| Recent years have witnessed a growing interest in modeling and control of complex systems, for their ubiquituous applications across engineering and science, including animal grouping, neural activity, kinesiology, telecommunication networks, and multivehicles teams.;In this dissertation, we focus on the control of complex systems modeled as networked dynamical systems. Specifically, we propose a class of pinning control techniques for effectively taming the dynamics of coupled dynamical systems onto a desired reference trajectory, by exerting control actions on selected network sites. To this aim, an exogenous system, acting as a master, exerts control signals on a limited number of the nodes of the slave network. The "pinned" agents share the control in put with their communicating neighbors, thus spreading the information in the whole network. The pinning control problem is converted into a stability problem for the dynamics of the error between the master and the slave systems. Partially averaging techniques, Lyapunov stability theory, and the Master Stability Function are integrated to develop effective and experimentally-validated pinning control strategies.;Moreover, towards extending such pinning control strategies to the control of collective behavior of animal groups, an equation-free modeling toolbox is developed based on real experimental data. Specifically, vision-based experimental data, from the observation of fish schooling in a planar environment, are used to explore collective motions of fish shoals. Experimental data are analyzed using the diffusion mapping technique, the main features of schooling are studied in terms of diffusion coordinates, and the onset of schooling is lumped into a global observable variable. |
