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arXiv 2004.06407eess.SYcs.SY
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具有输入输出约束的非凸反馈优化

Non-convex Feedback Optimization with Input and Output Constraints

Verena Häberle, Adrian Hauswirth, Lukas Ortmann, Saverio Bolognani, Florian Dörfler

摘要:

在本文中,我们提出了一种新的控制方案的反馈优化。也就是说,我们提出了一个离散时间控制器,可以引导一个物理工厂的稳定状态的约束优化问题的解决方案,而无需数值求解问题。我们的控制器可以被解释为连续时间投影梯度流的离散化。与用于反馈优化的其他方案(如鞍点流或不精确惩罚方法)相比,我们的算法结合了几个理想的特性:它渐进地对工厂稳态输出施加约束,并且可以很容易地量化临时约束违反。我们的算法只需要减少模型信息的形式,稳态输入输出灵敏度的工厂。此外,正如我们在本文中证明,全局收敛性是有保证的,即使是非凸问题。最后,我们的算法是简单的调整,因为步长是唯一的调整参数。

英文摘要:

In this paper, we present a novel control scheme for feedback optimization. That is, we propose a discrete-time controller that can steer the steady state of a physical plant to the solution of a constrained optimization problem without numerically solving the problem. Our controller can be interpreted as a discretization of a continuous-time projected gradient flow. Compared to other schemes used for feedback optimization, such as saddle-point flows or inexact penalty methods, our algorithm combines several desirable properties: It asymptotically enforces constraints on the plant steady-state outputs, and temporary constraint violations can be easily quantified. Our algorithm requires only reduced model information in the form of steady-state input-output sensitivities of the plant. Further, as we prove in this paper, global convergence is guaranteed even for non-convex problems. Finally, our algorithm is straightforward to tune, since the step-size is the only tuning parameter.

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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