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Double Dynamic Linearization-Based Higher Order Indirect Adaptive Iterative Learning Control
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DOI：10.1109/TCYB.2021.3125374.png)
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Abstract
En 中文
In this article, a higher order indirect adaptive iterative learning control (HO-iAILC) scheme is developed for nonlinear nonaffine systems. The inner loop adopts a P-type controller whose set-point is updated iteratively by learning from the iterations. To this end, an ideal nonlinear learning control law is designed in the outer loop. It is then transferred to a linear parametric-learning controller with a corresponding parameter estimation law by introducing an iterative dynamic linearization (IDL) method. This IDL method is also used to gain an iterative linear data model of the nonlinear system. A parameter iterative updating algorithm is utilized for estimating the unknown parameters of the obtained linear data model. Finally, the HO-iAILC is presented that utilizes additional error information to improve the control performance and employs two iterative adaptive mechanisms to deal with uncertainties. The convergence of the proposed HO-iAILC scheme is proved by using two basic mathematical tools, namely: 1) contraction mapping and 2) mathematical induction. Simulation studies are conducted for the verification of the theoretical results.
Keywords:
Data models
Convergence
Nonlinear dynamical systems
Adaptive systems
Uncertainty
Learning systems
Parameter estimation
Dynamic linearization
higher order learning law
indirect iterative learning control (iILC)
iterative adaptive mechanism
set-point learning law
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
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