Decentralized state feedback adaptive tracking for a class of stochastic nonlinear large-scale systems

In this paper, decentralized adaptive tracking problem is studied for a class of stochastic nonlinear large-scale systems, which can be parameterized. The systems are transformed into parameterized strict-feedback nonlinear large-scale form through coordinate transformation at first. By employing the stochastic Lyapunov-like theorem and the backstepping design technique, the adaptive state feedback decentralized controller and parameters adaptive law are developed. The output of the closed-loop system is proven to follow the desired trajectory asymptotically in probability.