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Immersion and invariance adaptive finite-time control of air-breathing hypersonic vehicles 期刊论文
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2019, 卷号: 233, 期号: 7, 页码: 2626-2641
Authors:  Han, Chao;  Liu, Zhen;  Yi, Jianqiang
Favorite  |  View/Download:10/0  |  Submit date:2019/07/11
Immersion and invariance  adaptive control  finite-time control  hypersonic vehicles  
Conditional disturbance negation based active disturbance rejection control for hypersonic vehicles 期刊论文
CONTROL ENGINEERING PRACTICE, 2019, 卷号: 84, 页码: 159-171
Authors:  Sun, Jinlin;  Pu, Zhiqiang;  Yi, Jianqiang
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Active disturbance rejection control  Conditional disturbance negation  Disturbance characterization indicator  Hypersonic vehicle  
Augmented fixed-time observer-based continuous robust control for hypersonic vehicles with measurement noises 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2019, 卷号: 13, 期号: 3, 页码: 422-433
Authors:  Sun, Jinlin;  Yi, Jianqiang;  Pu, Zhiqiang
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linearisation techniques  observers  control system synthesis  vehicle dynamics  adaptive control  aircraft control  uncertain systems  robust control  variable structure systems  space vehicles  augmented fixed-time observer  measurement noises  cruise control  flexible air-breathing hypersonic vehicles  controller gains  finite-time convergence  fixed convergence time  FAHV  gain adaptation law  continuous robust control  input-output linearisation technique  
Adaptive Neural Network Time-varying Formation Tracking Control for Multi-agent Systems via Minimal Learning Parameter Approach 会议论文
, Budapest, Hungary, July 14-19, 2019
Authors:  Xiong Tianyi;  Pu Zhiqiang;  Yi Jianqiang;  Sui Zezhi
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Formation Control  Minimal Learning Parameter  Multi-agent System  Neural Network  
State-Estimator-Integrated Robust Adaptive Tracking Control for Flexible Air-Breathing Hypersonic Vehicle With Noisy Measurements 期刊论文
IEEE Transactions on Instrumentation and Measurement, 2019, 卷号: XX, 期号: Early Access, 页码: XX
Authors:  Tao Xinlong;  Yi Jianqiang;  Pu Zhiqiang;  Xiong Tianyi
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Hypersonic Vehicle  Measurement Noises  Robust Control  State Estimator  Type-2 Fuzzy Logic System  
Observer-based robust adaptive T2 fuzzy tracking control for flexible air-breathing hypersonic vehicles 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2018, 卷号: 12, 期号: 8, 页码: 1036-1045
Authors:  Liu, Yifan;  Pu, Zhiqiang;  Yi, Jianqiang
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Hypersonic Vehicle  Output Feedback Control  Type-2 Fuzzy System  Linear Matrix Inequality  
Immersion and invariance adaptive control with sigma-modification for uncertain nonlinear systems 期刊论文
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2018, 卷号: 355, 期号: 5, 页码: 2091-2111
Authors:  Han, Chao;  Liu, Zhen;  Yi, Jianqiang
View  |  Adobe PDF(1921Kb)  |  Favorite  |  View/Download:152/38  |  Submit date:2018/06/01
Immersion And Invariance Adaptive  Σ-modification  Uncertain Nonlinear System  
Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 卷号: 26, 期号: 1, 页码: 56-71
Authors:  Li, Chengdong;  Gao, Junlong;  Yi, Jianqiang;  Zhang, Guiqing
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Data-driven Method  Fuzzy Inference System (Fis)  Least-squares Method  Parameter Learning  Single-input Rule Module (Sirm)  
Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction 期刊论文
ENERGIES, 2018, 卷号: 11, 期号: 1
Authors:  Li, Chengdong;  Ding, Zixiang;  Yi, Jianqiang;  Lv, Yisheng;  Zhang, Guiqing
Favorite  |  View/Download:62/0  |  Submit date:2018/10/10
Building Energy Consumption Prediction  Deep Belief Network  Contrastive Divergence Algorithm  Least Squares Learning  Energy-consuming Pattern  
Data driven hybrid fuzzy model for short-term traffic flow prediction 期刊论文
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 卷号: 35, 期号: 6, 页码: 6525-6536
Authors:  Li, Chengdong;  Yan, Bingyang;  Tang, Minjia;  Yi, Jianqiang;  Zhang, Xiqiao
Favorite  |  View/Download:7/0  |  Submit date:2019/07/12
Traffic flow prediction  fuzzy method  single input rule module  least square learning  traffic-flow pattern