Application of a Genetic Algorithm to the Optimization of a Missile Autopilot Controller for Performance Criteria with Non-Analytic Solutions
UNIVERSITY OF CENTRAL FLORIDA ORLANDO DEPT OF ELECTRICAL ENGINEERING
Pagination or Media Count:
Modern optimal control theory provides analytic solutions for a set of linear feedback design problems with linear quadratic performance criteria. Recent progress in the field of robust multivariable feedback design has incorporated additional constraints which have addressed the classical concerns with stability margins, system sensitivity and disturbance rejection. Despite these important advances, many practical design problems arise in which the desired system performance constraints cannot be accommodated by the available theoretic techniques. Genetic algorithms GAs, on the other hand, 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 thesis is to demonstrate that GAs provide a method of optimizing control system problems with analytically intractable constraints. A linear missile 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, first 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 that the genetic algorithm provides good convergence to near optimal controller designs for each successive combination of constraints.
- Guided Missile Dynamics, Configurations and Control Surfaces
- Computer Programming and Software