A robust game optimization for electromagnetic buffer under parameters uncertainty

2022 ◽  
Author(s):  
Fengjie Xu ◽  
Guolai Yang ◽  
Liqun Wang ◽  
Zixuan Li ◽  
Xiuye Wang
Keyword(s):  
Parameters Uncertainty

scholarly journals Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jinde Cao ◽  
Abdulaziz Alofi ◽  
Abdullah Al-Mazrooei ◽  
Ahmed Elaiw
Keyword(s):  
Neural Networks ◽  
Matrix Measure ◽  
Chaotic Neural Networks ◽  
Time Varying Delay ◽  
Synchronization Problem ◽  
Arbitrary Switching ◽  
Parameters Uncertainty ◽  
Inequality Technique ◽  
Error System ◽  
Switched Interval Networks

This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

Uncertainty in Collapse Strength Prediction of Sandwich Pipelines

2021 ◽  
Author(s):  
U. Bhardwaj ◽  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Probabilistic Models ◽  
Uncertainty Propagation ◽  
Simulation Method ◽  
External Pressure ◽  
Design Parameters ◽  
Collapse Strength ◽  
Parameters Uncertainty ◽  
Wide Range ◽  
Model Predictions ◽  
Strength Models

Abstract This paper assesses the uncertainty in the collapse strength of sandwich pipelines under external pressure predicted by various strength models in three categories based on interlayer adhesion conditions. First, the validity of the strength models is verified by comparing their predictions with sandwich pipeline collapse test data and the corresponding model uncertainty factors are derived. Then, a parametric analysis of deterministic collapse strength predictions by models is conducted, illustrating insights of models’ behaviour for a wide range of design configurations. Furthermore, the uncertainty among different model predictions is perceived at different configurations of outer and inner pipes and core thicknesses. A case study of a realistic sandwich pipeline is developed, and probabilistic models are defined to basic design parameters. Uncertainty propagation of models’ predictions is assessed by the Monte Carlo simulation method. Finally, the strength model predictions of sandwich pipelines are compared to that of an equivalent single walled pipe.

Robust Adaptive Inverse Optimal Tracking for Strict-Feedback Stochastic Nonlinear System

2010 ◽  
Vol 143-144 ◽  
pp. 984-989 ◽  
Author(s):  
Lu Wang
Keyword(s):  
Reference Signal ◽  
Original System ◽  
Optimal Tracking ◽  
Stochastic Nonlinear System ◽  
Parameters Uncertainty ◽  
Adaptive Laws ◽  
Error System ◽  
Robust Adaptive ◽  
Backstepping Algorithm ◽  
Strict Feedback

For a class of strict-feedback nonlinear systems with unknown constant parameters, uncertainty in the input gains and standard Wiener-noise, combining the system function with the known reference signal, the error system is obtained. The adaptive inverse optimal controller and adaptive laws of parameters are designed using Backstepping algorithm, thus the robust adaptive inverse optimal tracking problem of original system is solved．

scholarly journals Simulation of the Fate and Seasonal Variations ofα-Hexachlorocyclohexane in Lake Chaohu Using a Dynamic Fugacity Model

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xiang-zhen Kong ◽  
Wei He ◽  
Ning Qin ◽  
Qi-Shuang He ◽  
Bin Yang ◽  
...  
Keyword(s):  
Seasonal Variations ◽  
Model Uncertainty ◽  
Suspended Solids ◽  
Diffusion Flux ◽  
Sensitivity Analyses ◽  
Lake Chaohu ◽  
Fugacity Model ◽  
Parameters Uncertainty ◽  
Influential Parameters ◽  
Good Agreement

Fate and seasonal variations ofα-hexachlorocyclohexane (α-HCH) were simulated using a dynamic fugacity model in Lake Chaohu, China. Sensitivity analyses were performed to identify influential parameters and Monte Carlo simulation was conducted to assess model uncertainty. The calculated and measured values of the model were in good agreement except for suspended solids, which might be due to disregarding the plankton in water. The major source ofα-HCH was an input from atmospheric advection, while the major environmental outputs were atmospheric advection and sediment degradation. The net annual input and output ofα-HCH were approximately 0.294 t and 0.412 t, respectively. Sediment was an important sink forα-HCH. Seasonal patterns in various media were successfully modeled and factors leading to this seasonality were discussed. Sensitivity analysis found that parameters of source and degradation were more important than the other parameters. The sediment was influenced more by various parameters than air and water were. Temperature variation had a greater impact on the dynamics of the model output than other dynamic parameters. Uncertainty analysis showed that the model uncertainty was relatively low but significantly increased in the second half of the simulation period due to the increase in the gas-water diffusion flux variability.

scholarly journals Design of Adaptive Sliding Mode Controller for Uncertain Pendulum System

2021 ◽  
Vol 39 (3A) ◽  
pp. 355-369
Author(s):  
Dina H. Tohma ◽  
Ahmed K. Hamoudi
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
External Disturbances ◽  
Control Effort ◽  
Pendulum System ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller ◽  
Parameters Uncertainty ◽  
Better Than

This work aims to study and apply the adaptive sliding mode controller (ASMC) for the pendulum system with the existence of the parameters uncertainty, external disturbances, and coulomb friction. The adaptive sliding mode controller has several features over the conventional sliding mode control method. Firstly, the magnitude of the control signal is reduced to the minimally acceptable level defined by special conditions concerned with ASMC algorithm. Secondly, the upper bounds of uncertainties are not necessary to be defined before starting the work. For this reason, the ASMC can be used successfully to control the pendulum system with minimum control effort. These properties of the ASMC are confirming graphically by the simulation results using MATLAB 2019. The ASMC achieves an asymptotically stable system better than the Classical Sliding Mode Controller (CSMC). The unwanted phenomenon is called “chattering", which is appearing in the control action signal. These drawback properties are suppressed by employing a saturation function. Finally, the comparison between the results of the ASMC and CSMC showed that ASMC is the better one.

scholarly journals Parametric Sparse Bayesian Dictionary Learning for Multiple Sources Localization With Propagation Parameters Uncertainty

2020 ◽  
Vol 68 ◽  
pp. 4194-4209
Author(s):  
Kangyong You ◽  
Wenbin Guo ◽  
Tao Peng ◽  
Yueliang Liu ◽  
Peiliang Zuo ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Multiple Sources ◽  
Parameters Uncertainty ◽  
Propagation Parameters
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