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Article overview
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Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees | | Wenqi Cui
; Yan Jiang
; Baosen Zhang
; Yuanyuan Shi
; | | Date: |
1 Jun 2022 | | Abstract: | We study the control of networked systems with the goal of optimizing both
transient and steady-state performances while providing stability guarantees.
Linear Proportional-Integral (PI) controllers are almost always used in
practice, but the linear parameterization of the controller fundamentally
limits its performance. Learning-based approaches are becoming popular in
designing nonlinear controllers, but the lack of stability guarantees makes the
learned controllers difficult to apply in practical applications. This paper
bridges the gap between neural network-based controller design and the need for
stability guarantees. Using equilibrium-independent passivity, a property
present in a wide range of physical systems, we propose structured neural-PI
controllers that have provable guarantees on stability and zero steady-state
output tracking error. If communication between neighbours is available, we
further extend the controller to distributedly achieve optimal resource
allocation at the steady state. We explicitly characterize the stability
conditions and engineer neural networks that satisfy them by design.
Experiments on traffic and power networks demonstrate that the proposed
approach can improve both transient and steady-state performances compared to
existing state-of-the-art, while unstructured neural networks lead to unstable
behaviors. | | Source: | arXiv, 2206.00261 | | Services: | Forum | Review | PDF | Favorites | |
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