{"ID":2833282,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03410","arxiv_id":"2512.03410","title":"Suboptimal Shrinking Horizon MPC with a Lower Hessian Condition Number from Adjustable Terminal Cost","abstract":"A strategy for reducing the number of iterations and computational burden in shrinking horizon Model Predictive Control (SH-MPC) when steering into a prescribed terminal set despite unmeasured disturbances is proposed. This strategy exploits dynamic adjustment of the terminal cost weight and horizon length while ensuring that the terminal set is reached within a desired number of steps. A lower Hessian condition number which facilitates the computational reduction is proved under assumptions, and an example of spacecraft nutation damping using the proposed approach is reported.","short_abstract":"A strategy for reducing the number of iterations and computational burden in shrinking horizon Model Predictive Control (SH-MPC) when steering into a prescribed terminal set despite unmeasured disturbances is proposed. This strategy exploits dynamic adjustment of the terminal cost weight and horizon length while ensuri...","url_abs":"https://arxiv.org/abs/2512.03410","url_pdf":"https://arxiv.org/pdf/2512.03410v1","authors":"[\"Steven van Leeuwen\",\"Ilya Kolmanovsky\"]","published":"2025-12-03T03:31:48Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}