A swarm intelligence-based robust solution for Virtual Reference Feedback Tuning
| Autor: | Fiorio, L. V., Remes, C. L., de Novaes, Y. R. |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Popis: | This work proposes the inclusion of an $\mathcal{H}_{\infty}$ robustness constraint to the Virtual Reference Feedback Tuning (VRFT) cost function, which is solved by metaheuristic optimization with only a single batch of data (one-shot). The $\mathcal{H}_{\infty}$ norm of the sensitivity transfer function is estimated in a data-driven fashion, based on the regularized estimation of the system's impulse response. Four different swarm intelligence algorithms are chosen to be evaluated and compared at the optimization problem. Two real-world inspired examples are used to illustrate the proposed method through a Monte Carlo experiment with 50 runs. To compare the swarm intelligence algorithms to each other, 50 search agents have been adopted, with a maximum number of iterations of 100. 33 pages, 9 figures, journal |
| Databáze: | OpenAIRE |
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