次优收缩时间域MPC与可调终端成本下的下界Hessian条件数
Suboptimal Shrinking Horizon MPC with a Lower Hessian Condition Number from Adjustable Terminal Cost
AI总结：
本文提出一种通过动态调整终端成本和时间域长度，以降低Hessian条件数来减少SH-MPC计算负担的方法，并展示了其在航天器摇头阻尼中的应用。
摘要：
本文提出了一种减少收缩时间域模型预测控制（SH-MPC）迭代次数和计算负担的策略，该策略在面对未测量扰动时仍能引导进入指定终端集。该策略利用终端成本权重和时间域长度的动态调整，确保在期望的步数内达到终端集。在假设条件下，证明了下界Hessian条件数有助于计算减少，并报告了使用该方法进行航天器摇头阻尼的示例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
