On the Robustness of a Modified Super-Twisting Algorithm With Prescribed-Time Convergence

On the Robustness of a Modified Super-Twisting Algorithm With Prescribed-Time Convergence

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This paper addresses the robustness of a here novel two-stage super-twisting algorithm designed to converge within a prescribed time interval despite disturbances and model uncertainties.Initially, we introduce a method for tuning parameters that guarantees the algorithm’s analytic solution will reach the origin precisely at a prescribed instant, assuming an unperturbed scenario.We then enhance this method to maintain prescribed-time convergence, even when faced with unknown bounded disturbances.The algorithm’s performance is demonstrated through a Latest Product Releases & Innovations – Stay Updated! numerical simulation of a state estimation problem for a perturbed damped pendulum.The results show that the estimation errors converge robustly to the origin at the prescribed time and remain there afterward.