摘要
为了提高非线性、不确定和时变性灌浆过程中压力的控制精度,在分析灌浆过程数学模型的基础上,提出了灌浆压力的PID控制器参数的自适应调节方法。由神经网络预测模型对灌浆系统进行非线性建模,然后基于神经网络学习误差迭代优化PID控制参数。为了确保控制器参数矩阵在调节时灌浆压力能收敛于灌浆设计压力,采用了李亚普洛夫误差增量迭代函数,使得对每次采样时刻系统误差PID调节向量能渐近收敛于最优值,从而使模型跟踪误差最小。通过迭代反馈调节方法的压力输出同手工控制方法对比研究,仿真结果表明,此方法有更好的自适应能力,较好地跟踪了灌浆设计压力曲线。
To the problem of pressure controll precision of grouting process with is nonlinearity, uncertainty and time-variety, a robust adaptive PID parameter tuning method is proposed. The grouting pressure system is modeled by neural network predictive model based on analyzing the grouting pressure mathematical dynamic model. The PID parameters are adapted by Lyapunov asymptotically, and the plant output also tracks the desired design pressure. PID gain matrix is updated iteratively according to the NN model error at each sampling data. The simulation results show that the self-learning PID tuning is robust and effective.
出处
《控制工程》
CSCD
北大核心
2009年第3期261-263,共3页
Control Engineering of China
基金
湖南省教育厅基金资助项目(8C091)
关键词
神经网络
自学习
迭代调节
自适应控制
灌浆压力
neural network
self-learning
iteratively tuning
adaptive control
grouting pressure