发表机构
Delft Center of Systems and Control, TU Delft（代尔夫特系统与控制中心，代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对稳态映射灵敏度计算昂贵的问题，提出混合序贯反馈优化，结合RLS或ESC估计修正梯度，在压缩性假设下保持收敛，并在风电场功率最大化中改善早期瞬态性能。
AI 中文摘要
本文研究了当稳态输入-输出映射及其灵敏度计算代价高昂时，非线性离散时间系统最优稳态运行的反馈优化问题。我们提出了一种混合扩展的序贯反馈优化（SFO）方法，通过凸组合与可求和递减权重，将基于模型的SFO梯度与修正项相结合。研究了两种变体：一种基于递归最小二乘（RLS）灵敏度估计，另一种基于极值搜索控制（ESC）梯度估计。在压缩性和光滑性假设下，我们证明了两种混合方案都保持了SFO收敛到最优稳态邻域的性质。通过使用中等保真度模型的风电场功率最大化问题验证了所提方法，与纯SFO相比，展示了改进的早期瞬态性能。
英文摘要
This paper considers feedback optimization for optimal steady-state operation of nonlinear discrete-time systems when the steady-state input-output map and its sensitivity are expensive to compute. We propose a hybrid extension of sequential feedback optimization (SFO) that augments the model-based SFO gradient with correction terms through a convex combination with summable diminishing weights. Two variants are studied: one based on recursive least-squares (RLS) sensitivity estimation, and another on extremum seeking control (ESC) gradient estimation. Under contractivity and smoothness assumptions, we show that both hybrid schemes preserve the convergence of SFO to a neighborhood of the optimal steady state. The proposed methods are validated through a wind farm power maximization problem using a medium-fidelity model, demonstrating improved early transient performance compared to pure SFO.
CommentsAccepted to the 65th IEEE Conference on Decision and Control (CDC 2026)
