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Virtual reference feedback tuning with robustness constraints: A swarm intelligence solution.
- Source :
-
Engineering Applications of Artificial Intelligence . Aug2023:Part C, Vol. 123, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- The simplified modeling of a complex system allied with a low-order controller structure can lead to poor closed-loop performance and robustness. A feasible solution is to avoid the necessity of a model by using data for the controller design. The Virtual Reference Feedback Tuning (VRFT) is a data-driven design method that only requires a single batch of data and solves a reference tracking problem, although with no guarantee of robustness. In this work, the inclusion of an H ∞ robustness constraint to the VRFT cost function is addressed. The estimation of the H ∞ norm of the sensitivity transfer function is extended to maintain the one-shot characteristic of the VRFT. Swarm intelligence algorithms are used to solve the non-convex cost function. The proposed method is applied in two real-world inspired problems with four different swarm intelligence algorithms, which are compared with each other through a Monte Carlo experiment of 50 executions. The obtained results are satisfactory, achieving the desired robustness criteria. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SWARM intelligence
*COST functions
*TRANSFER functions
*ALGORITHMS
*ROBUST control
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 123
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 164285247
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106490