一种基于过程神经元网络辨识的PID控制模型及方法  

PID control model and method based on process neural network identification

作　　者：王兵[1] 李盼池[1] 许少华[1] 

机构地区：[1]大庆石油学院计算机与信息技术学院,黑龙江大庆163318

出　　处：《计算机应用》2010年第1期233-235,239,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60572174);黑龙江省科技厅科学研究项目(GZ07A103)

摘　　要：针对非线性动态系统PID过程控制问题,提出了一种基于过程神经元网络辨识的PID参数自适应整定的控制模型和方法。利用过程神经元网络对于动态系统时变输入/输出信号的学习机制,在某种最优控制律下通过对被控对象进行辨识来追踪被控对象的输出对控制输入变化的灵敏度信息,实现参数自适应匹配的PID控制。给出了基于过程神经元网络辨识的PID控制系统结构以及相应的实现机制,实验结果验证了模型和算法的有效性。Concerning PID process control of nonlinear dynamic system, a control model and its method of PID parameter adaptive tuning based on Process Neural Network （PNN） identification were proposed in the paper. Using the learning mechanism of PNN for time-varying input-output signals of dynamic system, the PID control with parameter adaptive matching was implemented by tracing change sensitivity information of the output with the controlling input of the controlled object by identifying the controlled object under certain optimal control rule. The PID control system structure and corresponding realization mechanism based on PNN identification were presented in the paper and the experimental results verify the effectiveness of the model and algorithm.

关 键 词：非线性动态系统 PID控制 参数辨识 过程神经元网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]