Open access
Datum
2022Typ
- Conference Paper
ETH Bibliographie
yes
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Abstract
We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all available information about the time-varying network state and dynamics to optimize a performance criterion. We propose a general solution structure analogous to the classical self-tuning regulator from adaptive control. We show that a special case with full-state feedback can be solved in principle with dynamic programming, and in the linear quadratic setting the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for joint architecture-policy design. We demonstrate in numerical experiments that self-tuning architectures can provide dramatically improved performance over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000610071Publikationsstatus
publishedExterne Links
Buchtitel
2022 IEEE 61st Conference on Decision and Control (CDC)Seiten / Artikelnummer
Verlag
IEEEKonferenz
Organisationseinheit
08814 - Smith, Roy (Tit.-Prof.)
03751 - Lygeros, John / Lygeros, John
Zugehörige Publikationen und Daten
Is supplemented by: https://doi.org/10.3929/ethz-b-000536276
ETH Bibliographie
yes
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