The tracking control problem for strict-feedback systems with unknown dynamics has been extensively studied. However, most existing control approaches require online approximation models and associated a priori assumptions. In order to avoid the necessity of deriving online models, this article proposes a data-driven backstepping control (DBC) approach for a class of strict-feedback systems with unknown dynamics. First, unlike the widely-studied adaptive backstepping control approaches, we identify the unknown dynamics of each subsystem based on off-line data and develop a data-driven continuous-time Lyapunov equation return controller, ensuring semi-global exponential stability of the error system. Furthermore, we propose a data-driven dynamic surface control (DDSC) approach for the "complexity explosion" problem in DBC. This approach uses a data-driven LMI to return the controller, ensuring that the error system remains semi-globally UUB, even when the derivative of the virtual controller cannot be calculated. Finally, the superiority and effectiveness of DBC and DDSC are verified by simulation examples.
Wang et al. (Wed,) studied this question.