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
Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-depe...Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-dependent trajectories. Specifically, by decoupling the current reference into the desired trajectory of the last trial and a disturbance signal with small magnitude, the learning and feedback parts were designed respectively to ensure fine tracking performance. After some theoretical analysis, the judging condition on whether the composite iterative learning control approach achieves better control results than pure feedback contro! was obtained for varying references. The convergence property of the closed-loop system was rigorously studied and the saturation problem was also addressed in the controller. The designed composite iterative learning control strategy is successfully employed in an atomic force microscope system, with both simulation and experimental results clearly demonstrating its superior performance.展开更多
基金Supported by National Natural Science Foundation of China （61273137, 51209026, 61074017）, the Scientific Research Fund of Liaoning Provincial Education Department （L2013202）, and the Fundamental Research Funds for the Central Universities （3132013037, 3132014047, 3132014321）
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Projects(61127006,61325017)supported by the National Natural Science Foundation of China
文摘Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-dependent trajectories. Specifically, by decoupling the current reference into the desired trajectory of the last trial and a disturbance signal with small magnitude, the learning and feedback parts were designed respectively to ensure fine tracking performance. After some theoretical analysis, the judging condition on whether the composite iterative learning control approach achieves better control results than pure feedback contro! was obtained for varying references. The convergence property of the closed-loop system was rigorously studied and the saturation problem was also addressed in the controller. The designed composite iterative learning control strategy is successfully employed in an atomic force microscope system, with both simulation and experimental results clearly demonstrating its superior performance.
