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Data-driven predictive point-to-point iterative learning control
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DOI：10.1016/j.neucom.2022.11.014.png)
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摘要
En 中文
Integrating the idea of predictive control and point-to-point iterative learning control, this paper presents a data-driven predictive point-to-point iterative learning control scheme for a class of unknown repeti-tive non-affine nonlinear SISO systems. The tracking task is driven by the optimal control input sequence generated by the proposed algorithm, and the tracking errors at the specified sampling time instants are minimized. The advantages of this scheme are that the structure of the controller and its stability analysis both are based on an equivalent dynamic linearization data model of the nonlinear system, and the pro-posed scheme does not involve the operation of matrix inversion. Numerical simulations verify the effec-tiveness of this method.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Predictive control
Point-to-point iterative learning control
(PTPILC)
Data-driven control (DDC)
Repetitive non-affine nonlinear systems
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
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