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Mathematical Problems in Engineering
Volume 2013, Article ID 858190, 10 pages
Research Article

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

College of Automation, Harbin Engineering University, Harbin 150001, China

Received 26 April 2013; Revised 5 August 2013; Accepted 30 August 2013

Academic Editor: Vishal Bhatnaga

Copyright © 2013 Xiaoyu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.