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arXiv 2402.18554eess.SYcs.SYeess.SP
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扩展卡尔曼滤波——Koopman算子用于可解的随机最优控制

Extended Kalman filter -- Koopman operator for tractable stochastic optimal control

Mohammad S. Ramadan, Mihai Anitescu

AI总结:

本文利用Koopman算子将随机最优控制问题重构为标准LQR问题,使双重控制可解,并通过数值示例验证其在变化可观测性系统中优于确定性等价控制。

摘要:

双重控制理论早在七十多年前就被提出。尽管它为控制、估计和系统辨识领域提供了丰富的见解,但双重控制通常在计算上是难以实现的。然而,近年来,Koopman算子理论在控制应用中的使用逐渐兴起。本文提出了一种随机最优控制问题的新重构方法,该方法利用Koopman算子,得到一个以双重控制为解的标准LQR问题。我们提供了一个数值示例,展示了与确定性等价控制相比,所提方法在应用于具有变化可观测性的系统时的有效性。

英文摘要:

The theory of dual control was introduced more than seven decades ago. Although it has provided rich insights to the fields of control, estimation, and system identification, dual control is generally computationally prohibitive. In recent years, however, the use of Koopman operator theory for control applications has been emerging. This paper presents a new reformulation of the stochastic optimal control problem that, employing the Koopman operator, yields a standard LQR problem with the dual control as its solution. We provide a numerical example that demonstrates the effectiveness of the proposed approach compared with certainty equivalence control, when applied to systems with varying observability.

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。

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