Fuzzy Neural Pseudo Control With Prescribed Performance for Waverider Vehicles: A Fragility-Avoidance Approach
针对乘波体飞行器，提出一种基于模糊神经逼近的伪非仿射控制协议，在保证跟踪误差预设性能的同时，克服传统预设性能控制固有的脆弱性缺陷。
A fuzzy-neural-approximation-based pseudo nonaffine control protocol is proposed for waverider vehicles (WVs), which is capable of guaranteeing tracking errors with desired prescribed performance and rejecting the obstacle of fragility inherent to the traditional prescribed performance control (PPC). The pseudo control is defined to approximate the nonaffine dynamics of WVs, while there is no need of model affinization. Furthermore, fuzzy neural approximators are combined with the adaptive compensation strategy to resist both system uncertainties and external disturbances. Especially, a new type of nonfragile prescribed performance, being able to self-adjust its prescribed funnel, is proposed to remedy the fragility defect associated with the existing PPC. Finally, the realizability of the spurred prescribed performance is proved via stability proof, and the superiority of the addressed design is tested by compared simulations.