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Double-Layered Iterative Learning Control for Nonlinear Systems
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AI
DOI：10.1109/TSMC.2024.3417378.png)
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Abstract
En 中文
This work aims at improving the control performance of the iterative learning control through set-point learning along iteration direction. A double-layered learning control mechanism is designed for both the control input and the set-point, respectively. The learning control of the input is regarded as a local controller in the inner layer, and the learning control of the set-point is designed as an auxiliary controller in the outer layer whose design is a main challenge since no any priori knowledge is available to describe the relationship between the set-point and the control performance. To solve this issue, an ideal nonlinear nonaffine set-point learning optimization (SPLO) algorithm is designed by taking the set-point and the tracking error as the arguments. Then, an iterative dynamic linearization (iDL) is introduced to formulate the ideal SPLO algorithm as a linear parametric one whose unknown parameter is estimated by designing a parameter updating algorithm. Further, since a strongly nonlinear and nonaffine system is considered without any model information available, the iDL is also used to derive its equivalent linear data model which is then updated by the input and output data to make the linear parametric SPLO realizable. Finally, a double-layered iterative learning control (DLILC) is proposed under the data-driven framework for tracking an iteration-varying trajectory. Convergence analysis and extensive simulations are included to demonstrate the effectiveness of the presented DLILC.
Keywords:
Heuristic algorithms
Optimization
Trajectory
Iterative learning control
Tracking loops
Iterative algorithms
Data models
Data-driven control
double-layered learning control mechanism
iterative learning control
nonlinear and nonaffine systems
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W