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 $\lambda$-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：在线广告中，预算调控对于在动态竞价环境下实现广告支出与营销目标的对齐至关重要。现有的调控方法通常依赖于临时参数调优，这种方式可能不稳定且效率低下。我们提出一种基于原理的控制器，将分桶滞后效应与比例反馈相结合，以实现稳定且自适应的支出控制。该方法提供了参数选择的框架与分析，能够精确追踪不同广告活动的预期支出速率。在实际竞价环境中的实验表明，相较于基线方法，本方法在调控精度和投放一致性方面均有显著提升：调控误差降低13%，$\lambda$-波动性减少54%。通过将控制理论与广告系统相结合，本方法为预算调控提供了可扩展且可靠的解决方案，尤其对小预算广告活动具有显著优势。