Learning Impulsive Pinning Control of Complex Networks

In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and neural network as identifier, to synthesize learning law. The model of the used in analysis has unknown self-dynamics, connections between nodes, where dynamics add feedback input only pinned nodes. proposed controller consists linearization reorder resulting quadratic Lyapunov function Rayleigh quotient. part is done recurrent high order identification which trained extended Kalman filter algorithm. A numerical simulation included illustrate behavior system under developed controller. For simulation, 20-node 5 used. discretized versions well-known continuous chaotic attractors.