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Title: Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees
We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantees. While neural network-based nonlinear controllers have shown superior performance in various applications, their lack of provable guarantees has restricted their adoption in high-stake real-world applications. This paper bridges the gap between neural network-based controllers and the need for stabilization guarantees. Using equilibrium-independent passivity, a property present in a wide range of physical systems, we propose neural Proportional-Integral (PI) controllers that have provable guarantees of stability and zero steady-state output tracking error. The key structure is the strict monotonicity on proportional and integral terms, which is parameterized as gradients of strictly convex neural networks (SCNN). We construct SCNN with tunable softplus-β activations, which yields universal approximation capability and is also useful in incorporating communication constraints. In addition, the SCNNs serve as Lyapunov functions, giving us end-to-end performance guarantees. Experiments on traffic and power networks demonstrate that the proposed approach improves both transient and steady-state performances, while unstructured neural networks lead to unstable behaviors.  more » « less
Award ID(s):
2200692
PAR ID:
10493721
Author(s) / Creator(s):
Editor(s):
A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine
Publisher / Repository:
NeurIPS Proceedings
Date Published:
Journal Name:
Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
Volume:
36
Page Range / eLocation ID:
68434--68457
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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