在液压APC(Autom atic Position Control)系统中,由于存在控制干扰和测量噪声,为了达到控制精度,通常采取低通滤波,但效果不甚理想。为此,本文提出了一种基于卡尔曼滤波器的控制方法,该方法采用时域上的递推算法进行数字滤波处理,通过...在液压APC(Autom atic Position Control)系统中,由于存在控制干扰和测量噪声,为了达到控制精度,通常采取低通滤波,但效果不甚理想。为此,本文提出了一种基于卡尔曼滤波器的控制方法,该方法采用时域上的递推算法进行数字滤波处理,通过仿真和实际应用证明该方法使APC系统运行平稳,大大提高了APC系统的精度。展开更多
The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning co...The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.展开更多
文摘在液压APC(Autom atic Position Control)系统中,由于存在控制干扰和测量噪声,为了达到控制精度,通常采取低通滤波,但效果不甚理想。为此,本文提出了一种基于卡尔曼滤波器的控制方法,该方法采用时域上的递推算法进行数字滤波处理,通过仿真和实际应用证明该方法使APC系统运行平稳,大大提高了APC系统的精度。
基金Project(51074051)supported by the National Natural Science Foundation of China
文摘The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.
