基于BSO改进模糊神经网络PID的管道压力控制策略研究

Research on Pipeline Pressure Control Strategy Based on BSO Improving Fuzzy Neural Network PID

针对煤矿供水管道压力控制系统智能化程度低、人工操作实时性和准确性差等问题,设计了一种基于天牛群优化(BSO)改进模糊神经网络PID的管道压力控制策略。首先,基于BSO算法对PID控制器的初始参数进行参数迭代优化,找到最佳初始参数;其次,通过模糊神经网络实现实际压力偏差值的模糊化、模糊推理等处理,实时调整PID控制参数;最后,PID控制器运算后得到的输出信号作用在执行机构上,实现电动阀门的智能化控制。实验结果表明,该控制策略响应速度更快,超调量更小,稳定性更强,满足阀门控制开度要求,提高了煤矿井下供水系统的智能化水平,达到了减人、降本增效的目的。

Aiming at the problems of low degree of intelligence and poor real-time and accuracy of manual operation of coal mine water supply pipeline pressure control system,a pipeline pressure control strategy based on beetle swarm optimization(BSO)improving fuzzy neural network PID was designed.Firstly,the initial parameters of the PID controller are optimized iteratively based on the BSO algorithm to find the best initial parameter settings.Secondly,the fuzzy neural network is used to realize the fuzzy and fuzzy reasoning of the actual pressure deviation value,and the PID control parameters are adjusted in real time.Finally,the output signals obtained from the operation of the PID controller are applied to the actuator,and the intelligent control of the motorized valve is realized.Experimental results show that this control strategy has faster response speed,smaller overshooting amount and stronger stability,meets the valve control opening requirements,improves the intelligent level of the coal mine underground water supply system,and achieves the purpose of staff reduction,cost reduction and efficiency increasing.