Disturbance compensation based model‐free adaptive tracking control for nonlinear systems with unknown disturbance

This paper considers a novel model‐free adaptive tracking control for nonlinear perturbed systems. A general dynamic linearization model with disturbance input is first developed for nonlinear systems based on the pseudo‐partial derivatives (PPDs) method and input/output (I/O) data. Due to the unknown disturbance input, an online disturbance estimation algorithm is proposed based on optimization by minimizing a cost function. Furthermore, an adaptive tracking control strategy with disturbance compensation is developed, such that the tracking error of nonlinear systems is convergent and closed‐loop systems are strongly robust. Finally, a comparing simulation is provided to demonstrate the effectiveness of the proposed approach.