Nonlinear Predictive Control of Mass Moment Aerospace Vehicles Based on Ant Colony Genetic Algorithm Optimization

摘要

Based on the mathematical model of the mass moment aerospace vehicles (MMAV), a coupled nonlinear dynamical system is established by rational simplification. The flight control system of MMAV is designed via utilizing nonlinear predictive control (NPC) approach. Aiming at the parameters of NPC is generally used the trial-and-error method to optimize and design, a novel kind of NPC parameters optimization strategy based on ant colony genetic algorithm (ACGA) is proposed in this paper. The method for setting NPC parameters with ACA in which the routes of ants are optimized by the genetic algorithm (GA) is derived. And then, a detailed realized process of this method is also presented. Furthermore, this optimization algorithm of the NPC parameters is applied to the flight control system of MMAV. The simulation results show that the system not only meets the demands of time-response specifications but also has excellent robustness.