Article
Automation & Control Systems
Lanlan Su
Summary: The study focuses on robust monotonic convergent iterative learning control for uncertain linear systems, deriving an ILC algorithm that optimizes convergence speed. It establishes robust monotonic convergence through the positive definiteness of a matrix polynomial and proposes a necessary and sufficient condition in the form of sum of squares for positive definiteness, amendable to linear matrix inequalities. The optimal ILC algorithm maximizing convergence speed is obtained by solving a set of convex optimization problems, allowing flexibility in choosing the order of the learning function for algorithm complexity.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Yan Liu, Xiaoe Ruan
Summary: This paper develops a parameter-optimal iterative learning control (POILC) scheme for linear discrete-time-invariant systems with Markov parameters, and rigorously analyzes its robustness to system parameter uncertainties. Numerical simulations validate the effectiveness of the proposed approach.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Nard Strijbosch, Tom Oomen
Summary: In this paper, a computationally tractable ILC framework is developed to exploit intermittent data while maintaining favorable properties, including monotonic convergence. The framework utilizes controllability and observability analysis to determine the appropriate conditions for monotonic convergence, even in the presence of missing data. An explicit ILC controller design independent of the sampling instances, resembling gradient-descent ILC, is proposed and demonstrated on intuitive and practical examples.
Article
Engineering, Multidisciplinary
Xiaoxin Yang, Saleem Riaz
Summary: An iterative learning control algorithm based on error backward association and control parameter correction is proposed for linear discrete time-invariant systems with repeated operation characteristics, parameter disturbance, and measurement noise. The algorithm's convergence is analyzed and compared with the traditional PD algorithm through theoretical proof and simulation results.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2022)
Article
Automation & Control Systems
Tae-Yong Don, Jung Rae Ryoo
Summary: Iterative learning control (ILC) combined with a feedback control system improves tracking performance by iteratively tuning the feedforward control signal based on system information. This paper proposes a method for designing an add-on-type robust iterative learning controller for an uncertain feedback control system, using explicit tracking-performance and plant-uncertainty information. The proposed ILC system consists of two learning controllers, one obtained from the inverse of the nominal feedback control system, and the other a Q-filter ensuring robustness for convergence under uncertainty.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Mathematics
Yan Geng, Shouqin Wang, Xiaoe Ruan
Summary: This paper proposes a data-driven optimal iterative learning control method for multi-phase batch processes. By estimating the system parameters using input-output data and embedding them into the learning control process, the tracking performance is optimized.
Article
Automation & Control Systems
Yuanqiang Zhou, Kaihua Gao, Xiaopeng Tang, Huanjia Hu, Dewei Li, Furong Gao
Summary: In this article, the optimal iterative learning control for constrained systems with bounded uncertainties is studied. A novel conic input mapping design methodology, called CIM, is proposed to utilize the process data for better control performance and faster convergence rate.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Krzysztof Patan, Maciej Patan
Summary: This paper discusses the design of neural-network-based iterative learning control for non-linear systems in a fault tolerant control regime. By utilizing the repetitive nature of the control task, the uncertainty associated with potential faulty system states can be accommodated through a data-driven iterative learning scheme using neural networks. The resulting flexible control technique accurately compensates for faults in both sensors and actuators, and also considers disturbances and noise acting on the system. The paper provides a complete characterization of the fault-tolerant iterative learning scheme, including system identification, fault detection and accommodation. Furthermore, convergence analysis is presented, and sufficient conditions are determined for updating the control law in consecutive process trials. The excellent performance of the developed control scheme is demonstrated through a nontrivial example involving actuator and/or sensor faults in a magnetic brake system.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Xiaodong Sun, Liyun Feng, Zhen Zhu, Gang Lei, Kaikai Diao, Youguang Guo, Jianguo Zhu
Summary: This article presents a nonsingular terminal sliding mode controller (NTSMC) based on direct torque control for a switched reluctance motor (SRM). The proposed NTSMC utilizes an improved reaching law to guarantee dynamic stability and suppress torque ripple. The grey wolf optimization algorithm is applied to automatically adjust the controller parameters, resulting in satisfactory performance.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Automation & Control Systems
Lixun Huang, Lijun Sun, Tao Wang, Qiuwen Zhang, Jianyong Li, Zhe Zhang, Weihua Liu
Summary: This article investigates the convergence performance of wireless networked iterative learning control (ILC) systems under data dropouts and channel noises in both sensor-to-controller and controller-to-actuator channels. In order to improve the convergence performance, an optimal input filter is developed at the actuator side to estimate the controller updated input with the effect of network uncertainties. The convergence performance of the filtering error covariance matrix is analyzed theoretically and simulation results demonstrate the effectiveness of the proposed filtering method.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Huiping Liang, Chunhua Yang, Mingjie Lv, Bei Sun, Yonggang Li
Summary: This paper presents a PI-type adaptive iterative learning control (PI-AILC) method for enhancing system tracking capabilities in nonlinear processes by adapting setpoints of the PI controller. The method uses dynamic linearization technology to obtain a local linear representation of unknown nonlinear systems and adaptively updates the iterative learning controller gain using this representation to ensure optimality. A pre-learning mechanism for offline data is also introduced to further improve efficiency. Strict convergence for PI-AILC is proven and experimental results validate its effectiveness.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Information Systems
Ijaz Hussain, Xiaoe Ruan, Chen Liu, Yan Liu
Summary: This article focuses on the modeling of repetitive finite-length linear discrete-time singular systems and the optimization of gain for iterative learning control. By adjusting the learning gain vector in minimizing the norm of tracking error and compensation vector, the linearly monotonic convergence of tracking error is derived. A robust quasi-scheme is proposed for addressing system parameter uncertainties.
Article
Computer Science, Interdisciplinary Applications
Dongjie Chen, Ying Xu, Tiantian Lu, Guojun Li
Summary: This paper presents a multi-phase iterative learning control strategy for second-order tracking systems with arbitrary initial shifts. The strategy ensures system stability and stable output through the selection of appropriate control gain, and rectifies fixed shifts using two proposed methods. Theoretical analysis demonstrates the strategy's ability to achieve complete tracking.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2024)
Article
Automation & Control Systems
Weicai Huang, Kaiming Yang, Yu Zhu, Sen Lu
Summary: This paper presents a new data-driven feedforward tuning approach associated with rational basis functions to obtain the global optimum with optimal estimation accuracy. Experimental results validate the excellent performance of the proposed approach for varying tasks.
IET CONTROL THEORY AND APPLICATIONS
(2021)
Article
Automation & Control Systems
Deyuan Meng
Summary: This article proposes an observer-based iterative method to bring control design into mathematics for solving linear algebraic equations (LAEs). The relationship between solving LAEs and designing observer-based control systems is revealed, and an iterative method for solving LAEs is developed based on the design of basic state observers. Different selections of initial conditions can determine the (least squares) solutions for any (un)solvable LAEs exponentially fast or monotonically. The general solution subspace and particular (least squares) solutions of LAEs are closely related to the unobservable subspace and observable states of their associated observer systems, respectively. By incorporating the design idea of deadbeat control, the solving of LAEs can be achieved within finite iterations. The proposed iterative method can be used to develop a new observer-based design algorithm for traditional two-dimensional iterative learning control to achieve perfect tracking tasks.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
