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Data-Driven Reference Trajectory Tracking Algorithm and Experimental Validation
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DOI：10.1109/TII.2012.2220973.png)
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Abstract
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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.
Keywords:
Data-driven reference trajectory tracking
experimental results
iterative feedback tuning
iterative learning control
lifted form representation
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