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iterative learning control algorithms combining feedback and difference for batch processes

CHEMICAL ENGINEERING SCIENCE 2025年 304卷

作者： Li, Guojun Lu, Tiantian Fan, Yingsheng Yang, Xue Zhejiang Police Coll Basic Courses Dept Hangzhou 310053 Peoples R China Zhejiang Police Coll Dept Comp & Network Secur Hangzhou 310053 Peoples R China

Two types of iterative learning controllers for batch processes are presented in this paper. The controllers not only contain a feedforward term, but also incorporate a feedback term and a difference term. First, vector lifting technology is employed to describe all the inputs or outputs of a single batch. Second, we utilize matrix inversion and exponentiation operations to obtain the sufficient condition for the monotonic convergence of the error norm, and give the upper bound of the error at any time of each iteration, which is not only limited by the control gains, but also related to the number of iterations and the time. Third, when choosing the control gains, the guidance is given according to the upper bound of the tracking error, and the suboptimal learning gains are yielded by applying the distribution of the matrix eigenvalues. Finally, four examples are applied to verify the effectiveness of the algorithm.

关键词： iterative learning control Feedback Difference Convergence Discrete-time systems

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iterative learning control for Pareto Optimal Tracking in Incompatible Multisensor Systems

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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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iterative learning control of an Euler-Bernoulli beam with time-varying boundary disturbance

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2024年 162卷 145-154页

作者： Wang, Yingying Wu, Wei Lou, Xuyang Goerges, Daniel Jiangnan Univ Sch IoT Engn Wuxi 214122 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Kaiserslautern Dept Elect & Comp Engn D-67663 Kaiserslautern Germany

This paper investigates the stabilization of Euler-Bernoulli beam systems modeled by a partial differential equation (PDE) with unknown time -varying disturbance. An iterative learning controller is designed using only boundary state feedback to realize the vibration control subject to unknown boundary disturbance. The wellposedness for the closed -loop system is given by the operator semigroup theory. Furthermore, the exponentially stable for the closed -loop system is proved by the Lyapunov method. The comparisons with existing results are made to demonstrate the effectiveness and advantages of the proposed boundary iterative learning control method.

关键词： Euler-Bernoulli beam iterative learning control Distributed parameter system Boundary control

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iterative learning control for a Flexible Wing System Under Iteration-Varying Disturbances and References

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第5期60卷 5973-5981页

作者： Meng, Tingting Guo, Bao-Zhu Huang, Haifeng He, Wei Univ Sci & Technol Beijing Minist Educ Inst Artificial Intelligence Key Lab Intelligent Unmanned Syst B Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China North China Elect Power Univ Dept Math & Phys Beijing 102206 Peoples R China Chinese Acad Sci Beijing Peoples R China Acad Sinica Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

In this article, robust iterative learning controls are proposed for a flexible wing with iteration-varying disturbances and references having unknown coefficients. The unknown basis functions of the disturbances and references are supposed to be from an exosystem with unknown initial values. Then, a higher internal model is used to describe the unknown iteration-varying property. A strongly coupled reference model, inspired by the fourth-order ODEs of the Timoshenko beam equation, is firstly proven to admit bounded solutions. In this case, feedforward controls are designed for the flexible wing system, where the iteration-varying input disturbances are divided into unknown constants and known iteration-varying states. By finding two adaption laws, robust iterative learning controls are designed to guarantee the convergence of the tracking errors along the iteration axis, as well as the boundedness of the closed-loop system. Simulation examples are further provided for the robust output regulation of the wing.

关键词： Regulation Mathematical models iterative learning control Eigenvalues and eigenfunctions Aerospace and electronic systems Adaptive control Adaptation models

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iterative learning control with parameter estimation for non-repetitive time-varying systems

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JOURNAL OF THE FRANKLIN INSTITUTE 2024年第3期361卷 1455-1466页

作者： Wang, Lei Huangfu, Ziwei Li, Ruiwen Wen, Xiewen Sun, Yuan Chen, Yiyang Wuxi Univ Sch Automat Wuxi 214105 Peoples R China Anhui Polytech Univ Key Lab Adv Percept & Intelligent Control High En Minist Educ Wuhu 241000 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Wuxi 214105 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China

This paper presents an extension method of iterative learning control (ILC) to address the applications associated with non-repetitive time-varying systems (NTVSs). Conventional ILC approaches employ fixed nominal system models, but non-repetitive time-varying models may lead to accumulated model uncertainties, which fails to satisfy the robust convergence conditions. To tackle this issue, a novel ILC algorithm with parameter estimation is proposed using back propagation neural network. This algorithm incorporates an approach that utilizes Bayesian regularization training mechanism to accurately estimate non-repetitive time-varying parameters. Through comprehensive experiment on Monolithic XY Stage, the performance of proposed algorithm is validated to demonstrate its feasibility and effectiveness while handling tasks on NTVSs.

关键词： iterative learning control Non-repetitive time-varying systems Parameter estimation Neural network Bayesian regularization

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iterative learning control for Nonlinear Uncertain Parameterized Multi-Agent Systems With Non-Identical Partially Unknown control Directions

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第4期11卷 3358-3369页

作者： Chen, Jiaxi Li, Junmin Chen, Weisheng Zhang, Shuai Zhang, Junlin Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sci & Technol Antennas & Microwave Lab Xian 710071 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

This paper investigates the learning consensus control and formation control of nonlinear uncertain parameterized multi-agent systems with non-identical partially unknown control directions, which generalizes the research results of linear uncertain parameterized multi-agent systems. Based on neural networks and Fourier series expansion, a new neural network approximator is constructed to approximate the nonlinear uncertain parameterized dynamics. Combined with the iterative learning control and adaptive control method, a novel adaptive iterative learning control law is designed, in which the parameter adaptive iterative learning estimation is used to eliminate the influence of approximation errors, unpredictable leader dynamics and unknown control directions. Then, a new parameterized composite energy function is constructed to demonstrate the stability of the entire consistent systems and formation systems. On this basis, the advantages of protocols in undirected and directed topologies were discussed. Finally, the simulation results verify the effectiveness of the proposed control algorithm.

关键词： Topology Vectors Multi-agent systems Task analysis Mathematical models iterative learning control Artificial neural networks iterative learning control non-identical partially unknown control directions nonlinear uncertain parameterized dynamics neural networks

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iterative learning control of Stochastic Multi-Agent Systems with Variable Reference Trajectory and Topology

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AUTOMATION AND REMOTE control 2023年第6期84卷 612-625页

作者： Koposov, A. S. Pakshin, P. V. Nizhnii Novgorod State Tech Univ Alekseev Arzamas Polytech Inst Arzamas Nizhny Novgorod Russia

In modern smart manufacturing, robots are often connected via a network, and their task can change according to a predetermined program. iterative learning control (ILC) is widely used for robots executing high-precision operations. Under network conditions, the efficiency of ILC algorithms may decrease if the program is restructured. In particular, the learning error may temporarily increase to an unacceptable value when changing the reference trajectory. This paper considers a networked system with the following features: the reference trajectory and parameters change between passes according to a known program, agents are subjected to random disturbances, and measurements are carried out with noise. In addition, the network topology changes due to the disconnection of some agents from the network and the connection of new agents to the network according to a given program. A distributed ILC design method is proposed based on vector Lyapunov functions for repetitive processes in combination with Kalman filtering. This method ensures the convergence of the learning error and reduces its increase caused by changes in the reference trajectory and network topology. The effectiveness of the proposed method is confirmed by an example.

关键词： iterative learning control multi-agent system variable topology random disturbances repetitive processes stability stabilization vector Lyapunov function linear matrix inequalities

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iterative learning control for Tricalcium Neutralization Process with Initial Disturbance

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CHEMICAL ENGINEERING & TECHNOLOGY 2023年第5期46卷 1028-1038页

作者： Pei, Sheng Zhao, Zhonggai Liu, Fei Miao, Maodong Jin, Sai Sun, Fuxin Shi, Guiyang Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Jiangsu Guoxin Union Energy Co Ltd Wuxi 214122 Peoples R China Jiangnan Univ Key Lab Ind Biotechnol Minist Educ Wuxi 214122 Peoples R China Jiangnan Univ Natl Engn Res Ctr Cereal Fermentat & Food Biomfg Wuxi 214122 Peoples R China

The tricalcium neutralization process (TNP) is a key recovery process in the citric acid production process. The amount of calcium carbonate added needs to be controlled to ensure the pH of the reactants within the target range at the terminal of every batch. But the random initial pH has a great influence on the stability of the terminal pH. In this work, an iterative learning control (ILC)-based control strategy is proposed to optimize the addition of calcium carbonate. First, the terminal iterative learning control (TILC) is performed and the optimal input of the process can be obtained. Then, an initial disturbance compensation controller optimized by ILC is proposed. The results of the TNP control experiments demonstrate that the suggested control strategy can suppress the disturbances and achieve terminal pH control.

关键词： Citric acid recovery Initial disturbance compensation iterative learning control Pilot platform experiments Tricalcium neutralization process

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iterative learning control for hyperbolic distributed parameter systems based on sensor-actuator networks

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ASIAN JOURNAL OF control 2023年第3期25卷 2074-2084页

作者： Zhang, Jianxiang Cui, Baotong Lou, Xuyang Wu, Wei Guangxi Univ Sci & Tchnol Sch Elect Elect & Comp Sci Liuzhou 545006 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Sch Internet Things Engn Wuxi Jiangsu Peoples R China

This paper proposes two kinds of iterative learning control (ILC) schemes for a class of the distributed parameter systems based on sensor-actuator networks which can be described by hyperbolic partial differential equations. A D-type ILC algorithm is first considered and the convergent condition of the output error is obtained via the contraction mapping methodology. Then, the PD-type ILC algorithm is considered in this hyperbolic distributed parameter systems based on sensor-actuator networks. Finally, a cable equation with air and structural damping is given to illustrate the effectiveness of the proposed methods.

关键词： convergence analysis hyperbolic distributed parameter systems iterative learning control

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iterative learning control of a Discrete-Time System under Delay along the Sample Trajectory and Input Saturation

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AUTOMATION AND REMOTE control 2023年第1期84卷 71-82页

作者： Pakshin, P. V. Emelyanova, J. P. Alekseev Nizhny Novgorod State Tech Univ Arzamas Polytech Inst Arzamas Nizhny Novgorod Russia

This paper considers a linear discrete-time system operating in a repetitive mode to track a reference trajectory with a required accuracy. The control variable has a delay along the sample trajectory, and saturation-type constraints are imposed. We introduce a new method for designing an iterative learning control law that depends on the delay and ensures the required accuracy of tracking. A numerical example demonstrates the effectiveness of this method.

关键词： iterative learning control input saturation delay repetitive processes 2D systems stability stabilization vector Lyapunov function linear matrix inequalities

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