Flatness-based control of nonlinear complex networks

Abstract

Effective control of nonlinear complex networks needs to identify not only the intervention sites but also the state variables to be monitored and the locations at which control inputs should be applied. Here, we present a network-scale generalization of an observability-guided, flatness-based control framework. The resulting control laws are implemented using sliding-mode control and evaluated in heterogeneous networks of chaotic oscillators driven toward a prescribed periodic regime. We compare two control architectures: in the node-wise method, oscillators are independently sensed and actuated, while in the pairwise method, measurements acquired from a single node within each dyad are used to synthesize control inputs applied to both nodes. Across the parameter regimes considered, node-wise control generally achieves accurate control over a broader operating region, whereas pairwise control remains effective over a more restricted region while using half as many measurement locations for a given number of actuated nodes. Moreover, network interactions can enhance control by propagating the control influence to non-actuated nodes, creating a cooperative effect between the collective dynamics and the control action. Collectively, these findings quantify the trade-offs among measurement-location requirements, control performance, and control effort in nonlinear complex networks.