TY - GEN
T1 - Using genetic algorithms to optimize a autopilot controller
AU - Cong, Mingyu
AU - Zhang, Wei
AU - Wang, Liping
PY - 2003
Y1 - 2003
N2 - Many practical design problems arise in which the desired system performance constraints cannot be accommodated by the available optimizing theoretic techniques. Genetic algorithms (GA) offer a numerical search method which does not require a statement of the mathematical relationship between the performance criteria and the parameter update rule. The objective of this paper is to demonstrate that GA provide a method of optimizing control system with analytically intractable constraints. A linear guided bomb airframe and actuator state space model is developed with linear feedback controller and implemented in a discrete time simulation. A genetic algorithm is constructed to optimize the linear controller parameters, with respect to a weighted linear quadratic performance index. Additional performance constraints are then imposed to meet rise time, peak actuator effort, and settling error specifications. Computer simulation results show mat the genetic algorithm provide good convergence to near optimal controller designs for each successive combination of constraints.
AB - Many practical design problems arise in which the desired system performance constraints cannot be accommodated by the available optimizing theoretic techniques. Genetic algorithms (GA) offer a numerical search method which does not require a statement of the mathematical relationship between the performance criteria and the parameter update rule. The objective of this paper is to demonstrate that GA provide a method of optimizing control system with analytically intractable constraints. A linear guided bomb airframe and actuator state space model is developed with linear feedback controller and implemented in a discrete time simulation. A genetic algorithm is constructed to optimize the linear controller parameters, with respect to a weighted linear quadratic performance index. Additional performance constraints are then imposed to meet rise time, peak actuator effort, and settling error specifications. Computer simulation results show mat the genetic algorithm provide good convergence to near optimal controller designs for each successive combination of constraints.
UR - https://www.scopus.com/pages/publications/78650163435
U2 - 10.1109/ICNNSP.2003.1279297
DO - 10.1109/ICNNSP.2003.1279297
M3 - 会议稿件
AN - SCOPUS:78650163435
SN - 0780377028
SN - 9780780377028
T3 - Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
SP - 416
EP - 419
BT - Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
T2 - 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Y2 - 14 December 2003 through 17 December 2003
ER -