面向非随机扰动的定向探索
Towards targeted exploration for non-stochastic disturbances
AI总结：
本文提出一种针对线性时不变系统的非随机定向探索策略，利用数据相关不确定性界和半定规划计算最优正弦输入，确保参数估计误差界，并通过数值示例对比了传统方法。
摘要：
我们提出了一种针对线性时不变系统的新型定向探索策略，该策略无需对噪声作随机假设，即不要求独立性或零均值，允许确定性的模型失配。本工作利用了在能量有界噪声存在时，最小二乘参数估计的经典数据相关不确定性界。我们给出了关于探索数据的充分条件，以确保估计参数达到期望的误差界。利用常见近似，我们推导出一个半定规划来计算最优正弦输入激励。最后，我们通过数值示例，阐明了所开发的非随机定向探索策略与基于经典辨识界的传统探索策略之间的异同。
英文摘要：
We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i.e., without requiring independence or zero mean, allowing for deterministic model misspecifications. This work utilizes classical data-dependent uncertainty bounds on the least-squares parameter estimates in the presence of energy-bounded noise. We provide a sufficient condition on the exploration data that ensures a desired error bound on the estimated parameter. Using common approximations, we derive a semidefinite program to compute the optimal sinusoidal input excitation. Finally, we highlight the differences and commonalities between the developed non-stochastic targeted exploration strategy and conventional exploration strategies based on classical identification bounds through a numerical example.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
