A Simplified Finite-Time Fuzzy Neural Controller With Prescribed Performance Applied to Waverider Aircraft

This article addresses a finite-time prescribed performance controller within the concise fuzzy-neural framework with application to a waverider aircraft. First, new finite-time performance functions are developed to construct a constraint funnel, which accomplishes that tracking errors converge to their steady-state values in a given time (i.e., finite-time convergence), being expected to guarantee tracking errors with small overshoots. Then, the equivalent transformation approach is introduced to unify unknown dynamics such that the control complexity is reduced. Moreover, to further reduce computational costs, a single-learning-parameter-based regulation scheme is developed for fuzzy-neural approximation. Finally, the proposed method is applied to a waverider aircraft to test its effectiveness and superiority.

Automated summary

This article introduces a finite-time prescribed performance controller within the concise fuzzy-neural framework for a waverider aircraft. New finite-time performance functions are developed to ensure finite-time convergence of tracking errors with small overshoots. The equivalent transformation approach and a single-learning-parameter-based regulation scheme are used to reduce control complexity and computational costs.