Article
Multidisciplinary Sciences
Junjian Zhao, Marko Kostic, Wei-Shih Du
Summary: This paper establishes generalized sampling theorems, stability theorems, and new inequalities in shift-invariant subspaces of Lebesgue and Wiener amalgam spaces. It also provides a convergence theorem for general iteration algorithms for sampling in certain shift-invariant subspaces of L-(p) over right arrow(R-d).
Article
Automation & Control Systems
Xingding Zhao, Youqing Wang
Summary: This article studies and solves the problem of point-to-point iterative learning control (P2PILC), and proposes a learning law to compensate the initial state error and validates its effectiveness.
Article
Engineering, Mechanical
Zhou Fengyu, Wang Yugang
Summary: This study applies closed-loop D-alpha-type iterative learning control with a proportional D-type updating law to address initial shift in nonlinear conformable fractional order systems. Frameworks for fractional order ILC experiencing initial shift problem in path tracking are discussed. Sufficient condition for convergence of tracking errors in time domain is obtained by introducing -norm and Holder's inequality. Numerical examples demonstrate effectiveness of the proposed methods.
NONLINEAR DYNAMICS
(2021)
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
Yong-Hong Lan, Bin Wu, Yi-Ping Luo
Summary: This paper proposes a PI-type iterative learning control law with initial state learning for a class of alpha (0 < alpha <= 1) fractional order linear systems. The convergence analysis of the iterative scheme is proved and a new convergence condition is derived. Simulation results demonstrate the effectiveness of the proposed control method.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chao Yuan, Liming Yang
Summary: A novel robust least squares twin support vector machine framework CL2,p-LSTSVM is proposed in this paper for binary classification, which utilizes capped L-2,L-p-norm distance metric to reduce the influence of noise and outliers. The goal is to minimize intra-class distance dispersion and eliminate the influence of outliers during training process, ensuring better robustness through the control of the capped parameter value.
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
Engineering, Chemical
Sheng Pei, Zhonggai Zhao, Fei Liu, Maodong Miao, Sai Jin, Fuxin Sun, Guiyang Shi
Summary: The tricalcium neutralization process (TNP) is an important recovery process in citric acid production. The addition of calcium carbonate needs to be controlled to maintain the desired pH range. This study proposes an iterative learning control (ILC)-based strategy to optimize calcium carbonate addition. Experimental results show that the suggested control strategy effectively suppresses disturbances and achieves terminal pH control in the TNP process.
CHEMICAL ENGINEERING & TECHNOLOGY
(2023)
Article
Automation & Control Systems
Lei Li
Summary: This paper investigates the monotonic convergence and speed comparison of first-and second-order proportional-a-order-integral-derivative-type ((PID)-D-a-type) iterative learning control (ILC) schemes for a linear time-invariant (LTI) system, which is governed by the fractional differential equation with order a ? (1, 2). The sufficient condition for the monotonic convergence of the first-order updating law is analyzed by introducing the Lebesgue-p (L-P) norm and utilizing the property of the Mittag-Leffler function and the boundedness feature of the fractional integration operator. The same means are used to establish the sufficient condition of the second-order learning law.
ASIAN JOURNAL OF CONTROL
(2023)
Article
Chemistry, Multidisciplinary
Shaozhe Liu, Zuojun Liu, Jie Zhang, Dong Hu
Summary: Iterative learning control (ILC) requires consistent operating conditions for effective learning, and any changes in system parameters may necessitate restarting the learning process. By adjusting previous ILC experiences to fit new system parameters, time and resources can be saved in the new learning process.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
Ridong Zhang, Furong Gao
Summary: A novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed in this study to achieve improved control performance of batch processes under uncertainty. By combining the advantages of a new two-dimensional extended model and error compensation strategy, the 2D-MPILC gradually eliminates the effects caused by uncertainty in batch processes, leading to the desired control performance improvement.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Min Wang, Liang Cao, Hongjing Liang, Wenbin Xiao
Summary: This paper investigates the problem of bipartite consensus tracking control with full-state constraints for p-norm multiagent systems. To address the full-state constraints problem, a transformed function is utilized in the controllers to achieve the objective of the constraints, which avoids the use of log-type functions or trigonometric functions and reduces complexity. Additionally, a compensation strategy is introduced to improve the accuracy of the tracking performance in p-norm multiagent systems under the simplified reinforcement learning framework.
Article
Computer Science, Artificial Intelligence
Zimian Lan
Summary: This paper presents a new iterative learning control algorithm for sensor faults in nonlinear systems, which corrects system state deviation and ensures convergence of tracking error to a predetermined boundary. The algorithm is proven effective through simulation results on speed control of an injection molding machine system.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Guojun Li, Tiantian Lu, Yishi Han, Zhijiang Xu
Summary: This paper proposes different initial state shifts rectifying schemes for high-order nonlinear systems to solve the iterative learning control problem with arbitrary initial state error. The theoretical analysis shows that the proposed schemes can bound all signals in the system and reduce the tracking error to zero. Simulation results validate the effectiveness of the proposed algorithms.
Article
Automation & Control Systems
Lixun Huang, Xinyang Guo, Lijun Sun, Qiuwen Zhang, Weihua Liu, Zhe Zhang
Summary: Instead of designing new ILC controllers, this paper focuses on designing a filter on the side of objects to calculate the updated input of ILC controllers under the effects of communication delays and noises in both links. The filter is designed based on the orthogonality projection principle using the knowledge of ILC controllers and the developed transmission model. Theoretical analysis and simulation results demonstrate that the calculated input effectively improves the convergence of objects controlled by the P-type ILC controller with communication delays and noises.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
