SDRE based optimal finite-time tracking control of a multi-motor driving system-Reference-Cited by-同舟云学术

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SDRE based optimal finite-time tracking control of a multi-motor driving system

Published:2020-01-31 Issue:9 Volume:94 Page:2551-2563
ISSN:0020-7179
Container-title:International Journal of Control
language:en
Short-container-title:International Journal of Control

Author:

Wang Minlin¹,Dong Xueming¹,Ren Xuemei²,Chen Qiang³

Affiliation:

1. Department of Inertia, Changcheng Institute of Metrology & Measurement, Beijing, People's Republic of China

2. School of Automation, Beijing Institute of Technology, Beijing, People's Republic of China

3. College of Information Engineering, Zhejiang University of Technology, Hangzhou, People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Computer Science Applications,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207179.2020.1717632

Reference40 articles.

1. Nonlinear feedback controllers and compensators: a state-dependent Riccati equation approach

2. Nonlinear Attitude Control of a Tether-Connected Multi-Satellite in Three-Dimensional Space

3. On the Redundant-Drive Backlash-Free Robotic Mechanisms

4. Adaptive Nonsingular Fixed-Time Attitude Stabilization of Uncertain Spacecraft

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1. Finite-time funnel synchronization control based on distributed observer for multi-motor driving systems with input saturation;Physica Scripta;2024-07-25

2. Hybrid Flapping and Gliding Flight for Robot Bird Using Closed-Loop Nonlinear Optimal Control: Indoors Experimentation;2024 International Conference on Unmanned Aircraft Systems (ICUAS);2024-06-04

3. Neural Adaptive Funnel Synchronization Control of Multi-motor Driving Systems;2024 36th Chinese Control and Decision Conference (CCDC);2024-05-25

4. Input/output data-based adaptive super-twisting sliding mode control for multi-motor servo systems;Nonlinear Dynamics;2024-05-08

5. Finite-time model-free robust synchronous control of multi-lift overhead cranes based on iterative learning;Transactions of the Institute of Measurement and Control;2024-03-16

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