Approximation-Avoidance-Based Robust Quantitative Prescribed Performance Control of Unknown Strict-Feedback Systems

Abstract

In this article, we propose a robust quantitative prescribed performance control (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 controllers based on the 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 for any fuzzy/neural approximation. Finally, compared simulation results are given to validate the effectiveness and advantage.

Keywords

• quantitative prescribed performance
• transient performance
• convergence time
• overshoot
• back-stepping