基于RBF神经网络整定的热风炉温控系统设计

为了提高热风炉的燃烧效率,改善热风炉温控系统的自动化程度,提出了一种基于RBF神经网络整定的PID控制策略。首先,通过RBF神经网络算法和增量式PID控制器的结合,将神经网络强大的自学习能力应用于对增量式PID参数的调整。然后,在常规热风炉温控系统的基础上,将其外环改为采用RBF神经网络整定的PID控制。热风炉温控系统中内环以煤气阀门开度为变量,外环以拱顶温度为控制变量,通过改进的串级控制来实现热风炉的燃烧优化调整。Matlab仿真分析和实际应用效果表明,RBF神经网络整定的PID控制曲线几乎无超调量,系统抗干扰能力相对传统的PID控制提高了50%。与传统的手动控制相比,所提出的控制策略使得原系统的抑制干扰能力明显增强、鲁棒性更好,在热风炉温控方面具有良好的研究和应用价值。

Research of temperature control of hot blast furnace based on RBF neural network tuning

In order to improve the combustion efficiency of the hot blast stove and improve the automation degree of the hot blast stove temperature control system, a PID control strategy based on RBF neural network tuning is proposed. First, through the combination of the RBF neural network algorithm and the incremental PID controller, the powerful self-learning ability of the neural network is used to adjust the parameters of the incremental PID. Then, based on the conventional hot-blast stove temperature control system, the outer loop was changed to PID control using RBF neural network tuning. In the hot-blast furnace temperature control system, the inner ring takes the opening degree of the gas valve as a variable, and the outer ring takes the dome temperature as a control variable. The improved cascade control is used to optimize the combustion of the hot-blast stove. Matlab simulation analysis and practical application results show that the PID control curve set by the RBF neural network has almost no overshoot, and the anti-interference ability of the system is increased by 50% compared with the traditional PID control. Compared with the traditional manual control, the proposed control strategy makes the original system''s ability to suppress interference significantly stronger and more robust. It has good research and application value in hot air furnace temperature control.