期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 21, 期 2, 页码 546-551出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2012.2185699
关键词
Data-driven control; energy-optimal point-to-point motions; iterative learning control (ILC); linear time-invariant (LTI) systems; precision motion control
资金
- Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen) [IWT-SBO 80032 (LECOPRO)]
- Research Foundation-Flanders (FWO-Flanders) [G.0422.08, G.0377.09]
- K.U. Leuven Center-of-Excellence Optimization in Engineering (OPTEC) [BOF PFV/10/002]
- Belgian Programme on Interuniversity Attraction Poles
- Belgian Federal Science Policy Office
This brief presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-to-point motion problems. The key contribution of this brief is the estimation of the system's impulse response using input/output measurements from previous iterations, hereby eliminating time-consuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to: 1) achieve tracking accuracy up to the repeatability of the test setup; 2) minimize the rms value of the tracking error while respecting the actuator input constraints; 3) learn energy-optimal system inputs for point-to-point motions.
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