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iterative learning control for UAVs formation based on point-to-point trajectory update tracking

MATHEMATICS AND COMPUTERS IN SIMULATION 2023年第1期209卷 1-15页

作者： Xingjian, Fu Jianshuai, Peng Beijing Informat Sci & Technol Univ Beijing 100192 Peoples R China

For the problem of trajectory tracking in UAVs formation system, an adaptive iterative learning control strategy based on point-to-point trajectory update tracking is proposed. The nonlinear discrete system after iterative dynamic linearization approximation is combined with the UAVs formation system, and the time-varying parameters are introduced for trajectory tracking. According to the iterative linear dynamic condition and the global Lipschitz condition, the system tracking error is analyzed under the iterative learning control based on point-to-point trajectory tracking. The proposed control method not only uses the freedom degree of the tracking points to analyze the convergence and robustness for the UAVs formation, but also introduces error prediction to improve the anti-disturbance ability for the system. Finally, the simulation study for the UAV formation system is carried out to verify the effectiveness of the proposed method.(c) 2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： UAVs formation iterative learning control Point-to-point tracking iterative dynamic linearization

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iterative learning control applied to distributed-order linear time invariant MIMO systems to achieve learnability

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ASIAN JOURNAL OF control 2023年第4期25卷 2508-2520页

作者： Angeles-Ramirez, Oscar A. Fernandez-Anaya, Guillermo Munoz-Vazquez, Aldo J. Sanchez-Torres, Juan D. Melendez-Vazquez, Fidel Univ Iberoamer Phys & Math Dept 880 Prol Paseo Reforma Mexico City 01219 DF Mexico Texas A&M Univ Higher Educ Ctr McAllen Dept Multidisciplinary Engn 6200 Tres Lagos Blvd Mcallen TX 78504 USA ITESO Dept Math & Phys Perifer Sur Manuel Gomez Morin 8585 Tlaquepaque 45604 Jalisco Mexico

Sufficient and necessary conditions for a distributed-order linear time invariant system to be positive real are derived in terms of linear matrix inequalities. The positive realness condition is derived for three of the most usual cases presented in literature, in the realm of distributed-order linear time invariant systems. As an additional product of this paper, the strictly positive realness condition can be derived. In addition, the concept of learnability of fractional-order multi-input multi-output systems is extended to the case of distributed-order systems, which is approached from the concept of output-dissipativity by using an iterative learning scheme.

关键词： distributed-order linear time invariant systems fractional systems iterative learning control learnability positive realness strictly positive realness

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iterative learning control for linear parabolic distributed parameter systems with multiple point sensors

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OPTIMAL control APPLICATIONS & METHODS 2023年第3期44卷 1174-1184页

作者： Liu, Yaqiang Lai, Jialun Jin, Zengwang Ren, Zhigang Xie, Shengli Wang, Yalin Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Guangdong Univ Technol Key Lab Intelligent Detect & Internet Things Mfg Minist Educ Guangzhou Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Guangdong Univ Technol Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong HongKong Macao Joint Lab Smart Discret Guangzhou Peoples R China Cent South Univ Sch Automat Changsha Peoples R China

This article presents an iterative learning control (ILC) approach for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. The distribution functions of actuators and sensors are chosen as delta function to produce pointwise control and pointwise measurement. A P-type ILC law is proposed based on the iterative inputs and outputs to ensure the iterative process of the system is convergent under the ILC law. By utilizing integration by parts, triangle inequality, property of delta function, and Gronwall lemma, a sufficient condition based on an inequality constraint for the convergence analysis of the track error system is presented. Finally, the effectiveness of the proposed design method is verified by numerical simulation results.

关键词： convergence analysis distributed parameter systems iterative learning control pointwise measurement

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iterative learning control for Path-Following of ASV With the Ice Floes Auto-Select Avoidance Mechanism

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Zhang, Guoqing Sun, Zhu Li, Jiqiang Huang, Jiangshuai Qiu, Bin Dalian Maritime Univ DMU Nav Coll Dalian 116026 Liaoning Peoples R China Dalian Key Lab Independent Shipping Safety Technol Dalian 116026 Liaoning Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Guilin Univ Technol Sch Comp Sci & Engn Guilin 541004 Peoples R China Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sys Guilin 541004 Peoples R China

The autonomous and security are the crucial requirements in fields of the polar transportation. This paper proposes a newly iterative learning control framework for the autonomous surface vessels (ASV) to implement the path-following operation in the ice floes scenario. The proposed framework is divided into two parts: the guidance and control. For the former, the ice floes are firstly identified into threatening and non-threatening based on the size. Subsequently, the obstacle area of each threatening ice floe is programmed considering the underwater portion. Then the ice floes avoidance guidance with auto-select mechanism for ice-zone traversal mission is constructed by setting the hazard threshold and target point. For the latter, a robust adaptive iterative learning control (ILC) system is designed for the path-following mission, where the control accuracy increases with the number of iterations. The stability of the closed-loop control system is proved with utilization of the Lyapunov theorem. Finally, two numerical examples are provided to evaluate the advantages and accuracy of the proposed algorithm, where the ice floes are generated with irregular.

关键词： Ice Navigation Accuracy Marine vehicles Sea surface Programming Actuators Surges iterative learning control Arctic Ice floes avoidance obstacle area ASV VS guidance ILC algorithm path-following control

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iterative learning control based on dynamic time warping for a class of discrete-time nonlinear systems with varying trial lengths and terminus constraint

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第5期33卷 3146-3163页

作者： Xia, Jingkang Zhang, Ruiqing Li, Yanan Huang, Deqing Li, Xuefang Southwest Jiaotong Univ Dept Elect Engn Chengdu 611731 Peoples R China Univ Sussex Dept Engn & Design Brighton England Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

This article proposes a dynamic time warping (DTW)-based iterative learning control (ILC) scheme for discrete-time nonlinear systems to tackle the path learning problem with varying trial lengths and terminus constraint. By incorporating the improved DTW algorithm, the varying trial lengths are aligned as a desired length. Meanwhile, this algorithm can find the most similar waypoints between the output and the desired paths, which can be used to design an updating law and facilitate the convergence of path learning. Different from the existing ILC methods based on the probability distribution function for learning trajectory in the time domain, the method in this article can be applied to learn the spatial path corresponding to the desired trajectory. Furthermore, the learning property in the presence of variable initial states is discussed under the proposed method. Several illustrative examples manifest the validity of the proposed DTW-based ILC algorithm.

关键词： discrete-time nonlinear systems dynamic time warping iterative learning control terminus constraint variable initial state varying trial lengths

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iterative learning control of complex Ginzburg-Landau system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2023年第6期45卷 1169-1179页

作者： Cai, Liuchi Dai, Xisheng Zhang, Jianxiang Huang, Qingnan Guangxi Univ Sci & Technol Sch Automat Liuzhou Peoples R China Guangxi Univ Sci & Technol Sch Automat Liuzhou 545006 Peoples R China

Different from the existing research on iterative learning control (ILC) real-valued systems, complex-valued partial differential system is studied in this paper. All variables and parameters of the system are complex, and distributed control is imposed on the system. First, the original complex system is transformed into two coupled real systems, which represent the real and imaginary parts of the original complex system, respectively. Second, a complex P-type ILC algorithm is designed that the real and imaginary parts of the learning law are coupled to each other. Then, the contraction mapping principle and analytical techniques are used to ensure that the tracking error converges to zero in the sense of L-2 -norm. Finally, a numerical simulation is presented to show the effectiveness of the proposed algorithm.

关键词： Complex Ginzburg-Landau system iterative learning control distributed control convergence analysis

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iterative learning control for Lateral Tracking With Repeated Path in Autonomous Vehicles for Dynamic Environments

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2023年第11期21卷 3712-3723页

作者： Areerob, Punyapat Panomruttanarug, Benjamas King Mongkuts Univ Technol Thonburi Fac Engn Dept Control Syst & Instrumentat Engn 126 Pracha Uthit Rd Bangkok 10140 Thailand

Many control techniques are proposed in literature for the lateral tracking of autonomous vehicles. Unfortunately, most of these demonstrate control efficiency based on supportive results from simulation only. A lack of evidence in experimental verification raises questions about the design performance. This research proposes a practical tracking algorithm using iterative learning control (ILC) with the expectation of achieving a lower tracking error in each iteration. ILC observes an error sequence from the past to adjust the steering command in a real vehicle. This work also develops a localization system to achieve the real-time positioning data based on extended Kalman filtering (EKF). Signals obtained from the inertial measurement unit (IMU), global navigation satellite system (GNSS) module, and vehicle encoders are fused to determine the real-time vehicle position. Experiments were conducted to validate the effectiveness of the ILC-based tracking control. The results show that the ILC designs can clearly improve the tracking performance from a typical control system by reducing the tracking error in the iteration domain. In addition, using more gains in the ILC design results in a smoother path.

关键词： Autonomous vehicles iterative learning control lateral control tracking

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iterative learning control of Constrained Systems With Varying Trial Lengths Under Alignment Condition

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第9期34卷 6670-6676页

作者： Shen, Mouquan Wu, Xingzheng Park, Ju H. Yi, Yang Sun, Yonghui Nanjing Technol Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea Yangzhou Univ Sch Informat Engn Dept Automat Yangzhou 225127 Jiangsu Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China

This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

关键词： Trajectory Convergence Adaptive systems Nonlinear systems MIMO communication iterative learning control System performance iterative learning control (ILC) nonidentical trial lengths tracking control

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iterative learning control for Compliant Underactuated Arms

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第6期53卷 3810-3822页

作者： Pierallini, Michele Angelini, Franco Mengacci, Riccardo Palleschi, Alessandro Bicchi, Antonio Garabini, Manolo Univ Pisa Ctr Ric Enrico Piaggio I-56126 Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Fdn Ist Italiano Tecnol Soft Robot Human Cooperat & Rehabil I-16163 Genoa Italy

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

关键词： Robots Arms Manipulators Soft robotics Feedforward systems Trajectory iterative learning control Flexible structures iterative learning control (ILC) robotics

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iterative learning control for piecewise arc path tracking with validation on a gantry robot manufacturing platform

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ISA TRANSACTIONS 2023年第1期139卷 650-659页

作者： Chen, Yiyang Freeman, Christopher T. Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

The piecewise arc path tracking problem is a common feature of manufacturing systems operating in a repetitive mode, e.g. assembly production lines. Here, the system end-effector must follow a spatial path without any specific temporal tracking constraints, which makes the temporal profile not fixed a priori. The technique of iterative learning control (ILC) is well-suited to handle this problem, since compared to classical feedback control methods, ILC is capable of learning from previous trial information to minimize the tracking error over repeated trials. This paper extends the ILC task description to address piecewise arc path tracking tasks, and further formulates a more general design framework than existing spatial ILC approaches. A comprehensive ILC algorithm is designed to handle this class of piecewise arc path tracking problems, and practical implementation instructions are provided. Validation is conducted on a gantry robot manufacturing testbed to confirm its feasibility and efficiency in practice with a comparison to existing methods showing its higher path tracking accuracy.& COPY;2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： iterative learning control Path tracking Optimization Gantry robot

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