期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3230200
关键词
3-D spin polarization control (3DSPC); adaptive dynamic programming (ADP); asymmetric input constraint; multicritic multiaction neural networks (MCMANNs); multiphysics; multiplayer nonzero-sum game (MP-NZSG); spin-exchange relaxation-free (SERF).
类别
资金
- Key Area Research and Development Program of Guangdong Province [2021B0101410005]
- National Natural Science Foundation of China [61673041]
This work solves the 3-D spin polarization control problem of atomic ensembles by utilizing multiphysics fields. A novel adaptive dynamic programming structure is proposed, along with a multicritic multiaction neural network, to solve the multiplayer nonzero-sum game problem. The proposed algorithm is validated through numerical simulations in a spin-exchange relaxation-free system.
This work is the first to solve the 3-D spin polarization control (3DSPC) problem of atomic ensembles, which controls the spin polarization to achieve arbitrary states with the cooperation of multiphysics fields. First, a novel adaptive dynamic programming (ADP) structure is proposed based on the developed multicritic multiaction neural network (MCMANN) structure with nonquadratic performance functions, as a way to solve the multiplayer nonzero-sum game (MP-NZSG) problem in 3DSPC under the constraints of asymmetric saturation inputs. Then, we utilize the MCMANNs to implement the multicritic multiaction ADP (MCMA-ADP) algorithm, whose convergence is proven by the compression mapping principle. Finally, the MCMA-ADP is deployed in the spin-exchange relaxation-free (SERF) system to provide a set of control laws in 3DSPC that fully exploits the multiphysics fields to achieve arbitrary spin polarization states. Numerical simulations support the theoretical results.
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