Abstract
Prescribed performance control (PPC) has been proved to be a powerful tool which seeks transient performance for tracking errors. Unfortunately, the existing PPC schemes only can qualitatively design transient performance, while they cannot quantitatively set the convergence time and meanwhile minimize overshoot. In this article, we propose a new quantitative PPC strategy for unknown strict-feedback systems, capable of quantitatively designing convergence time and minimizing overshoot. Firstly, a new quantitative prescribed performance mechanism is proposed to impose boundary constraint on tracking errors. Then, back-stepping is used to exploit virtual controllers and actual controller based on Nussbaum Function, without requiring any prior knowledge of system unknown dynamics. Compared with the existing methodologies, the main contribution of this paper is that it can guarantee predetermined convergence time and zero overshoot for tracking errors, and meanwhile there is no need of any fuzzy/neural approximation. Finally, compared simulation results are given to validate the effectiveness and advantage.
Approximation-free Quantitative Prescribed Performance Control of Unknown Strict-feedback Systems
