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Controller-Dynamic-Linearization-Based Data-Driven ILC for Nonlinear Discrete-Time Systems With RBFNN
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DOI：10.1109/TSMC.2021.3110790.png)
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Abstract
En 中文
In this article a novel data-driven iterative learning control (ILC) approach is proposed for unknown nonlinear nonaffine repetitive discrete-time systems, where the dynamic linearization (DL) technique in the iteration domain is applied both on the controlled nonlinear system and on the unknown nonlinear ideal learning controller. Through updating the weight matrix of a radial basis function neural network (RBFNN), the learning control gain of the obtained iterative learning law is automatically tuned in reaching the optimal learning controller using only the input-output data of the nonlinear system. The uniformly ultimately bounded property is established for the tracking error of the proposed ILC scheme in the iteration domain through rigorous theoretical analysis. The effectiveness and applicability are validated by a simulation example and further demonstrated by simulation on a high-speed train model.
Keywords:
Control systems
Data models
Systematics
Nonlinear dynamical systems
Discrete-time systems
Trajectory
Time-domain analysis
Data-driven iterative learning control (ILC)
dynamic linearization (DL)
nonlinear repetitive systems
radial basis function neural network (RBFNN)
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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