GLOBALLY DECENTRALIZED ADAPTIVE BACKSTEPPING NEURAL NETWORK TRACKING CONTROL FOR UNKNOWN NONLINEAR INTERCONNECTED SYSTEMS

A globally stable decentralized adaptive backstepping neural network tracking control scheme is designed for a class of large-scale systems with mismatched interconnections. Under the assumption that the subsystems share the reference signals from the other subsystems, neural networks are used to approximate the unknown interconnections dependent on all reference signals such that the NN approximation domain can be determined a priori based on the bounds of reference signals. The proposed control approach can guarantee that all closed-loop signals are globally uniformly ultimately bounded and that the tracking errors converge to a small residual set around the origin.