极值寻优控制：三次变革与未来之路
Extremum Seeking Control: Three Revolutions and the Road Ahead
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中文总结 AI 辅助
本文梳理极值寻优控制（ESC）的三次科学变革，指出其因核心挑战未变而保持生命力，有望推动新一代智能自主系统发展，或迎来第四次变革。
中文摘要 AI 辅助
极值寻优的历史不仅是一种算法的历史，更是一种理念的历史。在控制工程领域，极少有理念能展现出极值寻优控制（Extremum Seeking Control，ESC）这般非凡的生命力。ESC 诞生于一百多年前，在始终坚守核心目标——让系统无需依赖精确数学模型即可优化自身性能的同时，不断实现自我革新。本文提出，ESC 的演化历程可通过三次科学变革得到最佳阐释：第一次是工程变革（1922-1999），确立了无模型优化的工程原理；第二次是数学变革（2000-2010），提供了严谨的数学基础，使 ESC 发展为成熟的非线性控制学科；第三次是正在进行的无限维与信息物理变革（2010-至今），持续将其应用范围拓展至延迟、偏微分方程、分布式优化、事件触发实现以及日益复杂的信息物理系统。除梳理这一历史演化外，本文还从个人视角阐述了 ESC 在 successive 技术时代始终保持相关性的原因，认为其持久影响力源于核心工程挑战从未改变：“当最优值未知时，动力系统如何学会提升自身性能？”随着优化、学习与反馈控制的联系日益紧密，ESC 似乎具备独特优势，能为新一代智能自主系统作出贡献，预示着该领域可能迎来第四次科学变革。
英文摘要
The history of extremum seeking is not merely the history of an algorithm; it is the history of an idea. Few ideas in control engineering have demonstrated the remarkable longevity of extremum seeking control (ESC). Invented more than one hundred years ago, ESC has continually reinvented itself while remaining faithful to its original objective: enabling systems to optimize their performance without relying on accurate mathematical models. This perspective article proposes that the evolution of ESC is best understood through three scientific revolutions. The first Engineering Revolution (1922-1999) established the engineering principles of model-free optimization; the second Mathematical Revolution (2000-2010) provided the rigorous mathematical foundations that transformed ESC into a mature discipline of nonlinear control; and the third ongoing Infinite-Dimensional and Cyber-Physical Revolution (2010-present) continues to expand its scope toward delays, partial differential equations, distributed optimization, event-triggered implementations, and increasingly complex cyber-physical systems. Beyond recounting this historical evolution, we offer a personal perspective on why ESC has remained relevant across successive technological eras. We argue that its enduring influence arises because the fundamental engineering challenge has never changed: "how can a dynamical system learn to improve its own performance when the optimum is unknown?" As optimization, learning, and feedback control become increasingly intertwined, ESC appears uniquely positioned to contribute to a new generation of intelligent autonomous systems, pointing toward what may become the field's fourth scientific revolution.
发表机构
- State University of Rio de Janeiro（里约热内卢州立大学）
机构由 AI 辅助整理，请以论文原文为准。
