PID control strategy for UAV flight control system based on improved genetic algorithm optimization

2014 ◽  
Author(s):  
Feng Lin ◽  
Haidong Duan ◽  
Xiaoguang Qu
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Flight Control ◽  
Control Strategy ◽  
Pid Control ◽  
Flight Control System ◽  
Improved Genetic Algorithm ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

2017 ◽  
Vol 121 (1241) ◽  
pp. 877-900 ◽  
Author(s):  
Y. Xu ◽  
Z. Zhen
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Flight Control ◽  
Control Strategy ◽  
Formation Control ◽  
Parametric Uncertainty ◽  
Lyapunov Theory ◽  
Formation Flight ◽  
Flight Control System ◽  
Multiple Uavs

ABSTRACTThe Unmanned Aerial Vehicles (UAVs) become more and more popular due to various potential application fields. This paper studies the distributed leader-follower formation flight control problem of multiple UAVs with uncertain parameters for both the leader and followers. This problem has not been addressed in the literature. Most of the existing literature considers the leader-follower formation control strategy with parametric uncertainty for the followers. However, they do not take the leader parametric uncertainty into account. Meanwhile, the distributed control strategy depends on less information interactions and is more likely to avoid information conflict. The dynamic model of the UAVs is established based on the aerodynamic parameters. The establishment of the topology structure between a collection of UAVs is based on the algebraic graph theory. To handle the parametric uncertainty of the UAVs dynamics, a multivariable model reference adaptive control (MRAC) method is addressed to design the control law, which enables follower UAVs to track the leader UAV. The stability of the formation flight control system is proved by the Lyapunov theory. Simulation results show that the proposed distributed adaptive leader-following formation flight control system has stronger robustness and adaptivity than the fixed control system, as well as the existing adaptive control system.

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.

scholarly journals Genetic Algorithm Optimization and Control System Design of Flexible Structures

2008 ◽  
Vol 17 (Supplement) ◽  
Author(s):  
M.O. Tokhi ◽  
M.Z. Md Zain ◽  
M.S. Alam ◽  
F.M. Aldebrez ◽  
S.Z. Mohd Hashim ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
System Design ◽  
Flexible Structures ◽  
Control System Design ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Optimization And Control ◽  
And Control

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

PID Control Based Missile Sub-Channel Simulation

2012 ◽  
Vol 433-440 ◽  
pp. 7011-7016 ◽  
Author(s):  
Chao Bo Chen ◽  
Bing Liu ◽  
Ning He ◽  
Song Gao ◽  
Quan Pan
Keyword(s):  
Control System ◽  
Real Time ◽  
Flight Control ◽  
Pid Control ◽  
Pid Controller ◽  
Channel Model ◽  
Control Method ◽  
Flight Control System ◽  
Aerodynamic Control ◽  
The Stability

The accuracy and real-time of modern missile flight control system of traditional aerodynamic can not be satisfied. In this paper a new method is presented to improve the accuracy and real-time of missiles under this condition. First of all, a missile sub-channel model of the dynamic equations and steering gear is established, then based on the established model, using PID controller to control steering gear and three channels of missile pitch, yaw, roll respectively which is called missile sub-channel PID control method, and finally making use of MATLAB/Simulink to complete the simulation. Simulation results show that compared with traditional aerodynamic control system, this method can reduce the response time of aerodynamic missile and enhance the stability of the control system obviously.

Optimal analysis and application of the warp tension control system for a rapier loom

2021 ◽  
pp. 004051752110536
Author(s):  
Yanjun Xiao ◽  
Zhenpeng Zhang ◽  
Zhenhao Liu ◽  
Weiling Liu ◽  
Nan Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Pid Control ◽  
Performance Optimization ◽  
Experimental Studies ◽  
Tension Control ◽  
Control Performance ◽  
Improved Genetic Algorithm ◽  
Warp Tension ◽  
Tension Control System

Traditional proportional–integral–derivative (PID) control performance optimization is an essential method to improve a loom’s warp tension control performance. This work proposes an improved genetic algorithm optimized PID control scheme to overcome the decline in control performance of the traditional PID control algorithm in a loom’s warp tension control system. Through the decoupling analysis of loom motion mechanism, the establishment of warp tension model and the optimization of fitness evaluation mechanism of genetic algorithm can effectively overcome the problems of local optimal solution and algorithm degradation of genetic algorithm. Simulation experiments were carried out with the traditional PID, the integral separation PID, and the genetic PID in warp tension control. The results show the advantage of the genetic-PID algorithm to control warp tension stability. Ultimately, according to the functional characteristics of the loom mechanism, a tension control platform for experimental studies was established. The test results show that the maximum fluctuation range of warp tension is within [−2, +6] at the test speed of 850 rpm, which meets the requirements of long-term stable and reliable control of warp tension under different weaving conditions.

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