Nonlinear Trajectory Tracking Control of Unmanned Vehicle based on Dynamic Linearization

Abstract

 Aimed at the trajectory tracking control problem of nonlinear unmanned vehicle systems with unknown models, a model-free adaptive control algorithm based on dynamic linearization technology is proposed. Firstly, the dynamic data mapping of unmanned vehicles is established by the dynamic linearization method, and the dynamic of unmanned vehicles is estimated in real-time based on a pseudo-partial derivative estimation algorithm, which effectively reduces the modelling error of fixed parameters. On this basis, a model-free adaptive control scheme based on pseudo-partial derivatives is proposed, and the convergence of the algorithm is analyzed based on the compression mapping criterion. Finally, the effectiveness and robustness of the algorithm are verified by simulation experiments in normal and communication interruption scenarios.