A Sliding-mode Iterative Learning Control for a Nonlinear Discrete-time System via a Data-driven Design method

In this work, a sliding-mode iterative learning control (SM-ILC) is proposed for a nonlinear and non-affine discrete-time system, which is transferred into a linear incremental input-and-output form equivalently. The unknown parameters in the linear incremental input-and-output model can be estimate...

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Bibliographic Details
Main Authors: Chi, Ronghu, Wang, Rongrong, Wei, Yangchun
Format: Conference Proceeding
Language:English
Subjects:
Control systems
Convergence
Data-driven control
Discrete-time systems
Iterative algorithms
Iterative learning control
Nonlinear discrete-time systems
Simulation
Sliding mode control
Uncertainty
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Description
Summary:In this work, a sliding-mode iterative learning control (SM-ILC) is proposed for a nonlinear and non-affine discrete-time system, which is transferred into a linear incremental input-and-output form equivalently. The unknown parameters in the linear incremental input-and-output model can be estimated by using an iterative project algorithm. The optimal learning control law is developed with a sliding surface defined along the iteration direction. The proposed SM-ILC is a data-driven approach and can achieve a better control performance because much more control knowledge is utilized. Both the convergence analysis and the simulation results have been provided to show the effectiveness of the proposed method.
ISSN:1948-9447
DOI:10.1109/CCDC.2019.8832653