广东工业大学学报
廣東工業大學學報
엄동공업대학학보
Journal of Guangdong University of Technology
2015年
4期
112-117
,共6页
改进递推预测误差算法%神经网络%极点配置自校正PID
改進遞推預測誤差算法%神經網絡%極點配置自校正PID
개진체추예측오차산법%신경망락%겁점배치자교정PID
the modified recursive prediction error algorithm%neural network%self tuning PID via pole-assignment
针对工业控制中系统模型参数通常未知的特点,利用改进递推预测误差算法为基础的神经网络系统参数辨识方法,设计了极点配置自校正数字PID控制器.相比于基于梯度学习算法的神经网络辨识方法和通常的PID控制器,该方法具有参数辨识结构简单、神经元权值调整可持续且计算速度快、所采用的数字PID控制器鲁棒性强等优点.最后的数值仿真结果验证了本文算法及控制方法的有效性.
針對工業控製中繫統模型參數通常未知的特點,利用改進遞推預測誤差算法為基礎的神經網絡繫統參數辨識方法,設計瞭極點配置自校正數字PID控製器.相比于基于梯度學習算法的神經網絡辨識方法和通常的PID控製器,該方法具有參數辨識結構簡單、神經元權值調整可持續且計算速度快、所採用的數字PID控製器魯棒性彊等優點.最後的數值倣真結果驗證瞭本文算法及控製方法的有效性.
침대공업공제중계통모형삼수통상미지적특점,이용개진체추예측오차산법위기출적신경망락계통삼수변식방법,설계료겁점배치자교정수자PID공제기.상비우기우제도학습산법적신경망락변식방법화통상적PID공제기,해방법구유삼수변식결구간단、신경원권치조정가지속차계산속도쾌、소채용적수자PID공제기로봉성강등우점.최후적수치방진결과험증료본문산법급공제방법적유효성.
Since the parameters in control system models are usually unknown in industrial applications,this paper tries to identify the system parameters by using the modified recursive prediction error algorithm for neural networks,and then design a self tuning PID controller via the pole-assignment method.Com-pared with the neural network identification based on the gradient learning algorithm and conventional PID,the method in this paper has simple structure of parameters,sustainable adjustment of neuron weights and quick calculation speed.Furthermore,this digital PID controller also enjoys good perform-ance and easy application.And the simulation results verify that the effectiveness of this identification al-gorithm as well as the controller in this paper.
