Data-driven super-twisting observer design for systems of dimension more than one

This study presents data-driven super-twisting-like smooth observer design for systems of dimension more than one. The major advantage of the proposed observers is finite-time convergence of the estimation error to zero and smoothness of the observer trajectory. This study concludes with numerical simulations illustrating performance of the designed algorithms.