on Downscaled and Binarized Space

Expert S-plane Control of Underwater Vehicle

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Contact Info

Product

Resources

About