个人信息Personal Information
教授
博士生导师
硕士生导师
主要任职:Professor at the Institute of Advanced Measurement & Control Technology
其他任职:先进检测与控制技术研究所所长
性别:男
毕业院校:上海交通大学
学位:博士
所在单位:控制科学与工程学院
学科:控制理论与控制工程. 化学工程
办公地点:大连理工大学控制科学与工程学院先进检测与控制技术研究所
大连市凌工路2号大连理工大学海山楼A座724室
联系方式:Tel:(0411)84706465 实验室网站:http://act.dlut.edu.cn/
电子邮箱:tliu@dlut.edu.cn
Robust PI based set-point learning control for batch processes subject to time varying uncertainties and load disturbance
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论文类型:会议论文
发表时间:2014-01-01
收录刊物:CPCI-S、CPCI-SSH
卷号:47
期号:3
页面范围:1272-1277
摘要:Based on the proportional-integral (PI) closed-loop control widely used in industrial engineering practice, a robust iterative learning control (ILC) method is proposed for industrial batch processes subject to time-varying uncertainties and load disturbance. An important merit is that the proposed ILC design is independent of the PI tuning which maintains the closed-loop system stability, owing to that the ILC updating law is implemented through adjusting the set-point of the closed-loop system and adding a feedforward control signal to the plant input along the batch-to-batch direction. Using the robust H infinity control objective, a robust discrete-time PI tuning algorithm is given in terms of the plant state-space model description with norm-bounded time-varying uncertainties For the batch-to-batch direction, a robust ILC updating law is developed based on the two-dimensional (2D) control system theory, which is capable of perfect output tracking against repetitive type load disturbance. An illustrative example from the literature is adopted to demonstrate the effectiveness and merits of the proposed ILC method.