Improved Iterative Learning Control Study for Reducing the Non-repetitive Disturbance of Industrial Process
Noted that the restraint ability of traditional iterative learning control algorithm against non-repetitive disturbance exists in industrial processing is not good, this article propose a kind of iterative learning control algorithm with weighted PD-type exponential variable gain closed-loop which samples the repeatability disturbance signal and translates it into a step sequence that changes according to the set value. Corresponding to each non-repetitive disturbance adopts a modified iterative learning control algorithm with weighted PD-type exponential variable gain closed-loop, get more ideal system output) the dynamic performance of control system improved significantly. Finally, we proved that tracking error converge to zero congruously when the iteration tend to be infinite. Simulation results show the effectiveness of the proposed control algorithm.
Iterative learning control PD-type iterative learning control Non-repetitive Disturbance Convergence
Tie-jun Chen Li Cai
College of Electrical Engineering Zhengzhou University Zhengzhou, China
国际会议
重庆
英文
70-74
2011-01-21（万方平台首次上网日期，不代表论文的发表时间）