Abstract

In this paper, a modular design approach of adaptive tracking is proposed for parameter-strict-feedback stochastic nonlinear systems with standard Wiener noises and constant unknown parameters. Both the adaptive Backstepping procedure and input-to-state stable(ISS) controller of global stabilization in probability are designed separately to ensure the output-feedback tracking can be achieved. According to Swapping technique, we develop two filters and convert dynamic parametric models into a static one to which the gradient update law is chosen. The transient performance shows the tracking error is bounded.

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