为解决以往PID控制器对非线性、时变的推床位置自动控制(Automatic Position Control,APC)系统控制效果不理想,无法满足钛合金轧制精度标准的问题,提出将PID位置调节器改进为模糊PID位置调节器的方案。同时为解决模糊控制中量化因子和...为解决以往PID控制器对非线性、时变的推床位置自动控制(Automatic Position Control,APC)系统控制效果不理想,无法满足钛合金轧制精度标准的问题,提出将PID位置调节器改进为模糊PID位置调节器的方案。同时为解决模糊控制中量化因子和比例因子取值过度依赖专家经验的问题,引入粒子群算法寻找参数最优值。针对粒子群优化模糊PID算法难以在工程中应用这一现状,提出一种解决方法,并在实验平台中采用该方法对改进后的推床APC系统进行实验验证,证明该方法实际可行。仿真与实验表明:改进后的推床APC系统具有响应速度快、几乎无超调等优点,能够更好地满足钛合金轧制的精度要求。展开更多
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
The pneumatic rotary position system, in which an electro-pneumatic proportional flow valve controled a rotary cylinder, was studied, and its mathematical model was built. The model indicated that the controlled pneum...The pneumatic rotary position system, in which an electro-pneumatic proportional flow valve controled a rotary cylinder, was studied, and its mathematical model was built. The model indicated that the controlled pneumatic system had disadvantages such as inherent non-linearity and variations of system parameters with working points. In order to improve the dynamic performance of the system, feed forward compensation self-tuning pole-placement strategy was adopted to place the poles of the system in a desired position in real time, and a recursive least square method with fixed forgetting factors was also used in the parameter estimation. Experimental results show that the steady state error of the pneumatic rotary position system is within 3% and the identified system parameters can be converged in 5 s. Under different loads, the controlled system has an excellent tracking performance and robustness of anti-disturbance.展开更多
文摘为解决以往PID控制器对非线性、时变的推床位置自动控制(Automatic Position Control,APC)系统控制效果不理想,无法满足钛合金轧制精度标准的问题,提出将PID位置调节器改进为模糊PID位置调节器的方案。同时为解决模糊控制中量化因子和比例因子取值过度依赖专家经验的问题,引入粒子群算法寻找参数最优值。针对粒子群优化模糊PID算法难以在工程中应用这一现状,提出一种解决方法,并在实验平台中采用该方法对改进后的推床APC系统进行实验验证,证明该方法实际可行。仿真与实验表明:改进后的推床APC系统具有响应速度快、几乎无超调等优点,能够更好地满足钛合金轧制的精度要求。
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.
基金Project(50375034) supported by the National Natural Science Foundation of China
文摘The pneumatic rotary position system, in which an electro-pneumatic proportional flow valve controled a rotary cylinder, was studied, and its mathematical model was built. The model indicated that the controlled pneumatic system had disadvantages such as inherent non-linearity and variations of system parameters with working points. In order to improve the dynamic performance of the system, feed forward compensation self-tuning pole-placement strategy was adopted to place the poles of the system in a desired position in real time, and a recursive least square method with fixed forgetting factors was also used in the parameter estimation. Experimental results show that the steady state error of the pneumatic rotary position system is within 3% and the identified system parameters can be converged in 5 s. Under different loads, the controlled system has an excellent tracking performance and robustness of anti-disturbance.
