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Data-driven iterative feedforward control with rational parametrization: Achieving optimality for varying tasks
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DOI：10.1016/j.jfranklin.2019.06.002.png)
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In precision motion systems, well-designed feedforward control can effectively compensate for the reference-induced error. This paper aims to develop a novel data-driven iterative feedforward control approach for precision motion systems that execute varying reference tasks. The feedforward controller is parameterized with the rational basis functions, and the optimal parameters are sought to be solved through minimizing the tracking error. The key difficulty associated with the rational parametrization lies in the non-convexity of the parameter optimization problem. Hence, a new iterative parameter optimization algorithm is proposed such that the controller parameters can be optimally solved based on measured data only in each task irrespective of reference variations. Two simulation cases are presented to illustrate the enhanced performance of the proposed approach for varying tasks compared to preexisting results. (C) 2019 Published by Elsevier Ltd on behalf of The Franklin Institute.
Keyword:
LEARNING CONTROL
NONLINEAR-SYSTEMS
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