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Sampled-Data Adaptive Iterative Learning Control for Uncertain Nonlinear Systems
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DOI：10.1109/TSMC.2024.3373588.png)
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Abstract
En 中文
In the realm of data-driven adaptive iterative learning control (AILC), the emphasis in designing and analyzing control schemes mainly concentrates on discrete-time systems, while fewer results are developed for the more common continuous-time plants. To overcome this limitation, a practical sampled-data AILC (SDAILC) is developed for continuous-time nonaffine nonlinear plants. A sampled-data iterative dynamic linearization (SDIDL) method is devised to build the dynamic connection between input and output (I/O) data throughout different iterations. On this basis, the SDAILC method, including a sampled-data parameter estimation algorithm and a learning control law, is proposed by utilizing optimization-based design. In SDAILC, the sampling period is treated as a parameter to compensate for its influence on the control performance, and an error feedback is naturally involved, improving the robustness against uncertainties and the closed-loop stability of the plant. Notably, SDAILC is a data-driven approach independent of model information. The validity of SDAILC is proved mathematically and demonstrated by simulations.
Keywords:
Data-driven control
iterative learning control (ILC)
nonaffine nonlinear system
sampled-data control
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W