A Semi-Algebraic Optimization Approach to Data-Driven Control of Continuous-Time Nonlinear Systems

IEEE Control Systems Letters ◽

10.1109/lcsys.2020.3003505 ◽

2021 ◽

Vol 5 (2) ◽

pp. 487-492

Author(s):

T. Dai ◽

M. Sznaier

Keyword(s):

Nonlinear Systems ◽

Continuous Time ◽

Data Driven ◽

Optimization Approach ◽

Algebraic Optimization

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Data-Driven Finite-Horizon $H_{∞}$ Tracking Control With Event-Triggered Mechanism for the Continuous-Time Nonlinear Systems

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2021.3116464 ◽

2021 ◽

pp. 1-15

Author(s):

Huaguang Zhang ◽

Zhongyang Ming ◽

Yuqing Yan ◽

Wei Wang

Keyword(s):

Nonlinear Systems ◽

Continuous Time ◽

Tracking Control ◽

Finite Horizon ◽

Data Driven ◽

Event Triggered

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Data-Driven Tracking Control With Adaptive Dynamic Programming for a Class of Continuous-Time Nonlinear Systems

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2016.2548941 ◽

2017 ◽

Vol 47 (6) ◽

pp. 1460-1470 ◽

Author(s):

Chaoxu Mu ◽

Zhen Ni ◽

Changyin Sun ◽

Haibo He

Keyword(s):

Dynamic Programming ◽

Nonlinear Systems ◽

Continuous Time ◽

Tracking Control ◽

Adaptive Dynamic Programming ◽

Data Driven ◽

Adaptive Dynamic

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Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2015.2464080 ◽

2016 ◽

Vol 27 (2) ◽

pp. 444-458 ◽

Author(s):

Qinglai Wei ◽

Ruizhuo Song ◽

Pengfei Yan

Keyword(s):

Optimal Control ◽

Nonlinear Systems ◽

Continuous Time ◽

Data Driven ◽

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Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.134.1708 ◽

2014 ◽

Vol 134 (11) ◽

pp. 1708-1715

Author(s):

Tomohiro Hachino ◽

Kazuhiro Matsushita ◽

Hitoshi Takata ◽

Seiji Fukushima ◽

Yasutaka Igarashi

Keyword(s):

Nonlinear Systems ◽

Gaussian Process ◽

Continuous Time ◽

Process Models ◽

Gaussian Process Models

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Design of observers for continuous-time nonlinear systems using neural networks

Proceedings of the 2004 American Control Conference ◽

10.23919/acc.2004.1383829 ◽

2004 ◽

Author(s):

A. Alessandri ◽

C. Cervellera ◽

A.F. Grassia ◽

M. Sanguineti

Keyword(s):

Neural Networks ◽

Nonlinear Systems ◽

Continuous Time

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Approximate Optimal Tracking Control of Nondifferentiable Signals for a Class of Continuous-Time Nonlinear Systems

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2020.3027344 ◽

2020 ◽

pp. 1-10

Author(s):

Yue Fu ◽

Chengwen Hong ◽

Jun Fu ◽

Tianyou Chai

Keyword(s):

Nonlinear Systems ◽

Continuous Time ◽

Tracking Control ◽

Optimal Tracking ◽

Optimal Tracking Control

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Quantisation compensated data-driven iterative learning control for nonlinear systems

International Journal of Systems Science ◽

10.1080/00207721.2021.1950232 ◽

2021 ◽

pp. 1-16

Author(s):

Huimin Zhang ◽

Ronghu Chi ◽

Zhongsheng Hou ◽

Biao Huang

Keyword(s):

Nonlinear Systems ◽

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

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Continuous-time synthesizing robust sampled-data dynamic output-feedback controllers for uncertain nonlinear systems in Takagi–Sugeno form: A descriptor representation approach

Information Sciences ◽

10.1016/j.ins.2021.02.032 ◽

2021 ◽

Author(s):

Jaejun Lee ◽

Ji Hyun Moon ◽

Ho Jae Lee

Keyword(s):

Nonlinear Systems ◽

Output Feedback ◽

Continuous Time ◽

Dynamic Output Feedback ◽

Uncertain Nonlinear Systems ◽

Sampled Data ◽

Feedback Controllers ◽

Dynamic Output ◽

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Reinforcement Learning and Adaptive Optimal Control for Continuous-Time Nonlinear Systems: A Value Iteration Approach

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2020.3045087 ◽

2021 ◽

pp. 1-10

Author(s):

Tao Bian ◽

Zhong-Ping Jiang

Keyword(s):

Optimal Control ◽

Reinforcement Learning ◽

Nonlinear Systems ◽

Continuous Time ◽

Value Iteration ◽

Adaptive Optimal Control ◽

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New York State's 100% renewable electricity transition planning under uncertainty using a data-driven multistage adaptive robust optimization approach with machine-learning

Advances in Applied Energy ◽

10.1016/j.adapen.2021.100019 ◽

2021 ◽

pp. 100019

Author(s):

Ning Zhao ◽

Fengqi You

Keyword(s):

Machine Learning ◽

New York ◽

Robust Optimization ◽

Transition Planning ◽

Data Driven ◽

Optimization Approach ◽

Planning Under Uncertainty ◽

Renewable Electricity

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