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Mohamed-Amine Azzouz

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Aditya Mahajan
Sujets de recherche
Théorie de l'apprentissage automatique

Publications

Low-Dimensional solutions for optimal control of network-coupled subsystems over a directed network
Mohamed-Amine Azzouz
Shuang Gao
Aditya Mahajan
In this paper, we investigate optimal control of network-coupled subsystems, where the coupling between the dynamics of the subsystems is re… (voir plus)presented by the adjacency or Laplacian matrix of a directed graph. Under the assumption that the coupling matrix is normal and the cost coupling is compatible with the dynamics coupling, we use the spectral decomposition of the coupling matrix to decompose the overall system into at most n systems with noise coupled dynamics and decoupled cost, where n is the size of the network. Furthermore, the optimal control input at each subsystem can be computed by solving n1 decoupled Riccati equations where n1 (n1 ≤ n) denotes the number of distinct eigenvalues of the coupling matrix, where complex conjugate pairs are not double-counted. A salient feature of the result is that the solution complexity depends on the number of distinct eigenvalues of the coupling matrix rather than the size of the network. Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale network-coupled subsystems.
2024-12-31
CDC (publié)
doi.org