Velocity Control of Near Space Vehicle Based on Enhanced Pigeon-Inspired Optimization

The problem of near space vehicle (NSV) control has aroused widespread concern in recent years. However, optimal control parameters are not easy to obtain which still remains a pressing challenge. This paper addresses the issue of NSV control parameters optimization. First, the velocity control method based on an active disturbance rejection control (ADRC) technique for the longitudinal nonlinear model of the NSV is given. Then, an enhanced pigeon-inspired optimization algorithm with a golden-sine mutation mechanism and an opposition-based learning strategy (GOPIO) is presented to achieve control parameter optimization. A case study is conducted to prove the validity of the proposed method. Simulation results indicate that the local search ability is strengthened in GOPIO compared with traditional algorithms.