李东娟,女,1979 年生,辽宁葫芦岛人,硕士,教授,硕士生导师。2007年毕业于大连工业大学化学工艺专业,获工学硕士学位,同年到辽宁工业大学化学与化境工程学院任教。《澳门线上赌场》等期刊审稿人。
长期从事化工智能控制相关的研究工作。近年来,围绕非线性系统的自适应神经网络控制及其应用进行了系统研究,且取得了一定的研究成果。先后主持国家自然科学青年基金项目1项,辽宁省教育厅一般项目2项;并作为主要成员参与国家自然科学基金面上项目2项,国家自然科学基金青年基金项目2项。在 《 IEEE Transactions on Systems, Man, and Cybernetics: Systems 》、《IEEE Access 》、《澳门线上赌场》、《澳门线上赌场》等国内外权威期刊以第一作者发表学术论文共24篇,其中SCI检索论文17篇,EI检索论文7篇。
Ø 教育及工作经历
1999.09-2003.07 沈阳工业大学 应用化学专业 工学学士
2004.09-2007.04 大连工业大学 化学工艺专业 工学硕士
2007.04~今 辽宁工业大学 澳门线上赌场 教师
Ø 研究方向
1. 非线性系统的神经网络控制
2. 连续搅拌反应釜系统的智能控制
3. 化学反应釜中时滞问题的智能约束控制
Ø 科研课题
1. 国家自然科学基金青年基金项目,61603164,具有输出和状态约束非线性系统的自适应神经网络控制及其应用,2017.01-2019.12,21万,主持。
2. 辽宁省教育厅一般项目, L2013243, 连续搅拌反应釜系统的智能控制算法设计与分析,2013.07-2015.12, 3万元,主持。
3. 辽宁省教育厅一般项目,JJL202015406,时滞系统的智能约束控制及其在化学反应釜中的应用,2020.06-2022.06, 3万元,主持。
4. 国家自然科学基金面上项目, 61973147,不确定非线性偏微分系统的智能自适应边界控制, 2015.01-2018.12,63万元,第二参与人。
5. 国家自然科学基金面上项目,61473139,不确定非线性三角结构系统的自适应优化控制,2020.01-2024.12,80万元,第二参与人。
Ø 代表性科研成果(第一作者发表的SCI检索论文)
1. Dongjuan Li, Dongxing Wang, and Ying Gao*, Adaptive neural control and modeling for continuous stirred tank reactor with delays and full state constraints, Complexity, 2021, Article ID 9948044, 12 pages.
2. Dongjuan Li, Dongxing Wang, Lei Liu*, and Ying Gao, Adaptive finite-time tracking control for continuous stirred tank reactor with time-varying output constraint, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(9): 5929-5934.
3. Dongjuan Li, Shuming Lu, and Lei Liu*, Adaptive NN cross backstepping control for nonlinear systems with partial time-varying state constraints and applications to hyper-chaotic systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(5): 2821-2832.
4. Dongjuan Li * and Dapeng Li, Adaptive control via neural output feedback for a class of nonlinear discrete-time systems in a nested interconnected form, IEEE Transactions on Cybernetics, 2018, 48(9): 2633-2642.
5. Dongjuan Li, Shumin Lu, Yan-Jun Liu* and Dapeng Li, Adaptive fuzzy tracking control-based Barrier Functions of uncertain nonlinear MIMO systems with full state constraints and applications to chemical process, IEEE Transactions on Fuzzy Systems, 2018, 26(4): 2145-2159.
6. Dongjuan Li and Dapeng Li*, Adaptive tracking control for nonlinear time-varying delay systems with full state constraints and unknown control coefficients, Automatica, 2018, 93: 444-453.
7. Dongjuan Li, Jing Li and Shu Li, Adaptive Control of Nonlinear Systems with Full State Constraints Using Integral Barrier Lyapunov Functionals, Neurocomputing, 2016, 186: 90-96.
8. Dongjuan Li, Adaptive neural stabilization control for unified chaotic systems with full state constraints, Journal of Vibration and Control, 2016, 22(1): 121-128.
9. Dongjuan Li*, Adaptive neural network control for unified chaotic systems with dead-zone input, Journal of Vibration and Control, 2015, 21(12): 2446-2451.
10. Dongjuan Li*, Adaptive neural network control for a two continuously stirred tank reactor with output constraints , Neurocomputing, 2015, 167: 451-458.
11. Dongjuan Li* and Dapeng Li, Adaptive Controller Design-Based Neural Networks for Output Constraint Continuous Stirred Tank Reactor, Neurocomputing, 2015, 153: 159-163.
12. Dongjuan Li*, Adaptive Neural Network Control for a Class of Continuous Stirred Tank Reactor Systems, Science China Information Sciences, 2014, 57(10): 1-8.
13. Dongjuan Li* and Li Tang, Adaptive Control for a Class of Chemical Reactor Systems in Discrete-Time Form, Neural Computing & Applications, 2014, 24(6): 1807-1814.
14. Dongjuan Li*, Neural network control for a class of continuous stirred tank reactor process with dead-zone input, Neurocomputing, 2014, 131: 453-459.
15. D. J. Li, J. Zhang, Y. Cui, L. Liu, Intelligent Control of Nonlinear Systems with Application to Chemical Reactor Recycle, Neural Computing & Applications, 2013, 23(5): 1495-1502.
16. D. J. Li, L. Tang, Y. J. Liu, Adaptive intelligence learning for nonlinear chaotic systems, Nonlinear Dynamics, 2013, 73(4): 2103-2109.
17. D. J. Li, Adaptive output feedback control of uncertain nonlinear chaotic systems based on dynamic surface control technique, Nonlinear Dynamics, 2012, 68(1-2): 235-243.
Ø 研究生招生
招收对化工过程智能控制有浓厚兴趣,拥有较强编程能力和实验能力,具有化学工程与工艺、 应用化学、自动化等专业背景的本科毕业生。
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