摘要
为解决水煤浆加压气化炉温度控制中存在的干扰因素多、大滞后及难以建立精确数学模型等问题,在实际运用中,一般采用模糊控制器实现对气化炉温度控制,但模糊控制器的性能受很多方面的影响,控制效果不是很好。考虑到粒子群优化算法操作简单、收敛速度快等优点,通过定义一个等效概率矩阵,完成模糊规则的编码,选择合适的目标函数作为粒子的适应度函数,通过迭代优化,得到适应度较高的粒子,将其作为模糊控制的规则;应用结果表明优化后的控制效果虽然在平稳性方面有所损失,但在快速性上要明显优于原模糊预测控制器,达到优化控制的目的。
In order to solve the problems such as multi-interference factors, large delay and difficult to establish accurate mathematical model, temperature control system of water-coal-mixture gasifier, fuzzy controller in general realized the gasifier temperature control in actual use, however, the performance of fuzzy controller is affected by many factors, the control effect is not very good. Considering that the advantages, simple operation and fast convergence speed of the particle swarm optimization （PSO） algorithm, the coding of fuzzy rules is completed by defining an equivalent probability matrix, the appropriate objective function is selected as particle fitness function, the particles with higher fitness degree is obtained by iterative optimization, and used as the fuzzy control rules. The results of application show the opti- mized control effect is much better than the original fuzzy predictive controller in terms of rapidity rather than in terms of stationary, to a-chieve optimal control.
出处
《计算机测量与控制》
北大核心
2013年第4期913-915,共3页
Computer Measurement &Control
关键词
粒子群优化算法
模糊规则
温度控制
气化炉
particle swarm optimization （PSO） algorithm
fuzzy control rules
temperature control
gasifier
