scholarly journals Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Xing Zong-yi ◽  
Qin Yong ◽  
Pang Xue-miao ◽  
Jia Li-min ◽  
Zhang Yuan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Distance Measure ◽  
Rbf Neural Network ◽  
Least Square Method ◽  
Least Square ◽  
Nonlinear Characteristics ◽  
Electrohydraulic System ◽  
Comparison Results ◽  
Depth Control

The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by genetic algorithm-based technique. In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network. The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of the proposed technique is relieved. The proposed technique is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the electrohydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method.

The Layout Measurement Points Prediction and Flatness Calculation for the Antenna Plate After Welding Assembly Based on the RBF Neural Network Model

2016 ◽  
Author(s):  
Minghui Pan ◽  
Wencheng Tang ◽  
Yan Xing ◽  
Jun Ni
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Least Square Method ◽  
Least Square ◽  
Measuring Point ◽  
Improve Measurement ◽  
Neural Network Prediction ◽  
Measuring Experiment ◽  
Predicted Height ◽  
Measurement Efficiency

Due to the effect of the antenna plate flatness on the antenna performances, the flatness is one of the key performance indicators for the planar antenna. Before calculating the antenna plate flatness, the support assembly tools are built, and then measuring experiment for height coordinate values is carrying out on the assembly platform. This paper presents a predictive method that is the Radial Basis Function (RBF) neural network method to obtain the height coordinate values based on fewer measurement points on the antenna plate after welding assembly, and the antenna plate flatness is calculated by fitting least square plane using measuring point coordinate value through the least square method (LSM). Simultaneously, before or after welding assembly, comparing with the calculated flatness value, it is shown that the calculated flatness value by the predicted height coordinate values basically agrees well with the initial calculated flatness value. These results reveal that the RBF neural network prediction is adopted to be very correct and valid, which will reduce the measurement cost and improve measurement efficiency in future.

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.

Nonlinear Rectification for Capacitance Method Measuring the Moisture of Green Sand Based on Functional Link Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 806-810
Author(s):  
De Quan Shi ◽  
Gui Li Gao ◽  
Jing Wei Dong ◽  
Li Hua Wang
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Least Square Method ◽  
Least Square ◽  
Green Sand ◽  
Functional Link ◽  
Moisture Sensor ◽  
Single Output ◽  
Capacitance Method ◽  
Functional Link Neural Network

In order to solve the nonlinear output/input problem of the capacitance method measuring the moisture content of green sand, a nonlinear compensation is added into the measurement system and the neural network is used for nonlinear rectification. Based on introducing the principle of non-linear compensation, a functional link artificial network with multi-input and single-output is constructed. In the network, the output voltage of capacitance moisture sensor is taken as the input and the moisture content of green sand is taken as the output. The data samples obtained in laboratory are used to train the network, and the dynamic rectification model is got. The experimental results show that the maximum difference and relative error of the moisture content are ±0.09% and ±1.85% after nonlinear rectification by the functional link neural network, and it is significantly better than those of the least square method.

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