【摘要】 加热炉在重轨钢连铸凝固工艺中面临复杂的非线性热传导过程，其温度动态特性受煤气燃烧热量波动、蓄热体预热效率变化和烟气热损失等多因素耦合影响，使固定参数的PID控制器难以实现温度的快速跟踪与稳态调节，且参数固化使系统无法适应工况变化，最终造成温度控制精度不足。为此，设计重轨钢连铸凝固下加热炉温度模糊自寻优控制方法。剖析影响重轨钢连铸凝固下加热炉温度的主要因素，构建加热炉温度控制的传递函数；以传递函数为基础，设计模糊PID控制器动态调节量化因子与比例因子，实现加热炉温度的快速跟踪与稳态平衡；运用粒子群算法对模糊PID参数展开全局寻优，将参数组合视为智能粒子，基于适应度函数迭代更新粒子位置与速度，最终获取最优控制参数，以此实现对加热炉温度的精确控制。实验结果表明，所提方法控制精度与设定温度曲线基本一致，稳态误差接近零，使其保持在最优温度状态。更多还原

【Abstract】 The heating furnace faces a complex nonlinear heat conduction process in the continuous casting and solidification process of heavy rail steel, and its temperature dynamic characteristics are coupled by multiple factors such as fluctuations in gas combustion heat, changes in preheating efficiency of the heat storage body, and flue gas heat loss. This makes it difficult for a fixed parameter PID controller to achieve rapid temperature tracking and steady-state adjustment, and parameter solidification makes the system unable to adapt to changes in operating conditions, ultimately resulting in insufficient temperature control accuracy. Therefore, a fuzzy self optimization control method for the temperature of the heating furnace under continuous casting and solidification of heavy rail steel is designed. Analyze the main factors affecting the temperature of the heating furnace during continuous casting and solidification of heavy rail steel, and construct a transfer function for temperature control of the heating furnace; Based on the transfer function, a fuzzy PID controller is designed to dynamically adjust the quantization factor and scaling factor, achieving rapid tracking and steady-state equilibrium of the heating furnace temperature; Using particle swarm optimization algorithm to globally optimize fuzzy PID parameters, considering parameter combinations as intelligent particles, iteratively updating particle position and velocity based on fitness function, and ultimately obtaining optimal control parameters to achieve precise control of heating furnace temperature. The experimental results show that the control accuracy of the proposed method is basically consistent with the set temperature curve, and the steady-state error is close to zero, keeping it in the optimal temperature state.更多还原