Decentralized adaptive dynamic surface control for a class of large-scale systems with unmodeled dynamics

Based on the approximation capability of neural networks, a decentralized adaptive dynamic surface control scheme is presented for a class of large-scale nonlinear interconnected systems with unmodeled dynamics. A dynamic signal is introduced to dominate the unmodeled dynamics in the scheme. The restrictions of disturbance and interconnections are relaxed by using the separation technique. By combining dynamic surface control with the backstepping method, such as the “explosion of complexity” problem caused by the repeated differentiations on virtual control in traditional backstepping design is effectively solved. Finally, the decentralized adaptive control of interconnected large-scale systems is realized. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded.