Control Design of Thermoacoustic Generator Systems Using Reinforcement Learning Algorithm
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3394-3_9
Reference9 articles.
1. Do, T.T., Le, D.T., Dao, P.N., Bui, M.D., Dao, H.D.: Mathematical modelling of thermoacoustic generator systems and simulation studies. In: Sattler, K.U., Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H. (eds.) Advances in Engineering Research and Application. ICERA 2020. LNNS, vol. 178, pp. 534–540. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64719-3_59
2. Backhaus, S., Tward, E., Petach, M.: Traveling-wave thermoacoustic electric generator. Appl. Phys. Lett. 85(6), 1085–1087 (2004)
3. Dao, P.N., Nguyen, H.Q., Ngo, M.D., Ahn, S.J.: On stability of perturbed nonlinear switched systems with adaptive reinforcement learning. Energies 13(19), 5069 (2020)
4. Vu, V.T., Dao, P.N., Loc, P.T., Huy, T.Q.: Sliding variable-based online adaptive reinforcement learning of uncertain/disturbed nonlinear mechanical systems. J. Control Autom. Electr. Syst. 32(2), 281–290 (2021). https://doi.org/10.1007/s40313-020-00674-w
5. Dao, P.N., Liu, Y.-C.: Adaptive reinforcement learning strategy with sliding mode control for unknown and disturbed wheeled inverted pendulum. Int. J. Control Autom. Syst. 19(2), 1139–1150 (2020). https://doi.org/10.1007/s12555-019-0912-9
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