Budget pacing is critical in online advertising to align spend with campaign goals under dynamic auctions. Existing pacing methods often rely on ad-hoc parameter tuning, which can be unstable and inefficient. We propose a principled controller that combines bucketized hysteresis with proportional feedback to provide stable and adaptive spend control. Our method provides a framework and analysis for parameter selection that enables accurate tracking of desired spend rates across campaigns. Experiments in real-world auctions demonstrate significant improvements in pacing accuracy and delivery consistency, reducing pacing error by 13% and $λ$-volatility by 54% compared to baseline method. By bridging control theory with advertising systems, our approach offers a scalable and reliable solution for budget pacing, with particular benefits for small-budget campaigns.

翻译：预算步进控制在在线广告中至关重要，它能在动态竞价环境下使广告支出与营销目标保持一致。现有的步进控制方法通常依赖于临时参数调整，这种方法可能不稳定且效率低下。我们提出了一种基于原理的控制器，该控制器将分桶滞后与比例反馈相结合，以实现稳定且自适应的支出控制。我们的方法提供了一个参数选择框架和分析方法，能够在不同营销活动中精确跟踪期望的支出速率。在实际竞价环境中的实验表明，与基线方法相比，该方法在步进控制准确性和投放一致性方面均有显著提升，步进误差降低了13%，$λ$-波动性降低了54%。通过将控制理论与广告系统相结合，我们的方法为预算步进控制提供了一个可扩展且可靠的解决方案，尤其对小预算营销活动具有显著优势。