Tracking control of complex dynamical networks via reservoir computing
Guyue Wu, Longkun Tang, Wei Zhao · Chaos An Interdisciplinary Journal of Nonlinear Science · 2026
Since rich collective dynamical behaviors tend to emerge in networked systems with various interactions between nodes, tracking control for networked coupled systems is far more complicated than that for a single system. In this work, the data-driven tracking control for the single system is extended to the case for networked systems via the control scheme of reservoir computing (RC), and we develop a RC-based control technique for complex dynamical networks, which can control the network of homogeneous nodes, as well as of heterogeneous nodes, into any desired trajectory even for partially observable node states. Numerous simulations on man-made and real networks show that the proposed control scheme is feasible and effective for different network structures and coupling strengths and has good robustness against measurement noise.