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Discussion on neighborhood optimal trajectory online correction algorithm and its application range

Journal of Systems Engineering and Electronics 2023年第4期34卷 1053-1062页

作者： LI Wanli LI Jiong LEI Humin Graduate School Air Force Engineering UniversityXi’an 710051China Air and Missile Defense College Air Force Engineering UniversityXi’an 710051China

This paper presents a neighborhood optimal trajectory online correction algorithm considering terminal time variation,and investigates its application range.Firstly,the motion model of midcourse guidance is established,and the online trajectory correction-regenerating strategy is introduced.Secondly,based on the neighborhood optimal control theory,a neighborhood optimal trajectory online correction algorithm considering the terminal time variation is proposed by adding the consideration of terminal time variation to the traditional neighborhood optimal trajectory correction method.Thirdly,the Monte Carlo simulation method is used to analyze the application range of the algorithm,which provides a basis for the division of application domain of the online correction algorithm and the online regeneration algorithm of midcourse guidance trajectory.Finally,the simulation results show that the algorithm has high real-time performance,and the online correction trajectory can meet the requirements of terminal constraint change.The application range of the algorithm is obtained through Monte Carlo simulation.

关键词： midcourse guidance online correction neighborhood optimal control application range

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Soft-Sensing Method with online correction Based on Semi-Supervised Learning

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Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 171-176页

作者：汤奇峰李德伟席裕庚 Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiaotong University

Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

关键词： soft-sensing semi-supervised learning(SSL) online correction neural network

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SVM Modeling on Slow Time Varying System and online correction Studying

SVM Modeling on Slow Time Varying System and Online Correcti...

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2011 Chinese Control and Decision Conference（CCDC）

作者： He Dakuo is with the Institute of Information Science and Engineering,Northeastern University,CO 70517,China.Feng Jie is with the Institute of Information Science and Engineering,Northeastern University,CO 84984,China Chang Hongrui is with the Institute of Information Science and Engineering,Northeastern University,CO 47285,China Chen Bing is with the Institute of Information Science and Engineering,Northeastern University,CO 31986,China

The problem of identification and online correction for slow time-varying system has become a hotspot.Based on the traditional modeling algorithm and their shortages,in this paper,the intelligence algorithm AO-SVM is studied intensively.Because of the defects of AO-SVM,such as the sampling deletion strategy and instability of the algorithm,the improved AO-SVM which is combined with ZD-SVM is proposed.The simulation results show that the improved algorithm ensures the accuracy of modeling and avoids the data saturation.

关键词： identification online correction slow time-varying system SVM

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