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iterative learning control for Pareto Optimal Tracking in Incompatible Multisensor Systems

IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1093-1106页

作者： Zhang, Zhenfa Shen, Dong Yu, Xinghuo Renmin Univ China Sch Math Beijing 100872 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia

In a multisensor system, each sensor typically requires independent reference tracking while conflicts arise due to differing desired inputs for different sensors. This scenario presents an exemplary incompatible multiobjective tracking problem (IMOTP), which can be resolved as a multiobjective optimization problem (MOOP). We propose an iterative learning control strategy to resolve conflicts between sensors. First, we elaborate on the Pareto optimal solution (POS) set associated with the MOOP. Subsequently, we derive an update direction for Pareto improvement based on gradient-based algorithms for MOOP and establish a learning control algorithm ensuring that each update is a Pareto improvement and converges to a POS. These technical advancements effectively overcome tracking conflicts in multisensor systems. Illustrative simulations are provided to validate the theoretical results.

关键词： Multisensor systems Robot sensing systems Optimization Vectors Convergence Pareto optimization Indexes iterative learning control Trajectory Sensor systems Iteration learning control multiobjective optimization problem (MOOP) multiobjective tracking problem (MOTP) multisensor systems Pareto improvement Pareto optimal solution (POS)

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Distributed robust iterative learning control for AC microgrids with external disturbances

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SCIENCE CHINA-INFORMATION SCIENCES 2025年第10期68卷 1-21页

作者： Su, Hongyu Lai, Jingang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This study proposes a distributed secondary control scheme based on distributed robust iterative learning control (DRILC) for islanded alternating current (AC) microgrids subjected to external disturbances. By employing the decoupled sliding mode consensus approach, voltage regulation, frequency restoration, and accurate active power sharing can be achieved within a finite time on the proposed novel integral terminal sliding mode (ITSM) manifold. Furthermore, the appropriate iterative update law in the ITSM-based controller is utilized to learn and eliminate external disturbances as effectively as possible. In the proposed control scheme, the iterative learning control and sliding mode control are designed to function in a complementary manner, which enhances the performance of the secondary control scheme for multi-objective regulations. The stability criteria and robustness to external disturbances of the closed-loop microgrid system in the iteration and time domains are also rigorously derived with the help of the Lyapunov direct method. Finally, the effectiveness of the proposed secondary control scheme is validated by case studies of an islanded AC microgrid test system in the MATLAB/SimPowerSystems software environment.

关键词： distributed secondary control islanded microgrid iterative learning control sliding mode control external disturbances

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Robust optimal iterative learning control for constrained batch processes with nonuniform batch lengths

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年

作者： Ding, Jian Gao, Kaihua Ali, Husnain Gao, Furong Wuxi Inst Technol Sch Control Engn Wuxi Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

In practical applications, the durations of operations in uncertain batch processes may fluctuate due to security considerations, physical constraints, and environmental changes. Regarding this issue, this paper addresses iterative learning control (ILC) for a class of uncertain single-input single-output (SISO) batch processes with nonuniform batch lengths. The aim is to develop an optimization-based ILC scheme that guarantees robustness while meeting input constraints requirements. Two robust optimal ILC algorithms resting upon the modified error update model are proposed for the nonuniform batch length problem, where the linear matrix inequality (LMI) optimization techniques are employed to deal with polytopic and ellipsoidal model uncertainties. Hence, the ILC design requirements are reduced to a convex optimization method involving LMIs, which results in iterative input update signals at the end of each batch. Also, the theoretical analyses of the proposed algorithms are presented. Finally, two numerical cases for batch processes are simulated to testify the feasibility and performance of the proposed methods.

关键词： input constraint iterative learning control linear matrix inequality model uncertainty nonuniform batch length

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A Multistage Update Rule Framework for iterative learning control Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 1697-1713页

作者： Li, Zihan Shen, Dong Yu, Xinghuo Renmin Univ China Sch Math Beijing 100872 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia

The proportional type update rule (PTUR) is the most widely used iterative learning control (ILC) scheme. Recently, a fractional-power type update rule (FTUR) was proposed to accelerate PTUR. However, PTUR and FTUR converge slowly for small and large tracking errors, respectively. In this study, a multistage update rule (MSUR) is designed to accelerate PTUR and FTUR along the whole iteration axis. Under the proposed switching mechanism, PTUR and FTUR are adopted for large and small errors for fast convergence, and then PTUR is applied for zero-error tracking. The convergence of MSUR is proved by the analysis of a nonlinear recursion with perturbation. Moreover, for system information that is unknown, an extended MSUR is presented, and its zero-error convergence is proved. In addition, we discuss the influence of the parameters in MSUR on the convergence rate and propose a set of parameter selection rules to maximize the convergence rate of MSUR. Meanwhile, variable-gain and variable-power MSURs are designed to further accelerate the MSUR that only has a single gain and fractional power. Numerical simulations and experimental test verify the theoretical results. Note to Practitioners-Many engineering systems, including high-speed trains, earth-orbiting satellites, and robotic arms, complete a given tracking task over a finite time interval repeatedly. Fast convergence rate and high tracking precision are key technical requirements in these applications. iterative learning control (ILC) has been shown as an effective control method for these tasks. However, the widely-used proportional-type update rule (PTUR) and fractional-power type update rule (FTUR) in ILC cannot meet the abovementioned requirements well. PTUR converges fast for large tracking errors but slow for small tracking errors;in contrast, FTUR delivers fast convergence rate for small tracking errors but converges slow for large errors without achieving zero-error. Combining advantages of both PTUR and FTU

关键词： Multistage update rule iterative learning control nonlinear recursion variable learning parameters

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Piecewise iterative learning control for linear motors under random initial position

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第5期362卷

作者： Cao, Wei Qiao, Jinjie Qiqihar Univ Coll Comp & Control Engn Qiqihar 161006 Peoples R China Qiqihar Univ Coll Econ & Management Qiqihar 161006 Peoples R China

A piecewise iterative learning control algorithm for suppressing random initial position deviation is proposed for a permanent magnet linear synchronous motor that performs repetitive tasks in a finite time interval. This algorithm is divided into two time intervals. In the first time interval, the control algorithm with initial error correction law is used to suppress the effect of random initial deviation on tracking performance. In the second time interval, only the second derivative of the tracking error is used to correct the control input, so that the system output can accurately track the desired output. At the same time, the right boundary of the first time interval, that is, the left boundary of the second time interval, gradually shifts to the left with the increase of the number of iterations. So that the first time interval that can not track the desired output is gradually shortened, and the second time interval of accurately tracking the desired output is gradually widened, eventually achieving the complete tracking of the desired output in the whole time interval when the number of iterations tends to infinity. Furthermore, the convergence of the proposed algorithm is proved by the compression mapping method, and the convergence condition of the algorithm is presented. Both theoretical and simulation results are given to demonstrate the effectiveness of the proposed algorithm.

关键词： Linear motor iterative learning control Random initial position Initial position correction Position tracking

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Event-Triggered Data-Driven iterative learning control for Nonlinear MASs Under Switching Topologies

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第4期33卷 1322-1332页

作者： Sun, Shanshan Li, Yuan-Xin Hou, Zhongsheng Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China

This article aims to address the problem of distributed model-free adaptive iterative learning control for nonlinear discrete-time multiagent systems under switching topologies. To save valuable bandwidth in the wireless channel without sacrificing system performance, an event-triggered iterative learning control strategy is established and employed, where information is only transmitted at triggered instants. First, by virtue of the dynamic linearization technology, the controlled system can be converted into a linear model to construct the controller structure. Second, a model-free adaptive iterative learning consensus control scheme is proposed merely employing the input and output data, in which better tracking performance can be attained by learning the previous experience. Third, a dynamic event-triggered mechanism along the iteration domain is set up to deal with the limited bandwidth issue, effectively saving communication resources. Unlike most model-free adaptive control results, the constructed distributed controller is designed based on controller-dynamic-linearization approach to deal with the controller structure design issue without designing the cost function, making it more convenient in solving tracking control issues for multiagent systems under iteration-switching communication topologies, which is more suitable for the actual environment. Using graph theory and the contraction mapping principle, the convergence of tracking control errors is theoretically analyzed. Ultimately, the effectiveness of the established control schemes is illustrated through two simulation examples.

关键词： Event detection Topology control systems Switches Adaptation models Heuristic algorithms Consensus control Bandwidth Network topology Stability analysis controller-based dynamic linearization method data-driven control event-triggered control iterative learning control multiagent systems (MASs) switching communication topologies

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Boundary adaptive iterative learning control for flexible satellite system with input constraint

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NONLINEAR DYNAMICS 2025年第7期113卷 6907-6921页

作者： Zhao, Zhibo Yuan, Yuan Luo, Biao Xu, Xiaodong Huang, Tingwen Gui, Weihua Cent South Univ Sch Automat Changsha Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha Peoples R China Texas A&M Univ Qatar Dept Sci Doha Qatar

This paper is concerned with the constrained vibration control problem of a flexible satellite system in the presence of unknown external disturbances. Therein, the dynamic model of satellite system is governed as a high order partial differential equation. Unknown disturbances directly result that existing approaches fail in solving the precise control problem for the considered system. To this end, this paper proposes a boundary adaptive iterative learning controller. During the design of the control law, in response to the input constraint which is described by a saturation function model, a hyperbolic tangent function is employed. Novel adaptive learning laws are proposed to learn the upper bounds of disturbances exerted at the solar panels and the centerbody. The closed-loop stability is theoretically proved through a Lyapunov-Krasovskii composite energy function. Numerical simulations are provided to represent the effectiveness of the proposed control method.

关键词： Adaptive control iterative learning control Flexible satellite Input constraint

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Compensatory Data-Driven Networked iterative learning control With Communication Constraints and DoS Attacks

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10728-10740页

作者： Zhang, Huimin Chi, Ronghu Huang, Biao Hou, Zhongsheng Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

Considering the three critical factors of data quantization, channel fading, and denial of service (DoS) attack introduced by the networked control systems (NCSs) simultaneously, we propose a novel compensatory data-driven networked iterative learning control (COMP-DDNILC) method for nonlinear repetitive NCSs under a model-free design and analysis framework. By reformulating the iterative input-and-output (I/O) dynamics of the nonlinear NCS as an iterative linear data model (iLDM), an iterative linear predictive data model (iLPDM) is developed to predict the missing data arisen from DoS attacks. Then, a relationship is built to describe the coupling effects of the three critical factors, based on which the COMP-DDNILC is designed by involving the compensatory mechanism of DoS attacks and the fading coefficient inversion to improve the control performance. The COMP-DDNILC also involves an iterative adaption mechanism to update the iLPDM to enhance the robustness against uncertainties. The data-driven nature of COMP-DDNILC makes it applicable to practical NCSs without model information available. The simulation study verifies the results. Note to Practitioners-Networked control systems (NCSs) have becoming an important topic in practical processes owing to the shareable communication among the sensors, actuators, and controllers. However, the communication constraint and the cyber attack are inevitably induced to NCSs where the former is mainly involved with finite channel capacity and channel fading. Among different cyber attacks, denial of service (DoS) attack is most common and reachable. Further, it is worth noting that data quantization is an effective method to deal with channel capacity, but may degrade control performance by compressing I/O data. Therefore, we are motivated to propose a compensatory data-driven networked iterative learning control method by considering the three factors of data quantization, channel fading and DoS attack simultaneously to comp

关键词： Fading channels Quantization (signal) Predictive models Data models Mathematical models Cyberattack Automation Vehicle dynamics iterative learning control Costs Networked iterative learning control data quantization channel fading DoS attack data-driven design and analysis

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Dual-channel event-triggered model-free adaptive iterative learning control under DoS attacks

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年第2期56卷 363-374页

作者： Zhang, Yaoyuan Bu, Xuhui Yin, Yanling Liang, Jiaqi Henan Polytech Univ Sch Elect Engn & Automat Henan Key Lab Intelligent Detect & Control Coal Mi 2001 Century Ave Jiaozuo Henan Peoples R China Henan Polytech Univ Sch Business Adm Jiaozuo Peoples R China

This paper investigates a dual-channel event-triggered model-free adaptive iterative learning control problem for unknown nonlinear systems under periodic denial-of-service (DoS) attacks. Firstly, periodic DoS attacks on the measured output of the system are modelled using the Bernoulli distribution. Then, to minimise the utilisation of system bandwidth resources, a dual-channel event-triggered mechanism is designed for the sensor-to-controller and controller-to-actuator channels. Subsequently, a dual-channel event-triggered model-free adaptive iterative learning control algorithm is introduced. The convergence of the tracking error is proven in terms of mathematical expectation using Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is verified through two simulation cases.

关键词： iterative learning control model-free adaptive control event-triggered control

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Model-free tool path modification in ultra-precision diamond turning of freeform surfaces using iterative learning control

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PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2025年 94卷 736-748页

作者： Zhang, Xinquan Wu, Hao Yu, Yangqin Jia, Zelong Wang, Xiangyuan Ren, Mingjun Zhu, Limin Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

With the aid of the commercial ultra-precision machine tool, the tool servo diamond turning process provides stable and deterministic material removal, meeting the demands for mass production of high-end freeform surface optics. However, in relatively high-speed applications, the machining accuracy is limited by the heavy servo axes, even within the working bandwidth of-3 dB. Therefore, to facilitate the industrial adoption of diamond turning, a cost-effective and user-friendly programming strategy is essential for enhanced motion accuracy in commercial machine tools. This work proposes a model-free tool path modification strategy using iterative learning control (ILC), which adjusts tool path amplitude iteratively based on the error data of servo axes. By aligning the geometry-based tool path with the dynamic properties of the servo axes, such adjustments reduce tracking errors caused by frequency-based phase lag and amplitude variation effects in high-speed applications. Additionally, this strategy eliminates the need for additional complex equipment or model identification, making it well-suited for industrial applications. The fundamental principle of the proposed method is first presented, followed by a demonstration of its convergence. A series of validation experiments are conducted through trajectory tracking and diamond turning. Experimental results indicate that trajectory tracking achieves a reduction of approximately 60 % in peak-to-valley error and about 80 % in root-mean-square error with the proposed strategy. For diamond turning experiments on sinusoidal grid surfaces, the form error is significantly reduced from 903 nm to 527 nm. Further experiments confirm the long-term effectiveness of the ILC-based tool path modification strategy in high-speed applications, offering valuable insights for industrial use.

关键词： Ultra-precision diamond turning iterative learning control Tool path modification Freeform surface

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