Difference Estimator Based Data-Driven Predictive Anti-Disturbance Control for Input-Delayed Nonlinear Systems with Unknown Dynamics

This paper proposes a difference estimator based data-driven predictive anti-disturbance control scheme for unknown nonlinear systems with input delay. By reformulating the system into a dynamic linearization model with a residual term, a predictive anti-disturbance control law is firstly designed to compensate for the time delay, where the unknown partial derivative and the residual uncertainty are respectively estimated by a projection-like algorithm and a difference estimator. To make the control scheme executable, the future system output is then reconstructed by combining the first-order Taylor expansion with the nonlinear tracking differentiator. The closed-loop convergence analysis is conducted rigorously by contraction mapping principle. Finally, the efficacy of the proposed scheme is illustrated by a numerical example.