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针对钢包精炼炉(Ladle Refining Furnace)又称LF炉,配料加料过程的惯性、时滞、非线性等控制特性,设计了一种基于微粒群优化算法(Particle Swarm Optimization,PSO)、误差反向传播(Back Propagation,BP)神经网络以及比例-积分-微分(PID)的复合控制算法PSO-BP-PID,并将该复合算法应用于150 t钢包精炼炉配料称重控制系统中,实现配料称重过程的智能控制。PSO-BP-PID算法利用微粒群优化算法的全局寻优特性,优化BP神经网络的初始权值以提高神经网络的收敛性;采用经微粒群算法优化后的BP神经网络在线实时调整PID参数。通过基于PSO和BP网络的PID控制器实时控制钢包精炼沪的配料过程。仿真实验和运行实验结果表明,PSO-BP-PID算法的控制效果优于单一PID算法的控制效果。采用PSO-BPPID算法的钢包炉配料系统后,明显提高了配料精度,有效地解决了配料称重过程中速度与精度的矛盾。
Aimed at the control characteristics of Ladle Refining Furnace, also called LF furnace, the inertia, time delay and nonlinearity of the material feeding process, a Particle Swarm Optimization (PSO) Back Propagation (BP) neural network and proportional-integral-derivative (PID) composite control algorithm PSO-BP-PID are applied to the weighing control system of 150 t ladle refining furnace, Intelligent control of the process. PSO-BP-PID algorithm uses the global optimization of Particle Swarm Optimization (PSO-BP-PID) algorithm to optimize the initial weights of BP neural network to improve the convergence of neural network. The BP neural network optimized by PSO algorithm is used to adjust PID parameters online in real time. Through the PID controller based on PSO and BP network, the process of ladle refining in Shanghai was controlled in real time. The simulation experiment and experimental results show that the control effect of PSO-BP-PID algorithm is better than that of single PID algorithm. The ladle furnace batching system using PSO-BPPID algorithm obviously improves the precision of ingredients and effectively solves the contradiction between speed and accuracy in the weighing process of ingredients.