Adaptive trajectory tracking control of vector propulsion unmanned surface vehicle with disturbances and input saturation

Sun, Xiaojie; Wang, Guofeng; Fan, Yunsheng

doi:10.1007/s11071-021-06873-7

Cited by 21 publications

(11 citation statements)

References 44 publications

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“…In the research process, it is found that the calculation process of wavelet neural network algorithm is more complex than LS-SVM. At the same time, due to the small number of samples in this paper (there are about 480 training samples in literature [7]), there is an overfitting phenomenon, but the accuracy of future trend prediction is also weaker than the model proposed in this article.…”

Section: Analysis and Comparison Of Model Prediction Resultsmentioning

confidence: 80%

“…Next, based on the conclusion that there is a nonlinear relationship among SMPI, CPI, PPI, and MPI, we use the wavelet neural network algorithm proposed in literature [7] to study the nonlinear cointegration relationship, establish the NECM model to predict SMPI, and get the prediction results from 2018 to 2021. e results are shown in Table 7. In the research process, it is found that the calculation process of wavelet neural network algorithm is more complex than LS-SVM.…”

Section: Analysis and Comparison Of Model Prediction Resultsmentioning

confidence: 99%

“…In view of the fact that the general linear cointegration analysis method cannot realize the nonlinear cointegration test and modeling, the literature [6] proposed a neural network algorithm suitable for the test and modeling of the nonlinear cointegration relationship of vector time series and discussed the theoretical basis of the method and its feasibility. By using a wavelet neural network with the ability to approximate nonlinear functions arbitrarily, literature [7] studied the NECM of nonlinear cointegration systems, provided the modeling method, and carried out relevant analysis. ese research found that compared with the linear VAR model, the NECM has a better prediction effect and can effectively predict the nonlinear economic system.…”

Section: Introductionmentioning

confidence: 99%

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Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

2022

Complexity

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According to the definition of nonlinear cointegration, this article studies the small sample nonlinear cointegration test and NECM (Nonlinear Error Correction Model) based on the LS-SVM (Least Squares Support Vector Machine) optimized by PSO (Particle Swarm Optimization). And the logical process of this method is also designed. Then, we carry out empirical research on the ship maintenance cost index and the several price indexes. Based on the judgment of the type of cointegration test relationship, the test of the nonlinear cointegration test relationship of small samples is realized, and the NECM for predicting the ship maintenance cost index is established as well, which is compared with the VAR model of linear vector. The research result shows that the small sample nonlinear cointegration test and modeling method based on the LS-SVM Optimized by PSO can describe the nonlinear cointegration test relationship of the small sample system. And the NECM has better performance. The prediction effect can effectively predict small sample nonlinear systems. We also compare the prediction results with the wavelet neural network algorithm, and the results show that the generalization ability of LS-SVM Optimized by PSO is better, and the prediction accuracy of small samples is higher.

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Section: Analysis and Comparison Of Model Prediction Resultsmentioning

confidence: 80%

Section: Analysis and Comparison Of Model Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

2022

Complexity

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“…Remark 1: The lateral velocity are not considered in the kinematic model of WMRs [33], but USVs is sus-ceptible to disturbances from currents and crosswinds [42]. Therefore, to unify the kinematics model of the robot on the water surface and the ground, the model includes lateral velocity.…”

Section: Assumptionmentioning

confidence: 99%

“…Interestingly, the form of the HSMO is very similar to that of ESO with sign function. Furthermore, neural networks can estimate uncertain disturbances instead of observers [42,43].…”

Section: Introductionmentioning

confidence: 99%

Adaptive ground and water surface control for cross-domain robots based on adaptive finite-time convergence sliding mode observer

Wang

Liu

Huang

2023

Preprint

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The non-holonomic constraints cross-domain robot (CDR) is unable to follow the desired trajectory when moving on the ground or water surface due to the lateral side-slipping. Thus, an adaptive positionattitude controller is proposed in this paper to re-plan the robot’s angular velocity and linear velocity. To achieve adaptive control of the robot on the water surface and ground, an adaptive finite-time convergence sliding mode observer (AFSMO) is proposed. This observer is used to obtain time-varying and unknown lumped disturbance which includes uncertain dynamics parameters and environmental disturbances. An adaptive law is designed to improve observer accuracy and reduce output chattering. FSMO is also used to obtain the performance parameter to prevent driver saturation when the robot moves on the water surface. The performance parameter limits the maximum angular velocity and linear velocity. Simulation results demonstrate the effectiveness of the proposed control methods.

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Event-triggered observer-based T-S fuzzy dynamic positioning fault-tolerant control for unmanned surface vehicle

Sun

Shi

Hou

2023

Neural Comput & Applic

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Adaptive trajectory tracking control of vector propulsion unmanned surface vehicle with disturbances and input saturation

Cited by 21 publications

References 44 publications

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

Adaptive ground and water surface control for cross-domain robots based on adaptive finite-time convergence sliding mode observer

Event-triggered observer-based T-S fuzzy dynamic positioning fault-tolerant control for unmanned surface vehicle

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