@article{xuf-yan-wan-liz-wan-22-aa-elbuff,
  author = {Fengjie Xu and Guolai Yang and Liqun Wang and Zixuan Li and Xiuye Wang},
  title = {A Robust Game Optimization for Electromagnetic Buffer under Parameters Uncertainty},
  journal = {Engineering with Computers},
  volume = {??},
  pages = {??-??},
  doi = {10.1007/s00366-021-01561-x},
  year = 2022,
  month = jan,
  comment = {Uses AA. Game theory for optimization??},
  abstract = {The resistance force variation of the electromagnetic buffer is its core performance. To reduce its peak values, this paper designs a segmented electromagnetic buffer scheme, and further proposes a novel optimization framework based on robust game theory considering the parameter uncertainty. The most challenging step of the robust Nash problem is to identify the worst-case scenario of the cost function under uncertainty. To attack this, an interval model is applied to describe the necessary uncertainty. Using the affine arithmetic, the uncertainty quantity is re-written into affine form; thus, the worst-case scenario of the cost function can be directly obtained. However, affine arithmetic can only deal with functions with explicit expressions. To address such deficiency, the Chebyshev polynomial expansion is formulated to convert the cost functions of the original electromagnetic buffer model into explicit expressions. The detailed assessment results demonstrate the effectiveness of the proposed approach for better resultant force performance; besides, it significantly improves the computational efficiency without compromising accuracy when solving the robust Nash equilibrium.},
}