摘要
真空退火系统是一个多输入多输出、耦合很强的时变系统,耦合会使其控制系统的调节品质降低,严重耦合时将会使系统无法运行。为了对退火温度实现精确地控制,对其顶部、中部和底部的温度进行了解耦控制方案的设计,并利用改进PSO-GA混合优化算法对神经网络的权值和阈值进行优化,为真空退火炉提出一种新的解耦控制方法。利用Matlab对该算法的性能进行仿真验证,并与传统PID及PID神经网络等解耦控制方法进行了对比分析,该解耦控制方法在控制性和抗干扰性上都具有较好的控制效果。
Vacuum annealing system is a multiple input multiple output、strong coupling、time-varying system,Coupling will reduce the quality of the control system,Serious when the system unable to run.In order to realize the precise control of the annealing temperature,Design of decoupling control scheme for the temperature at the top,middle and bottom of the system,The weights and thresholds of the neural network are optimized by using the improved PSO-GA hybrid optimization algorithm,A new decoupling control method is proposed for vacuum annealing furnace.And the performance of the algorithm is simulated and verified by Matlab,And compared with the traditional PID and PID neural network decoupling control methods is analyzed,The method proposed in this paper has a good control effect on decoupling control and noise immunity.
出处
《电脑知识与技术（过刊）》
2017年第6X期180-182,共3页
Computer Knowledge and Technology
关键词
PSO-GA
混合优化算法
真空退火炉
解耦控制
神经网络
PSO-GA
hybrid optimization algorithm
vacuum annealing furnace
decoupling control
neural networks
