Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
This work addresses the challenge of ensuring guaranteed properties in recurrent neural networks for control applications, representing an incremental advance in structured learning methods.
The paper tackled the problem of learning control-oriented models for large-scale networked systems by proposing a physics-informed framework that preserves structural and stability properties, with simulation results demonstrating its effectiveness.
This paper proposes a physics-informed learning framework for a class of recurrent neural networks tailored to large-scale and networked systems. The approach aims to learn control-oriented models that preserve the structural and stability properties of the plant. The learning algorithm is formulated as a convex optimisation problem, allowing the inclusion of linear matrix inequality constraints to enforce desired system features. Furthermore, when the plant exhibits structural modularity, the resulting optimisation problem can be parallelised, requiring communication only among neighbouring subsystems. Simulation results show the effectiveness of the proposed approach.