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arXiv 2602.15215math.OC
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有限时间控制问题中HJB方程的线性误差界

Linear error bounds for HJB equations in finite horizon control problems

Sapienza, Università di Roma(罗马大学)

Alessandro Alla, Filippo Mayer

AI总结:

本文提出了一种改进的半拉格朗日近似方案,针对有限时间最优控制问题中HJB方程的线性误差界,消除了混合项并实现了更优的收敛性。

摘要:

我们研究了来自有限时间最优控制问题的Hamilton-Jacobi-Bellman方程的半拉格朗日近似方案。经典误差估计包含1/Δt项,导致悲观的收敛界,但在数值实验中并未观察到。在本工作中,我们在标准正则性假设下,即动态、运行成本和最终成本的正则性假设下,提供了改进的误差估计,假设存在正的折扣因子。新的界与时间步长、空间网格大小以及控制的时变振荡度成线性关系,从而消除了先前分析中出现的混合项。证明依赖于连续和离散成本函数的精细比较,以及受控动态的稳定性估计。数值实验确认了空间和时间上的第一阶收敛性,并表明改进的行为在无折扣情况下也持续存在。

英文摘要:

We study semi Lagrangian approximation schemes for Hamilton Jacobi Bellman equations arising from finite horizon optimal control problems. Classical error estimates for these schemes include the term $\frac{1}{Δt}$ which leads to pessimistic convergence bounds and is not observed in numerical experiments. In this work, we provide improved error estimates under standard regularity assumptions on the dynamics, the running cost, and the final cost, assuming the presence of a positive discount factor. The new bound depends linearly on the time step, the spatial mesh size, and a measure of the temporal oscillation of the control, thus removing the mixed term appearing in previous analyses. The proof relies on a refined comparison between continuous and discrete cost functionals and on stability estimates for the controlled dynamics. Numerical experiments confirm first-order convergence in both space and time and suggest that the improved behavior persists even in the undiscounted case.

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