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iterative learning control for integrated system of robot and machine tool

ASIAN JOURNAL OF control 2023年第2期25卷 807-823页

作者： Thanh-Quan Ta Chen, Shyh-Leh Natl Chung Cheng Univ Dept Mech Engn Chiayi 621 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi 621 Taiwan

This study is concerned with the integrated system of a robot and a machine tool. The major task of robot is loading the workpiece to the machine tool for contour cutting. An iterative learning control (ILC) algorithm is proposed to improve the accuracy of the finished product. The proposed ILC is to modify the input command of the next machining cycle for both robot and machine tool to iteratively enhance the output accuracy of the robot and machine tool. The modified command is computed based on the current tracking/contour error. For the ILC of the robot, tracking error is considered as the control objective to reduce the tracking error of motion path, in particular, the error at the endpoint. Meanwhile, for the ILC of the machine tool, contour error is considered as the control objective to improve the contouring accuracy, which determines the quality of machining. In view of the complicated contour error model, the equivalent contour error instead of the actual contour error is taken as the control objective in this study. One challenge for the integrated system is that there exists an initial state error for the machine tool dynamics, violating the basic assumption of ILC. It will be shown in this study that the effects of initial state error can be significantly reduced by the ILC of the robot. The proposed ILC algorithm is verified experimentally on an integrated system of commercial robot and machine tool. The experimental results show that the proposed ILC can achieve more than 90% of reduction on both the RMS tracking error of the robot and the RMS contour error of the machine tool within six learning iterations. The results clearly validate the effectiveness of the proposed ILC for the integrated system.

关键词： integrated system iterative learning control motion control

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iterative learning control of Discrete Systems With Actuator Backlash Using a Weighted Sum of Previous Trial control Signals

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IEEE control SYSTEMS LETTERS 2023年 7卷 2958-2963页

作者： Pakshin, Pavel Emelianova, Julia Rogers, Eric Galkowski, Krzysztof Nizhnii Novgorod State Tech Univ Arzamas Polytech Inst RE Alekseev Dept Appl Math Arzamas 607227 Russia Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England Univ Zielona Gora Inst Automat Elect & Elect Engn PL-65516 Zielona Gora Poland

This letter considers iterative learning control design for discrete dynamics in the presence of backlash in the actuators. A new control design for this problem is developed based on the stability theory for nonlinear repetitive processes. An example demonstrates the effectiveness of the new design where the system model is constructed from data collected from frequency response tests on a physical system.

关键词： Nonlinear dynamics iterative learning control repetitive processes

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iterative learning control of a Pneumatically Driven Robot Joint 10

Iterative Learning Control of a Pneumatically Driven Robot J...

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10th International Conference on control, Decision and Information Technologies (CoDIT)

作者： Nitsche, Rainer Heubach, Timo Festo SE & Co KG Robot Syst Design Esslingen Germany

ISBN: (纸本)9798350373981;9798350373974

The unique physical characteristics of pneumatic drives facilitate the creation of robots that are safe lightweight and intuitive to operate. However, pneumatic robots are due to nonlinear behaviour that is hard to control. Especially friction plays a major role and limits the control performance of tracking a desired trajectory. In this contribution, the concept of iterative learning control (ILC) is introduced and applied to a pneumatically driven joint build-in a pneumatic cobot. iterative learning control is highly suitable for repeatable control tasks as they appear typically in robot applications [13]. With this approach, it is possible almost perfectly to track a given, repeatable trajectory.

关键词： model-based control robot control flatness iterative learning control pneumatic robotics

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iterative learning control for Path-Following Tasks With Performance Optimization

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2022年第1期30卷 234-246页

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

The classical problem setup of iterative learning control (ILC) is to enforce tracking of a reference profile specified at all time points in the fixed task duration. The removal of the time specification releases significant design freedom in how the path is followed but has not been fully exploited in the literature. This article unlocks this extra design freedom by formulating the ILC task description to handle repeated path-following tasks, e.g., welding and laser cutting, which aim at following a given "spatial" path defined in the output space without any temporal information. The general ILC problem is reformulated for ILC design with the inclusion of an additional performance index, and the class of piecewise linear paths is characterized for the reformulated problem setup. A two-stage design framework is proposed to solve the characterized problem and yields a comprehensive algorithm based on an ILC update and a gradient projection update. This algorithm is verified on a gantry robot experimental platform to demonstrate its practical efficacy and robustness against model uncertainty.

关键词： iterative learning control Hilbert space iterative learning control (ILC) optimization path following

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iterative learning control for high-speed trains with velocity and displacement constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2022年第6期32卷 3647-3661页

作者： Huang, Deqing Huang, Tengfei Chen, Chunrong Qin, Na Jin, Xu Wang, Qingyuan Chen, Yong Southwest Jiaotong Univ Inst Syst Sci & Technol Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Railway Ind Adv Energy Tract & Comprehens Chengdu Peoples R China Univ Kentucky Dept Mech Engn Lexington KY 40506 USA Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China

In this article, a novel iterative learning control (ILC) scheme is presented for the operation control of high-speed train (HST), where the velocity and displacement of HST are strictly limited to ensure safety and comfort. The model of HST constructed in the article is practical in the sense that both parametric and nonparametric uncertainties of system are addressed simultaneously. Backstepping design with the newly proposed barrier Lyapunov function is incorporated in analysis to ensure the uniform convergence of the state tracking error and that the constraint requirements on velocity and displacement would not be violated during the whole operation process. In the end, a simulation study is presented to demonstrate the efficacy of the proposed ILC law.

关键词： barrier composite energy function constraint high-speed train iterative learning control

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iterative learning control for Output Tracking of Nonlinear Systems With Unavailable State Information

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第9期33卷 5085-5092页

作者： Li, Xuefang Shen, Dong Ding, Beichen Sun Yat Sen Univ Sch Intelligent Syst Engn Guangdong Prov Key Lab Intelligent Transportat S Guangzhou 510275 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

This work presents a novel design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems. By using the closed-loop reference model that can be viewed as an observer, the proposed adaptive ILC approach can be adapted to deal with the output tracking problem of nonlinear systems with unavailable system states. In the systems considered, two classes of uncertainties are taken into account, including parametric input disturbances and input distribution uncertainties. To facilitate the controller design and convergence analysis, the composite energy function (CEF) methodology is employed. The design framework in this brief is novel and widely applicable, which extends the CEF-based ILC approach to output tracking control of nonlinear systems without requiring full knowledge of state information and complicated observer design process. To show the effectiveness of the proposed design framework and control algorithms, two numerical examples are illustrated.

关键词： Observers Uncertainty Nonlinear systems Convergence control systems Adaptation models iterative learning control Composite energy function (CEF) convergence analysis iterative learning control (ILC) output tracking

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iterative learning control Analysis for Linear Fractional-order Singular Systems

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2022年第12期20卷 3951-3959页

作者： Liu, Qian Tian, Senping Yantai Univ Sch Math & Informat Sci Yantai 264005 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

The issue of iterative learning control analysis for linear fractional-order singular systems is considered in this research. The focus is placed upon the design of the iterative learning control algorithm for the sake of tracking the desired output trajectory. An appropriate P-type algorithm is proposed for the linear fractional-order singular systems. Furthermore, a PD alpha-type algorithm is presented for such systems with time-delay. Sufficient conditions for the convergence of the presented algorithms are analyzed thoroughly. Finally, the efficiency of the algorithm is verified by simulation illustration.

关键词： Convergence analysis iterative learning control linear fractional-order singular systems P-type algorithm PD alpha-type algorithm

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iterative learning control for impulsive fractional order time-delay systems with nonpermutable constant coefficient matrices

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INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL PROCESSING 2022年第6期36卷 1419-1438页

作者： Aydin, Mustafa Mahmudov, Nazim I. Istanbul Tech Univ Dept Math Istanbul Turkey Eastern Mediterranean Univ Dept Math TR-99628 Famagusta Northern Cyprus Turkey

We introduce an impulsive fractional order time-delay systems with nonpermutable constant coefficient matrices whose solution is given by delayed perturbation of Mittag-Leffler type matrix function. The existence and uniqueness of the solution of the system is proved by using Banach contraction principle. The Ulam-Hyers stability of the given system below are demonstrated. Construct the iterative learning control (ILC) problem obtaining from the mentioned system. The conditions of convergence of ILC problem of each of P, D, and D alpha-types are presented and proved. A comprehensive example with three different original reference trajectories is given to illustrate some theoretical results. This article provides novel outcomes.

关键词： impulsive fractional order time-delay system iterative learning control

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iterative learning control with high-order internal model for first-order hyperbolic systems

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ISA TRANSACTIONS 2022年 120卷 70-77页

作者： Gu, Panpan Tian, Senping Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

This paper studies the iterative learning control (ILC) algorithm for first-order hyperbolic systems. Unlike most of the ILC literature of distributed parameter systems, in the iteration domain, that require identical desired trajectories. Here the desired trajectories are iteratively varying and described by a high-order internal model (HOIM). The HOIM-based P-type ILC design is firstly introduced in this paper to the first-order hyperbolic systems, which enable the systems to achieve the perfect tracking for the iteration-varying desired trajectories on L-2 space. Meanwhile, the convergence theorem of the proposed algorithm is established for first-order time-delay hyperbolic systems. Finally, simulation results testify the validity of the algorithm. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： iterative learning control First-order hyperbolic systems High-order internal model Iteration-varying desired trajectories

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iterative learning control for Precise Aircraft Trajectory Tracking in Continuous Climb and Descent Operations

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 10481-10491页

作者： Buelta, Almudena Olivares, Alberto Staffetti, Ernesto Univ Rey Juan Carlos Dept Signal Theory & Commun & Telemat Syst & Comp Fuenlabrada 28942 Spain

This paper presents an iterative learning control method for precise aircraft trajectory tracking. Given a trajectory to be followed by an aircraft with a dynamical model which is assumed to be known, the proposed algorithm improves the system performance in following the trajectory using the spatial and temporal deviations suffered by previous flights to anticipate recurring disturbances and compensate for them proactively by generating a new reference trajectory to be followed, which is the input for the aircraft's own trajectory tracking controller. The proposed method is tested in a simulated busy terminal maneuvering area in which the time-based separation between aircraft is short enough for similar weather conditions to be expected. The numerical experiments are conducted considering aircraft of the same type, which are assumed to follow the same trajectory in two operations in which precise trajectory tracking is essential: continuous climb and descent operations. The obtained results show a significant reduction of the trajectory tracking error in few iterations, proving the effectiveness of the iterative learning control method applied to commercial aircraft trajectory tracking. Higher precision in trajectory tracking implies higher predictability of aircraft trajectories, which results in an improvement of the efficiency and capacity of the air traffic management system and in reductions of costs and emissions.

关键词： Aircraft Aerospace control Trajectory Trajectory tracking Atmospheric modeling Adaptive control Mathematical model Aircraft trajectory tracking iterative learning control trajectory predictability air traffic management trajectory based operations

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