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arXiv 2103.10482math.OC
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学习一个半全局镇定半线性抛物型方程的最优反馈算子

Learning an optimal feedback operator semiglobally stabilizing semilinear parabolic equations

Karl Kunisch, Sérgio S. Rodrigues, Daniel Walter

AI总结:

本文提出仅依赖状态有限维投影的单调反馈算子,在特定条件下实现对半线性抛物型方程的半全局镇定,并利用深度神经网络求解最小化二次能量代价的最优反馈控制。

摘要:

提出了仅依赖于状态到有限维控制空间上的正交投影的镇定反馈算子。考虑了一类将有限维控制空间映射到自身的单调反馈算子,其中包括比例单位算子作为特例。给出了关于执行器集合和单调性幅度的条件,这些条件保证了反馈对于一类半线性抛物型方程具有半全局镇定性质。随后,利用反馈仅依赖于状态的有限维分量这一事实,通过深度神经网络计算了最小化二次能量代价的最优反馈控制。数值仿真展示了显式比例正交投影反馈和深度神经网络反馈的镇定性能。

英文摘要:

Stabilizing feedback operators are presented which depend only on the orthogonal projection of the state onto the finite-dimensional control space. A class of monotone feedback operators mapping the finite-dimensional control space into itself is considered. The special case of the scaled identity operator is included. Conditions are given on the set of actuators and on the magnitude of the monotonicity, which guarantee the semiglobal stabilizing property of the feedback for a class semilinear parabolic-like equations. Subsequently an optimal feedback control minimizing the quadratic energy cost is computed by a deep neural network, exploiting the fact that the feedback depends only on a finite dimensional component of the state. Numerical simulations demonstrate the stabilizing performance of explicitly scaled orthogonal projection feedbacks, and of deep neural network feedbacks.

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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