Finite-Time Adaptive Control for Non-Strict Feedback Stochastic Nonlinear Systems

The problem of finite time adaptive control for stochastic nonlinear system is studied in this paper, where the system has a non-strict feedback structure. Under the sense of finite time stability, the authors propose a new neural network (NN) adaptive controller for stochastic nonlinear systems by backstepping technique. To overcome the difficulties that arise from the non-strict feedback structure of system, a key lemma is introduced. All the signals of the closed-loop systems are bounded in finite time in probability under the adaptive controller. At the same time good tracking performance can be achieved. A simulation example further shows the effectiveness of the control strategy in this thesis.