Article
Computer Science, Artificial Intelligence
Mouquan Shen, Xingzheng Wu, Ju H. Park, Yang Yi, Yonghui Sun
Summary: This paper is concerned with iterative learning control of constrained multi-input multi-output nonlinear systems under the state alignment condition. A modified reference trajectory is constructed and an adaptive ILC scheme is built using the barrier composite energy function approach to guarantee the bounded convergence of the closed-loop system. Illustrative examples are provided to verify the effectiveness of the proposed iteration scheme.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Yu Hui, Chiang-Ju Chien, Biao Huang, Zhongsheng Hou
Summary: This article proposes a new convergence analysis method for sampled-data iterative learning control systems, considering LLC nonlinear nonaffine systems and relaxing repetitive conditions. It proves the bounded convergence of tracking error and further investigates the relationship between error bounds and iteration-varying uncertainties and sampling periods.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Genfeng Liu, Zhongsheng Hou
Summary: This article presents an adaptive iterative learning fault-tolerant control algorithm for state constrained nonlinear systems with randomly varying iteration lengths subjected to actuator faults. The modified parameters updating laws and the radial basis function neural network method are used to handle the randomly varying iteration lengths and the time-iteration-dependent unknown nonlinearity, respectively. A new barrier composite energy function is used to achieve the tracking error convergence of the control algorithm along the iteration axis with the state constraint.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Yiyang Chen, Bing Chu, Christopher T. Freeman
Summary: Iterative learning control (ILC) aims to maximize the performance of systems performing repeated tracking tasks. This article extends the ILC task description, proposes a spatial ILC algorithm with efficient implementation and robust convergence analysis, and verifies its performance and practical feasibility through experiments. Comparisons with other control methods highlight advantages such as error reduction, control effort reduction, and constraint handling.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Acoustics
Mojtaba Ayatinia, Mehdi Forouzanfar, Amin Ramezani
Summary: This paper investigates a new sufficient robust convergence condition of iterative learning control with initial state learning in the presence of iteration-varying uncertainty for multivariable systems in the time domain. The proposed method provides fixed learning gains over time and iteration, addressing the issue of tracking error caused by the constant initial state in the basic ILC algorithm.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Automation & Control Systems
Mojtaba Ayatinia, Mehdi Forouzanfar, Amin Ramezani
Summary: This paper presents a new robust convergence condition for linear multivariable discrete-time systems with iteration-varying uncertainty using iterative learning control with initial state learning (ILC-ISL). The proposed method is based on linear matrix inequality (LMI) and provides fixed learning gains during time and iteration. The effectiveness of the approach is demonstrated through numerical examples and a mechanical system.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Mojtaba Ayatinia, Mehdi Forouzanfar, Amin Ramezani
Summary: This paper presents a new robust convergence condition for iterative learning control (ILC) in the presence of iteration-varying uncertainty. The proposed method, based on linear matrix inequality (LMI), provides a fixed learning gain over time and iteration. The effectiveness of the method is evaluated through two numerical examples.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Deyuan Meng, Jingyao Zhang
Summary: This article introduces a system equivalence transformation method for robust iterative learning control, addressing the contradiction in convergence conditions and simplifying the control of system signals while ensuring the convergence of output tracking errors. Simulation examples are provided to validate the established robust ILC results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Huaying Li, Na Lin, Ronghu Chi
Summary: In this paper, an event-triggered P-type iterative learning control (ILC) scheme is developed, with control input update only occurring in the event-triggered iterations. The proposed algorithms ensure convergence and stability by using pre-defined event-triggering conditions, as shown in simulation results that the control input update frequency can be reduced to save resources.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Qiongxia Yu, Zhongsheng Hou
Summary: The article proposes a new adaptive fuzzy iterative learning control (AFILC) method for tracking control of high-speed trains (HST) that have uncertain nonlinear operation systems. The AFILC method can actively manipulate position, speed, and input force of the train to ensure safe operation and minimize tracking control errors over varying time intervals. Simulations on a practical train operation system similar to China Railway High-speed (CRH)-3 train demonstrate the applicability and effectiveness of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Deyuan Meng, Jingyao Zhang
Summary: This paper addresses the problem of trackability in iterative learning control (ILC), introducing the concept of trackability and establishing related criteria. It also explores the relationship between trackability and perfect tracking tasks in ILC, and proposes a new convergence analysis method based on functional Cauchy sequence (FCS). Simulation examples are provided to verify the effectiveness of the presented trackability criteria and FCS-induced convergence analysis method for ILC.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Computer Science, Artificial Intelligence
Xuefang Li, Dong Shen, Beichen Ding
Summary: This paper introduces a novel design framework of adaptive iterative learning control for a class of uncertain nonlinear systems to address the output tracking problem, considering parametric input disturbances and input distribution uncertainties. The use of composite energy function methodology facilitates controller design and convergence analysis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Min Li, Taotao Chen, Rong Cheng, Kaiming Yang, Yu Zhu, Caohui Mao
Summary: The dual-loop iterative learning control (DILC) approach proposed in this article explicitly addresses the design tradeoff between robustness and tracking performance in standard ILC. By adding an additional feedforward signal to eliminate the nonzero asymptotic error caused by the robustness filter, the DILC significantly enhances tracking performance without sacrificing robustness against model uncertainties and disturbances. Application to an ultraprecision wafer stage shows a reduction of 52.7% and 43.9% in peak values of moving average and moving standard deviation of tracking error, respectively.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Civil
Yong Chen, Deqing Huang, Chao Xu, Hairong Dong
Summary: In this paper, an adaptive iterative learning control approach is proposed to address the operation control problem of high-speed trains in the presence of nonlinear parameterized uncertainties and multiple unknown state delays. A multi-particle train model is established to describe the operational dynamics of trains, and the proposed control scheme leverages various techniques to cope with the inherent nonlinearities and uncertainties of the system.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Xu Jin
Summary: This article proposes a new structure of ILC laws to address iteration-varying trial lengths and system uncertainties, showing that traditional ILC problems are a special case of the more general problem considered here. The proposed control scheme guarantees asymptotic convergence over the iteration domain and is demonstrated to be effective through a simulation example.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)