Abstract
In this paper, characteristic model-based modeling and control approaches for complex dynamical networks based on sampled data are studied. It shows that the characteristic model, in which underline network topological structures are simplified, can provide a straightforward and implicit description for network dynamics. The induced parameter estimation method can further make the model adaptive and purely data-driven. Moreover, a control law based on this model is also proposed to govern the network dynamics. Finally, the theoretical results are verified through numerical simulations of modeling and stabilizing a dynamical network.
Original language | English |
---|---|
Article number | 8790993 |
Pages (from-to) | 3599-3607 |
Number of pages | 9 |
Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
Volume | 51 |
Issue number | 6 |
DOIs | |
Publication status | Published - Jun 2021 |
Externally published | Yes |
Keywords
- Adaptive control
- complex networks
- model reduction
- signal sampling