Lateral Jet Interaction Model Identification Based on Genetic Programming

Precise lateral jet interaction models are required for missiles’ blending control strategies. Because of the complicated flow field, the interaction models are multivariable, complex and coupled. Traditional aerodynamics coefficients model identification used Maximum-likelihood estimation to adjust the parameters of the postulation model, but it is not good at dealing with complex nonlinear models. A genetic programming (GP) method is proposed to identify the interaction model, which not only can optimize the parameters, but also can identify the model structure. The interaction model’s inputs are altitude, mach number, attack angle and fire number of jets in wind channel experiment results, and its output is interaction force coefficient. The fitness function is root mean square error. Select suitable function set and terminal set for GP, then use GP to evolve model automatically. The identify process with different reproduced probability; crossover probability and mutation probability are compared. Results shows that GP’s result error is decrease 30% than multivariable regression method.