Learning-Based Adaptive Optimal Control for Flotation Processes Subject to Input Constraints-Reference-Cited by-同舟云学术

Learning-Based Adaptive Optimal Control for Flotation Processes Subject to Input Constraints

Published:2023-01 Issue:1 Volume:31 Page:252-264
ISSN:1063-6536
Container-title:IEEE Transactions on Control Systems Technology
language:
Short-container-title:IEEE Trans. Contr. Syst. Technol.

Author:

Li Zhongmei¹^ORCID,Huang Mengzhe²^ORCID,Zhu Jianyong³^ORCID,Gui Weihua⁴^ORCID,Jiang Zhong-Ping²^ORCID,Du Wenli¹^ORCID

Affiliation:

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

2. Tandon School of Engineering, New York University, Brooklyn, NY, USA

3. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China

4. School of Automation, Central South University, Changsha, China

Funder

National Natural Science Foundation of China through the Basic Science Center Program

National Science Fund for Distinguished Young Scholars

Shanghai Sailing Program

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/87/10001841/09771062.pdf?arnumber=9771062

Reference54 articles.

1. Combined fuzzy based feedforward and bubble size distribution based feedback control for reagent dosage in copper roughing process

2. Process identification using second order Volterra models for nonlinear model predictive control design of flotation circuits;delport,2005

3. On the optimization of froth flotation by the use of an artificial neural network

4. Model-Based Application of Fuzzy Control to a Class of Industrial Process Operation Systems With Uncertainty

5. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980,2014

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