Composite Adaptive Control of Nonlinear Systems Using Dynamic Regressor Extension and Mixing: A Fas Approach

In this paper, a novel composite adaptive control strategy is proposed for multi-order non-affine fully actuated systems based on the dynamic regressor extension and mixing technique. By properly choosing stable linear time-invariant filters and mixing the filtered data, an alternative linear regression equation is constructed for the estimator design. To enhance parameter convergence and transient response, a composite adaptive controller integrating both tracking and prediction errors is developed to achieve asymptotic tracking and improve transient performance. Comparative simulations on a permanent magnet stepper motor verify the effectiveness and superiority of the presented controller in terms of overall tracking performance.