Data-Driven Reference Trajectory Tracking Algorithm and Experimental Validation

doi:10.1109/TII.2012.2220973

Data-Driven Reference Trajectory Tracking Algorithm and Experimental Validation

2013-11-01

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Claudia‐Adina Bojan‐Dragos

DOI：10.1109/TII.2012.2220973

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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.

Keyword:

Data-driven reference trajectory tracking

experimental results

iterative feedback tuning

iterative learning control

lifted form representation

Data-Driven Reference Trajectory Tracking Algorithm and Experimental Validation

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