针对突发式无线通信自动增益控制(automatic gain control,AGC)存在的器件非线性、响应时延及调制峰均比问题,提出一种基于模糊控制和比例微分控制的快速AGC控制方法。设计系统结构,分析AGC控制的影响因素,进行模糊控制粗调和PD控制精调...针对突发式无线通信自动增益控制(automatic gain control,AGC)存在的器件非线性、响应时延及调制峰均比问题,提出一种基于模糊控制和比例微分控制的快速AGC控制方法。设计系统结构,分析AGC控制的影响因素,进行模糊控制粗调和PD控制精调,给出软件流程,并在常温环境下进行系统实验。实验结果表明:相比传统AGC方案,该方案可在大动态范围实现快速收敛。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
文摘针对突发式无线通信自动增益控制(automatic gain control,AGC)存在的器件非线性、响应时延及调制峰均比问题,提出一种基于模糊控制和比例微分控制的快速AGC控制方法。设计系统结构,分析AGC控制的影响因素,进行模糊控制粗调和PD控制精调,给出软件流程,并在常温环境下进行系统实验。实验结果表明:相比传统AGC方案,该方案可在大动态范围实现快速收敛。
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.
