Litong Jia, Q. Gao, Yuan-long Hou, Zhiyuan Jia, L. Jin, Kang Li
{"title":"Active Disturbance Rejection Control of Certain Balanced and Positioning Electro-Hydraulic Servo System Based on Neural Network","authors":"Litong Jia, Q. Gao, Yuan-long Hou, Zhiyuan Jia, L. Jin, Kang Li","doi":"10.1109/IHMSC.2015.207","DOIUrl":null,"url":null,"abstract":"For certain balance and positioning of electro-hydraulic servo system existing nonlinear, the active disturbance rejection control (ADRC) has many adjustable parameters which are difficult to regulate, so active disturbance rejection control with neural network (NN-ADRC) is developed in this paper. The method uses neural network self-learning ability, through a single neuron adaptive configuration parameters to complete an online self-tuning parameters, while taking advantage of RBF neural network as identifier to identify the controlled object gradient information. Simulation results show that: the controller parameters are reduced significantly, and effectively inhibit the system unbalance force disturbance and realize the accurate positioning. It also has fast response speed, no overshoot, and high steady-state accuracy.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"20 1","pages":"211-215"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
For certain balance and positioning of electro-hydraulic servo system existing nonlinear, the active disturbance rejection control (ADRC) has many adjustable parameters which are difficult to regulate, so active disturbance rejection control with neural network (NN-ADRC) is developed in this paper. The method uses neural network self-learning ability, through a single neuron adaptive configuration parameters to complete an online self-tuning parameters, while taking advantage of RBF neural network as identifier to identify the controlled object gradient information. Simulation results show that: the controller parameters are reduced significantly, and effectively inhibit the system unbalance force disturbance and realize the accurate positioning. It also has fast response speed, no overshoot, and high steady-state accuracy.