Data-Driven Reference Trajectory Tracking Algorithm and Experimental Validation

This paper proposes a data-driven algorithm that solves a reference trajectory tracking problem defined as an optimization problem. The new data-driven reference trajectory tracking algorithm (DDRTTA) solves the optimization problem in the framework of iterative learning control (ILC). The DDRTTA up...

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Bibliographic Details
Published inIEEE transactions on industrial informatics Vol. 9; no. 4; pp. 2327 - 2336
Main Authors Radac, Mircea-Bogdan, Precup, Radu-Emil, Petriu, Emil M., Preitl, Stefan, Dragos, Claudia-Adina
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.11.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithm design and analysis
Data models
Data-driven reference trajectory tracking
experimental results
iterative feedback tuning
iterative learning control
Iterative methods
lifted form representation
Optimization
Tracking
Trajectory
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Summary:This paper proposes a data-driven algorithm that solves a reference trajectory tracking problem defined as an optimization problem. The new data-driven reference trajectory tracking algorithm (DDRTTA) solves the optimization problem in the framework of iterative learning control (ILC). The DDRTTA updates the reference input sequence using an experiment-based approach which accounts for operational constraints and employs an interior point barrier algorithm. Therefore the DDRTTA combines the advantages of data-driven control and ILC. A case study which deals with the angular position control of a nonlinear servo system is included to validate the DDRTTA by experimental and simulation results.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2012.2220973