Tunnel Prescribed Performance Control for Distributed Path Maneuvering of Multi-UAV Swarms via Distributed Neural Predictor

In this brief, we investigate the multiple parameterized paths-guided distributed maneuvering problem of a swarm of simplified unmanned aerial vehicles (UAVs) using a tunnel prescribed performance (TPP) strategy under directed communication. The primary focus of this paper lies in establishing a distributed TPP-based path maneuvering controller to achieve the desired cooperative performance. Firstly, a kinematic control law is designed by using the TPP strategy to limit the overshoot of the distributed path maneuvering error in transient and steady process. Secondly, an update law is developed for each path variable based on a control effort minimization method. Next, a total control law is designed by using a distributed neural predictor (DNP) at the kinetic level, where the DNP is constructed based on the information of neighbors to estimate uncertainties in the kinetics of UAVs. Then, via the Lyapunov analysis, practical distributed path maneuvering of multiple UAVs is achieved, ensuring the uniform ultimate bounded stability of the total closed-loop system through the proposed method. Finally, the effectiveness of the proposed approach for UAV swarms is validated via simulation results.