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 in:IEEE transactions on industrial informatics 2013-11, Vol.9 (4), p.2327-2336
Main Authors: Radac, Mircea-Bogdan, Precup, Radu-Emil, Petriu, Emil M., Preitl, Stefan, Dragos, Claudia-Adina
Format: Article
Language:English
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