| Autor: |
Yeong-Chan Chang, Hui-Min Yen, Kuang-Fen Han |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 10, Pp 31775-31787 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3160835 |
| Popis: |
The problem of the robust neural network-based model matching control is considered for a large class of uncertain immune systems. In order to achieve the purpose of therapeutic enhancement, it is essential to deal simultaneously with the effects of plant uncertainties, time-varying perturbations, and continuing environmental pathogens. Neural network control algorithm, robust $H_{\infty } $ control theory and VSC technique are combined to construct the hybrid adaptive/robust tracking control scheme such that the controlled immune system achieves a satisfactory model matching control performance. An adaptive neural network system is constructed to learn the behavior of the immune system dynamics. Moreover, an algebraic Riccati-like inequality must be solved to achieve a desired $H_{\infty } $ control performance. Consequently, the robust control scheme developed here can be analytically computed and easily implemented. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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