Self-Adapting Anti-Surge Intelligence Control and Numerical Simulation of Centrifugal Compressors Based on RBF Neural Network

2021 ◽  
Author(s):  
San He ◽  
Mengyu Xie ◽  
Patioon Tontiwachwuthikul ◽  
Christine Chan ◽  
Jianfeng Li
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Rbf Neural Network ◽  
Centrifugal Compressors ◽  
Intelligence Control

scholarly journals Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network

2021 ◽  
Vol 13 (1) ◽  
pp. 72-84
Author(s):  
Yiran Yang ◽  
Xingping Lai ◽  
Tao Luo ◽  
Kekuo Yuan ◽  
Feng Cui
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Relative Error ◽  
Test Data ◽  
Creep Test ◽  
Rbf Neural Network ◽  
Creep Property ◽  
Creep Tests ◽  
Viscoplastic Model ◽  
Mean Relative Error

Abstract Creep is a fundamental time-dependent property of rock. As one of the main surrounding rocks of underground engineering, layered siltstone is governed by creep to a great extent because of special structure. Based on the structural characteristics of layered siltstone, a viscoelastic–viscoplastic model was proposed to simulate and present its creep property. To verify the accuracy of the model, governing equation of the viscoelastic–viscoplastic model was introduced into finite element difference program to simulate a series of creep tests of layered siltstone. Meanwhile, creep tests on layered siltstone were conducted. Numerical simulation results of the viscoelastic–viscoplastic model were compared with creep test data. Mean relative error of creep test data and numerical simulation result was 0.41%. Combined with Lyapunov function, the radial basis function (RBF) neural network trained with creep test data was adopted. Mean relative error of creep test data and RBF neural network data was 0.57%. The results further showed high accuracy and stability of RBF neural network and the viscoelastic–viscoplastic model.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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