Particle swarm optimisation in nonlinear model predictive control; comprehensive simulation study for two selected problems-Reference-Cited by-同舟云学术

Particle swarm optimisation in nonlinear model predictive control; comprehensive simulation study for two selected problems

Published:2020-02-19 Issue:10 Volume:94 Page:2623-2639
ISSN:0020-7179
Container-title:International Journal of Control
language:en
Short-container-title:International Journal of Control

Author:

Zietkiewicz Joanna¹^ORCID,Kozierski Piotr¹²,Giernacki Wojciech¹

Affiliation:

1. Faculty of Electrical Engineering, Institute of Control, Robotics and Information Engineering, Poznan University of Technology, Poznan, Poland

2. Faculty of Computing, Institute of Automation and Robotics, Division of Signal Processing and Electronic Systems, Poznan University of Technology, Poznan, Poland

Publisher

Informa UK Limited

Subject

Computer Science Applications,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207179.2020.1727957

Reference51 articles.

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2. Beckmann, D., Dagen, M. & Ortmaier, T. (2018). A comparison of discretization methods for parameter estimation of nonlinear mechanical systems using extended Kalman filter: Symplectic versus classical approaches. In K. Madani, D. Peaucelle, & O. Gusikhin (Eds.), Informatics in control, automation and robotics. Lecture notes in electrical engineering, 430, 367–384. Cham: Springer.

3. Rain water algorithm: Newton’s law of rain water movements during free fall and uniformly accelerated motion utilization

4. Efficient nonlinear model predictive control algorithms

5. Chen, H., Kremling, A. & Algöwer, F. (1995). Nonlinear predictive control of a benchmark CSTR. In Proceedings of 3rd european control conference, 3247–3252, Rome, Italy.

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