R
Ronghu Chi
Researcher at Qingdao University of Science and Technology
Publications - 169
Citations - 2437
Ronghu Chi is an academic researcher from Qingdao University of Science and Technology. The author has contributed to research in topics: Iterative learning control & Nonlinear system. The author has an hindex of 20, co-authored 139 publications receiving 1499 citations. Previous affiliations of Ronghu Chi include Beijing Jiaotong University & East China University of Science and Technology.
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An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications
TL;DR: This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers.
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Technical communique: Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition
TL;DR: This work presents a discrete-time adaptive iterative learning control scheme to deal with systems with time-varying parametric uncertainties and can incorporate a Recursive Least Squares algorithm, hence the learning gain can be tuned iteratively along the learning axis and pointwisely along the time axis.
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Data-driven optimal terminal iterative learning control
TL;DR: Rigorous analysis and convergence proof are developed with sufficient conditions for the terminal ILC design and the results are developed for both linear and nonlinear discrete-time systems.
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A unified data-driven design framework of optimality-based generalized iterative learning control
TL;DR: A unified design framework for data-driven optimality-based generalized iterative learning control (DDOGILC), including data- driven optimal ILC (DDOILC),Data-driven optimal point-to-point I LC (DDOPTPilC), and data- Driven optimal terminal ILC(DDTILC) is proposed.
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Dual-stage Optimal Iterative Learning Control for Nonlinear Non-affine Discrete-time Systems
TL;DR: In this paper, a dual-stage optimal iterative learning control for nonlinear and non-affine discrete-time systems is presented, where two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively.