基于改进蝴蝶优化算法的电液伺服系统参数辨识研究

Parameter Identification of Electro-Hydraulic Servo Systems Using Improved Butterfly Optimization

  • 摘要: 电液伺服系统是非线性的、时变的、参数耦合的,传统的辨识方法存在着收敛慢、精度低、容易陷入局部最优的问题。为了提高电液伺服系统参数辨识的效果,本文提出了一种改进的蝴蝶优化算法(IBOA),用它来对系统进行参数辨识。首先建立电液伺服系统状态空间数学模型,确定待辨识参数及主要需求;其次针对标准蝴蝶优化算法(BOA)的不足,从混沌初始化、自适应感知系数等四个方面进行改进;然后建立目标函数,设计辨识流程和软硬件方案;最后用仿真和实验验证的方法与BOA、PSO、GA进行比较。结果表明IBOA辨识精度高、收敛快、稳定,可以给系统建模和控制提供可靠的支撑。

     

    Abstract: The electro-hydraulic servo system is nonlinear, time-varying, and parameter-coupled. Traditional identification methods suffer from slow convergence, low accuracy, and the tendency to get trapped in local optima. To improve the effectiveness of parameter identification for the electro-hydraulic servo system, this paper proposes an improved butterfly optimization algorithm (IBOA) and uses it to identify the system parameters. Firstly, a state-space mathematical model of the electro-hydraulic servo system is established, and the parameters to be identified and the main requirements are determined. Secondly, in view of the shortcomings of the standard butterfly optimization algorithm (BOA), improvements are made in four aspects: chaotic initialization and adaptive perception coefficients. Then, the objective function is established, the identification process and software/hardware schemes are designed. Finally, the methods of simulation and experiment verification are used to compare with BOA, PSO, and GA. The results show that IBOA has high identification accuracy, fast convergence, and stability, and can provide reliable support for system modeling and control.

     

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