Non-Fragile Prescribed Performance Neural Control of Constrained Waverider Vehicle

Abstract

Aiming at the defect that traditional PPC usually leads to control singularity when dealing with the input constrained problem, a new non-fragile prescribed performance control (PPC) methodology is proposed based on neural approximation for waverider vehicle (WV). To deal with the saturation of velocity control input and altitude control input, compensated systems are devised. Furthermore, the states of compensated systems are used to construct adaptive re-adjusting terms that are further applied to improve the constraint envelopes of traditional PPC. Besides, neural networks are introduced to approximate the WV normalized unknown terms, which guarantees the robust performance. The advantage of the proposed method is that it overcomes the fragile defect of traditional PPC, and also reduces the control complexity and online computational load. Finally, the effectiveness and superiority of the exploited method are validated via numerical simulation.

Keywords

• |waverider vehicle|prescribed performance control|fragility|neural network|input constraints|control system|weapon