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常规伺服系统根据电机轴系转动进行模型分析,以轴系所在的基座空间作为参照系。稳定平台的被控量以惯性空间作为参照系,因此不适合用常规伺服系统模型来建模。针对稳定平台的多参照系问题,文章采用以惯性空间作为电机轴系转动参照系的多空间分析模型,并将改进粒子群算法应用于该模型。粒子群算法作为一种群智能算法,广泛应用于参数优化。文中通过惯性权重改进和越界改进,利用改进后的粒子群算法进行稳定平台PID参数的优化和整定。通过仿真和硬件实验平台验证,结果表明:在稳定平台多空间分析模型基础之上,采用改进粒子群算法优化后的PID控制器可以使稳定平台有更高的稳定精度、更好的鲁棒性,有效地隔离了外部的震动和干扰。
Conventional servo system based on motor shaft rotation model analysis, with the shaft base where the space as a reference system. The controlled platform of the stable platform takes the inertial space as the frame of reference and therefore is not suitable for modeling with the conventional servo system model. In order to solve the problem of multi-reference system of stable platform, this paper adopts inertial space as the multi-space analysis model of motor shaft rotation reference frame and applies the improved particle swarm optimization algorithm to this model. As a swarm intelligence algorithm, Particle Swarm Optimization is widely used in parameter optimization. In this paper, the improved PSO algorithm is used to optimize and set the steady-state PID parameters through inertia weight improvement and cross-border improvement. The results of simulation and hardware experimental platform show that the improved PID controller based on improved particle swarm optimization can make the stable platform have higher stability and better robustness based on the stable platform multi-space analysis model. , Effectively isolate the external vibration and interference.