Iterative learning control in high-performance motion systems: from theory to implementation

Goubej, Martin; Meeusen, Sven; Mooren, Noud; Oomen, Tom

doi:10.1109/etfa.2019.8868996

Cited by 4 publications

(4 citation statements)

References 15 publications

(16 reference statements)

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“…Typical scenarios include, for example, a rigid body system, flexible load with one or several dominant bending modes, friction-dominant system, unbalanced rotor system etc. The setup proved to be a valuable benchmark problem for the evaluation of various methods from the field of mechatronics, including modelling, identification, and feedback control design [11][12][13][14].…”

Section: Flexible Arm Benchmark Problemmentioning

confidence: 99%

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Employing Finite Element Analysis and Robust Control Concepts in Mechatronic System Design-Flexible Manipulator Case Study

et al. 2021

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The paper deals with development of a methodology for mechatronic system design using state-of-the-art model-based system engineering methods. A simple flexible robotic arm is considered as a benchmark problem for the evaluation of various techniques used in the phases of modelling, analysis, control system design, validation, and implementation. The flexible nature of the mechanical structure introduces inherently oscillatory dynamics in the target bandwidth range, which complicates all the above-mentioned design steps. This paper demonstrates the process of deriving a complex nonlinear model of the flexible arm setup. An initial idea about the plant dynamics is acquired from analytical modelling using the Euler–Bernoulli beam theory. A more thorough understanding is subsequently acquired from finite element analysis. Linearisation and order reduction are the next steps necessary for the derivation of a simplified control-relevant model. A time-dependent variable parameter of load mass position is considered and a robust controller is subsequently designed in order to fulfil certain performance criteria for all the admissible plant configurations. This is performed using a recent H-infinity loop shaping method for fixed structure controller design. The results are validated by means of a physical plant, comparing the experimental data with the model predictions.

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Section: Flexible Arm Benchmark Problemmentioning

confidence: 99%

“…Specifically, the payload mass is located at the last element, i.e., n = 4, and is related to the variable q 9 . The energies of the arm are considered as (12) for n = 4. By summing energies of all considered components, we obtain…”

Section: Analytical Model Development Using Euler-bernoulli Beam Theorymentioning

confidence: 99%

Employing Finite Element Analysis and Robust Control Concepts in Mechatronic System Design-Flexible Manipulator Case Study

et al. 2021

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“…Building block 9 focuses explicitly on controlled systems that perform repetitive/iterative tasks (ILC/RC) either in terms of reference trajectories or disturbances [51,52]. Developed SW function blocks include complex ILC/RC schemes with a self-commissioning "service" that is beneficiary for different motion control applications having periodic character.…”

Section: Iterative and Repetitive Control Module (Bb9)mentioning

confidence: 99%

Pushing Mechatronic Applications to the Limits via Smart Motion Control

2021

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Modern machines strive to run at limit performance and dependability while their operational area and size are getting restricted. To achieve those objectives, often swift integration of custom-made subsystems is required, either actuators, sensors, electronic, or SW modules. Such a diverse suite of elements needs specific approaches and tools for fast optimization and adjustment following model-based system engineering (MBSE) and digital twinning principles. The large-scale I-MECH project was an industry-driven initiative striving to give a scientific response to those demands. The intermediate results were summarized in the authors’ previous work. The purpose of this paper is to report on final project results, namely specific performance achievements and figures based on measurable KPIs. After a brief description of key technologies, special focus is given to industrial printing technology based on a generic substrate carrier. However, it is shown that similar and consistent methodology can be applicable in many other industrial domains, such as semiconductors, healthcare robotics, machining, packaging, etc. Thus, the main merit of this survey is a holistic approach to motion control design.

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“…The application of the proposed method to non‐repeating tasks is possible. Specifically, the desired path can be represented using the basis functions, such as nonuniform rational B‐spline (NURBS), and the ILC could be applied to the parameters of the basis functions [26], or applying the adaptive ILC [22].…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for integrated system of robot and machine tool

Chen

2022

Asian Journal of Control

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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.

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Iterative learning control in high-performance motion systems: from theory to implementation

Cited by 4 publications

References 15 publications

Employing Finite Element Analysis and Robust Control Concepts in Mechatronic System Design-Flexible Manipulator Case Study

Employing Finite Element Analysis and Robust Control Concepts in Mechatronic System Design-Flexible Manipulator Case Study

Pushing Mechatronic Applications to the Limits via Smart Motion Control

Iterative learning control for integrated system of robot and machine tool

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