Parameters identification and forward rushing motion optimization for a big calibre soft recoil gun

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Aiming to deal with the problem of the forward rushing velocity reduction caused by the increase of the firing angle of the soft recoil gun, a dynamical model of the soft recoil gun during forward rushing motion is established, and an improved immune clone selection algorithm is proposed. The vaccination strategy is introduced into the immune clone selection algorithm to improve the convergence speed. The random crossover strategy and elite learning mechanism enhances the ability to escape from the local extremes and avoids premature algorithmic maturity. Based on the introduction of the above three strategies, the convergence speed and calculation accuracy of the algorithm are improved, and the convergence time is greatly shortened which saves a lot of time for subsequent parameter identification and optimization calculations. By using the improved immune clone selection algorithm, the unknown parameters in the dynamical model can be identified accurately and quickly. Then, the parameters are used to correct the dynamical model which increase the accuracy of the optimization results. Finally, the algorithm is used to optimize the initial pressure of the gas spring in the forward momentum generator, so as to ensure that different firing angles have the same maximum forward rushing velocity, which improves the stability of the forward rushing motion and the reliability of the firing. Through the optimization calculation, the pressure variation curve of the gas spring with the change of the firing angle is finally determined. The forward rushing velocity is stable at 5.65m/s with different firing angles.