Discrete-time multivariable PID controller design with application to an overhead crane-Reference-Cited by-同舟云学术

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Discrete-time multivariable PID controller design with application to an overhead crane

Published:2020-08-04 Issue:14 Volume:51 Page:2733-2745
ISSN:0020-7721
Container-title:International Journal of Systems Science
language:en
Short-container-title:International Journal of Systems Science

Author:

Guo Huiru¹²,Feng Zhi-Yong¹²,She Jinhua³

Affiliation:

1. Hubei Key Laboratory of Advanced Technology of Automotive Parts, Wuhan University of Technology, Wuhan, People's Republic of China

2. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, People's Republic of China

3. School of Computer Science, Tokyo University of Technology, Hachioji, Tokyo, Japan

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207721.2020.1801881

Reference29 articles.

1. Multivariable PID control with set-point weighting via BMI optimisation

2. NP-Hardness of Some Linear Control Design Problems

3. MIMO PID tuning via iterated LMI restriction

4. New Results on Output Feedback $H_{\infty} $ Control for Linear Discrete-Time Systems

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1. Adaptive coupled double-pendulum overhead crane control strategy with enhanced attitude suppression under initial input constraints;Journal of the Brazilian Society of Mechanical Sciences and Engineering;2024-08-01

2. On the PID-structured model-free adaptive control: a comparison of different approaches;2024 European Control Conference (ECC);2024-06-25

3. A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach;International Journal of Systems Science;2024-06-13

4. Modeling and anti-sway control method for vertical lift-up/lay-down process of slender-beam payload;ISA Transactions;2024-06

5. Gaussian Reinforcement Learning: Optimal Tracking Control for Uncertain Linear Systems;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

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