Journal Article10.1109/tcyb.2022.3232136
Data-Driven Indirect Iterative Learning Control
01 Jan 2023
pp 1-11
11
TL;DR: In this article , a data-driven indirect iterative learning control (DD-iILC) is presented for a repetitive nonlinear system by taking a proportional-integral-derivative (PID) feedback control in the inner loop.
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Abstract: In this work, a data-driven indirect iterative learning control (DD-iILC) is presented for a repetitive nonlinear system by taking a proportional-integral-derivative (PID) feedback control in the inner loop. A linear parametric iterative tuning algorithm for the set-point is developed from an ideal nonlinear learning function that exists in theory by utilizing an iterative dynamic linearization (IDL) technique. Then, an adaptive iterative updating strategy of the parameter in the linear parametric set-point iterative tuning law is presented by optimizing an objective function for the controlled system. Since the system considered is nonlinear and nonaffine with no available model information, the IDL technique is also used along with a strategy similar to the parameter adaptive iterative learning law. Finally, the entire DD-iILC scheme is completed by incorporating the local PID controller. The convergence is proved by applying contraction mapping and mathematical induction. The theoretical results are verified by simulations on a numerical example and a permanent magnet linear motor example.
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Citations
Data-Driven Event-Triggered Adaptive Dynamic Programming Control for Nonlinear Systems With Input Saturation.
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Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems
Yunfeng Hu,Chong Zhang,Bo Wang,Jing Zhao,Xun Gong,Jin-wu Gao,Hong Chen +6 more
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ESO-Based Data Driven Set-Point Learning Control for Nonlinear batch Processes with PD-Type Feedback Control Structure Subject to Nonrepetitive Uncertainties*
Naseem Ahmad,Shoulin Hao,Tao Liu,Yihui Gong,Jiyan Zhang,Haixia Wang,Yong Zhu +6 more
- 17 May 2024
TL;DR: This paper proposes an ESO-based data-driven set-point learning control scheme for nonlinear batch processes with PD-type feedback control, actively suppressing nonrepetitive uncertainties and nonidentical initial resetting conditions using iterative dynamic linearization and adaptive learning gain.
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References
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Controller-Dynamic-Linearization-Based Model Free Adaptive Control for Discrete-Time Nonlinear Systems
Zhongsheng Hou,Yuanming Zhu +1 more
TL;DR: A new type of model free adaptive control (MFAC) method, including MFAC scheme designs with the compact-form-dynamic-linearization-based controller (CFDLc) and partial-forms-of-magnification-based- controller (PFDLc), is presented for a class of discrete-time SISO nonlinear systems.
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Data-Driven Flotation Industrial Process Operational Optimal Control Based on Reinforcement Learning
TL;DR: A new model-free data-driven method is developed here for real-time solution of the operational optimal control problem for the industrial flotation process, and optimal controls are learned online in real time using a novel form of reinforcement learning the authors call interleaved learning for online computation of the Operational optimal control solution.
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