Data-driven predictive point-to-point iterative learning control

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 repetitive non-affine nonlinear SISO systems. The tracking task is driven by the optimal cont...

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Vydáno v:	Neurocomputing (Amsterdam) Ročník 518; s. 431 - 439
Hlavní autoři:	Zhang, Xueming, Hou, Zhongsheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 21.01.2023
Témata:	Data-driven control (DDC) Point-to-point iterative learning control (PTPILC) Predictive control Repetitive non-affine nonlinear systems Point-to-point iterative learning control (PTPILC) Data-driven control (DDC) Repetitive non-affine nonlinear systems Predictive control
ISSN:	0925-2312, 1872-8286
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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 repetitive 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 proposed scheme does not involve the operation of matrix inversion. Numerical simulations verify the effectiveness of this method.
ISSN:	0925-2312 1872-8286
DOI:	10.1016/j.neucom.2022.11.014