Composite adaptive dynamic surface control using online recorded data

This paper presents an online recorded data-based design of composite adaptive dynamic surface control for a class of uncertain parameter strict-feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed-loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time-interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed-loop system is rigorously established by the time-scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach. Copyright (c) 2016 John Wiley & Sons, Ltd.