基于单神经元自适应控制器的IGCC电站气化炉控制

Adaptive PID Control for IGCC Gasifier Based on Single Neuron

在分析IGCC电站中气化炉的工作特性与气化炉控制系统基本结构的基础上,对气化炉控制系统进行了设计。运用单神经元自适应控制方法,采用改进的有监督的Hebb学习规则在线完成控制器参数的自整定,从而实现系统的智能控制。仿真结果表明,与常规PID控制方法相比,该方法具有控制精度高、响应速度快的优点,并且具备较强的自适应性和鲁棒性。

Based on the analysis of working characteristics and the control system of the IGCC gasifier,an adaptive PID controller using single neuron is used.The self-learning method is an improved Supervised Hebb learning method that can revise the PID parameters on line.It is validated by simulation that the adaptive controller is of higher accuracy,faster response,stronger self-adaptability and robustness than normal PID controller.