A novel modeling and controlling approach for high-order nonlinear systems

This paper proposes a completely data–driven control law for a class of high–order nonlinear systems based on their virtual characteristic models. With sampled–data techniques and estimation methods, a novel lower–order adaptive characteristic model is constructed to reduce the system complexities. Moreover, a corresponding sliding mode control law is designed, which can guarantee tracking errors of the resulting closed-loop system converging into a predefined bound in finite time. The practical examples are provided to illuminate the effectiveness of the proposed approaches.