关键词: 弹仓, 非线性建模, 参数辨识, 灵敏度分析

Abstract: Aiming at the issues of strong subjectivity in control model selection during the design of automatic chain rotational shell magazines, which lead to insufficient model accuracy and difficulties in improving control performance, a tripartite solution of “full-order modeling, parameter identification, and sensitivity-driven model reduction” is proposed. Based on the structural characteristics of a chain rotational shell magazine in an automatic loading mechanism for self-propelled artillery ammunition, a comprehensive nonlinear full-order dynamic model is developed by integrating critical factors including the dynamic characteristics of drive motors, transmission backlash of reducers, polygonal effect in chain drives, time-varying meshing collisions, nonlinear friction dissipation, and structural flexibility. To address the challenges of numerous coupled parameters and empirical calibration difficulties in the dynamic model, a multi-objective parameter identification framework based on the Particle Swarm Optimization (PSO) algorithm is developed for key parameter determination. Multi-condition experimental verification demonstrates that the steady-state relative error between the established full-order dynamic model and the actual system remains below 5.97%. Furthermore, Sobol global sensitivity analysis is employed to quantitatively evaluate the impact of 17 parameters on system dynamic response and positioning accuracy. The analysis reveals that the current-loop global sensitivity indices for load-end mass and Coulomb friction are 0.25 and 0.42 respectively, while the position-loop global sensitivity indices for gear backlash and maximum static friction at the load end are 0.20 and 0.78 respectively. Based on these findings, a reduced-order mathematical model is constructed by retaining critical parameters, significantly simplifying controller design complexity and providing a lightweight model architecture with enhanced adaptability for model-based control algorithm development.

Key words: shell magazine, nonlinear modeling, parameter identification, sensitivity analysis