An Optimization Method of Attitude Control Parameters Based on Genetic Algorithm for the Boost-Glide Rocket

The proportional-integral-differential (PID) controller is improved to meet control system requirements for the boost-glide rocket with difficulty in manually adjusting parameters. A method is proposed that directly takes the simulated results with the nonlinear six-degree-freedom model of the rocket as the objective function value, the genetic algorithm is adopted to adjust parameters adaptively for the global optimal control effect with a given range, and the convergence of the algorithm to search the global optimal value under given conditions is analyzed. This method introduces the measured angular velocity into the differential term of the PID controller, which is convenient for establishing the system model and adjust control parameters. Parameter adjustment based on the nonlinear dynamic model can avoid deviations caused by model linearization and facilitate parallel search by genetic algorithm, then reasonable parameters can be quickly obtained with the specified value range. The proposed method is compared with the classical test method in time domain simulation. The simulated results indicate that the proposed method can adjust the parameters adaptively, the maximum deviation of attitude angle is less than 2°, the mean of deviation and the standard deviation of attitude angle deviation are less than 1°, and the control precision is obviously better than that of the classical test method. The experiments from the hardware-in-the-loop simulation show that the attitude control of the selected control parameters is stable, the deviation converges to 0°, and the control effect is good.