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

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoyu Zhang ◽  
Peng Li ◽  
Dexin Xu ◽  
Ben Mao ◽  
Kunpeng He
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Predictive Control ◽  
Flight Control ◽  
Nonlinear Dynamical System ◽  
Ant Colony ◽  
Flight Control System ◽  
Aerospace Vehicles ◽  
Nonlinear Predictive Control ◽  
Mass Moment

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.

Knowledge-Based Automatic Fault Detection in Flight Control System

2007 ◽  
Author(s):  
C. H. Lo ◽  
Eric H. K. Fung ◽  
Y. K. Wong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Control System ◽  
Fault Detection ◽  
Flight Control ◽  
Detection System ◽  
Flight Control System ◽  
Actuator Failure ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic

There are various possible failures, like, actuator, sensor, or structural, which can occur on a sophisticated modern aircraft. In certain situations the need for an automatic fault detection system provides additional information about the status of the aircraft to assist pilots to compensate for failures. In this paper, we develop an intelligent technique based on fuzzy-genetic algorithm for automatically detecting failures in flight control system. The fuzzy-genetic algorithm is proposed to construct the automatic fault detection system for monitoring aircraft behaviors. Fuzzy system is employed to estimates the times and types of actuator failure. Genetic algorithms are used to generate an optimal fuzzy rule set based on the training data. The optimization capability of genetic algorithms provides and efficient and effective way to generate optimal fuzzy rules. Different types of actuator failure can be detected by the fuzzy-genetic algorithm based automatic fault detection system after tuning its rule table. Simulations with different actuator failures of the non-linear F-16 aircraft model are conducted to appraise the performance of the proposed automatic fault detection system.

Flight control system design and optimisation with a genetic algorithm

2005 ◽  
Vol 9 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Roberto Fantinutto ◽  
Giorgio Guglieri ◽  
Fulvia B. Quagliotti
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
System Design ◽  
Flight Control ◽  
Control System Design ◽  
Flight Control System
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