无需模型即可快速定位未知信号的极值点，适用于复杂非二次函数。
Model-free source seeking of exponentially convergent unicycle: theoretical and robotic experimental results
- 基于实时测量设计无模型控制算法，引导机器人向信号极值移动。
- 在四次多项式类信号下实现指数收敛，优于传统二次函数假设。
- 首次在真实机器人上验证，支持噪声和延迟下的稳定性能。
本文提出一种新型无模型、实时的基于全向轮机器人的源寻优设计。该设计能自主将动态系统导向一个表达式未知但可通过测量获取的标量信号或目标函数的极值点。核心贡献在于，所提方法在目标函数局部表现为高阶幂函数（如四次多项式）时，仍可实现指数收敛，突破了文献中通常假设局部二次性的限制。论文提供了理论分析与设计特性描述，并通过多种仿真验证了其鲁棒性，涵盖不同初始条件及存在测量延迟与噪声的情况。此外，首次在文献中展示实验机器人结果，证实该指数收敛的寻优设计可在实际机器人平台上实现，满足真实世界传感与执行约束。
原文摘要 · Abstract (English)
This paper introduces a novel model-free, real-time unicycle-based source seeking design. This design autonomously steers the unicycle dynamic system towards the extremum point of an objective function or physical/scalar signal that is unknown expression-wise, but accessible via measurements. A key contribution of this paper is that the introduced design converges exponentially to the extremum point of objective functions (or scalar signals) that behave locally like a higher-degree power function (e.g., fourth-degree polynomial function) as opposed to locally quadratic objective functions, the usual case in literature. We provide theoretical results and design characterization, supported by a variety of simulation results that demonstrate the robustness of the proposed design, including cases with different initial conditions and measurement delays/noise. Also, for the first time in the literature, we provide experimental robotic results that demonstrate the effectiveness of the proposed design and its exponential convergence ability. These experimental results confirm that the proposed exponentially convergent extremum seeking design can be practically realized on a physical robotic platform under real-world sensing and actuation constraints.
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