摘要：

针对一类伺服电机直接驱动油泵的注塑机液控系统, 工业界通常采用PID控制方法进行控制, 但其控制效果较差, 难以达到较高的控制精度。为了改进PID控制, 将模糊控制与PID控制相结合成为一种有效的方法。针对模糊PID算法参数调试过程中存在的操作繁琐、难以找到最优参数组合等问题, 提出一种基于遗传粒子群算法(GAPSO)优化的模糊PID控制方法。对粒子群算法(PSO)进行改进, 提出一种惯性因子随S函数变化的改进PSO算法(SDIF-PSO), 在改进粒子群算法的基础上, 将改进PSO算法与GA算法相结合, 构建基于GAPSO算法优化的模糊PID控制器。利用Matlab/Simulink对注射过程进行仿真, 实验结果表明, 相比于传统的模糊PID控制器以及分别采用改进PSO算法和GA算法优化的模糊PID控制器, 基于GAPSO优化的模糊PID控制器具有响应速度更快、超调量更小、稳态精度更高等优点。

关键词: 伺服电机, 注塑机, 注射速度, 模糊PID, 遗传粒子群算法, 混合优化算法

Abstract:

In the hydraulic control system of injection molding machines directly driven by servo motors for oil pumps, the industrial sector typically employs Proportional-Integral-Derivative (PID) control methods. However, their control performance is relatively poor, and they struggle to achieve high control accuracy. Combining fuzzy control with PID control has emerged as an effective approach for improving PID control. To address issues such as cumbersome operations and difficulties in finding optimal parameter combinations during the tuning process of fuzzy PID algorithms, a fuzzy PID control method optimized by the Genetic Algorithm-Particle Swarm Optimization (GAPSO) algorithm is proposed. An improved Particle Swarm Optimization (PSO) algorithm, in which the inertia weight varies with an S-function (SDIF-PSO), is introduced. This modified PSO algorithm is integrated with the Genetic Algorithm (GA) to construct a fuzzy PID controller optimized by the GAPSO algorithm. Simulations of the injection process are conducted using Matlab/Simulink. Experimental results demonstrate that, compared with traditional fuzzy PID controllers and fuzzy PID controllers optimized solely by the improved PSO algorithm or GA algorithm, the fuzzy PID controller optimized by GAPSO exhibits faster response, smaller overshoots, and higher steady-state accuracy.

Key words: servo motor, injection molding machine, injection speed, fuzzy PID, genetic particle swarm algorithm, hybrid optimization algorithm