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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： van Keulen, Thijs Oomen, Tom Heemels, Maurice Eindhoven Univ Technol Dept Mech Engn Control Syst Technol Grp Eindhoven Netherlands ASML Veldhoven Netherlands

ISBN: (纸本)9781713872344

High-tech motion system development is driven by increasingly accurate and fast positioning requirements. Feedforward compensation together with high bandwidth feedback control are essential to achieve these ever tightening performance demands. In particular, online adaptation of the feedforward parameters, to correct for small position dependencies and slow variations, is crucial to approach zero error tracking. The aim of this paper is a framework that provides robust recursive learning of feedforward parameters for any bounded reference trajectory. The convergence of the parameter learning strategy exploits the difference in timescale between the parameter variation rate and the bandwidth of the servo controlled system. This enables to describe a servo-error-based objective function for varying trajectories as a static sector bounded nonlinearity. Subsequently, the circle criterion is employed to derive stability guarantees on the learning with explicit robustness to reference trajectory variation. A numerical case study demonstrates that a significant performance improvement can be achieved. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： iterative and repetitive learning control Extremum seeking and model free adaptive control Continuous time system estimation

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Iteration learning control for Uncertain Nonlinear Systems with Time-Varying Output Constraint 22

Iteration Learning Control for Uncertain Nonlinear Systems w...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Zhao, Yangyang Zhang, Heng Liu, Xiaopei Wang, Yang ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

ISBN: (纸本)9781713872344

A novel Adaptive Iteration learning control (AILC) method is proposed to solve the trajectory tracking problem for a class of nonlinear uncertain systems with external disturbances. Furthermore, the output of system is required to be bounded by a time-varying function. To this end, a Barrier Lyapunov Function (BLF) term is integrated into the AILC scheme such that the impact of the uncertainties and disturbances are significantly reduced without violating the output constraints. A Barrier Composite Energy Function (BCEF) is utilized to analyze the convergence of state error and the boundedness of output. The validity of the proposed AILC scheme is verified by a numerical example. In addition, a high-fidelity simulation platform that can generate a real-life turbulent flow is utilized to demonstrate the robustness of the algorithm. Copyright (c) 2023 The Authors.

关键词： iterative and repetitive learning control control of constrained systems Uncertain systems Disturbance rejection High fidelity simulation

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Feedforward control in the Presence of Input Nonlinearities: With Application to a Wirebonder 22

Feedforward Control in the Presence of Input Nonlinearities:...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Poot, Maurice van Hulst, Jilles Yan, Kai Wa Kostic, Dragan Portegies, Jim Oomen, Tom Eindhoven Univ Technol Dept Mech Engn CST Eindhoven Netherlands ASMPT Ctr Competency Beuningen Netherlands Eindhoven Univ Technol Dept Math & Comp Sci CASA Eindhoven Netherlands Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

ISBN: (纸本)9781713872344

The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a Wiener feedforward, i.e., linear parameterization with an output nonlinearity, to achieve high tracking accuracy and task flexibility for a class of Hammerstein systems. The developed approach exploits iterative learning control to learn a feedforward signal from data that minimizes the error and utilizes a control-relevant cost function to learn the parameters of a Wiener feedforward parameterization. Experimental validation on a wirebonder shows that the developed approach enables high tracking accuracy and task flexibility. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC- ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： iterative and repetitive learning control Identification and control methods Nonlinear system identification Data-driven control learning for control.

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Iteration learning control for Uncertain Nonlinear Systems with Time-Varying Output Constraint

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IFAC-PapersOnLine 2023年第2期56卷 11867-11873页

作者： Yangyang Zhao Heng Zhang Xiaopei Liu Yang Wang School of Information Science and Technology ShanghaiTech University 201210 China

关键词： iterative and repetitive learning control control of constrained systems Uncertain systems Disturbance rejection High fidelity simulation

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Feedforward control in the Presence of Input Nonlinearities: With Application to a Wirebonder

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IFAC-PapersOnLine 2023年第2期56卷 1895-1900页

作者： Maurice Poot Jilles van Hulst Kai Wa Yan Dragan Kostić Jim Portegies Tom Oomen CST Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Center of Competency ASMPT Beuningen The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

关键词： iterative and repetitive learning control Identification and control methods Nonlinear system identification Data-driven control learning for control

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Online feedforward parameter learning with robustness to set-point variations

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IFAC-PapersOnLine 2023年第2期56卷 1919-1925页

作者： Thijs van Keulen Tom Oomen Maurice Heemels Eindhoven University of Technology Department of Mechanical Engineering Control Systems Technology group ASML Veldhoven the Netherlands

High-tech motion system development is driven by increasingly accurate and fast positioning requirements. Feedforward compensation together with high bandwidth feedback control are essential to achieve these ever tightening performance demands. In particular, online adaptation of the feedforward parameters, to correct for small position dependencies and slow variations, is crucial to approach zero error tracking. The aim of this paper is a framework that provides robust recursive learning of feedforward parameters for any bounded reference trajectory. The convergence of the parameter learning strategy exploits the difference in time-scale between the parameter variation rate and the bandwidth of the servo controlled system. This enables to describe a servo-error-based objective function for varying trajectories as a static sector bounded nonlinearity. Subsequently, the circle criterion is employed to derive stability guarantees on the learning with explicit robustness to reference trajectory variation. A numerical case study demonstrates that a significant performance improvement can be achieved.

关键词： iterative and repetitive learning control Extremum seeking and model free adaptive control Continuous time system estimation

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Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems 21st

Task Decomposition for MPC: A Computationally Efficient Appr...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Vallon, Charlott Borrelli, Francesco Univ Calif Berkeley Berkeley CA 94701 USA

Task Decomposition method for iterative learning Model Predictive control (TDMPC) for linear time-varying systems is presented. We consider the availability of state-input trajectories which solve an original task T1, and design a feasible MPC policy for a new task, T2, using stored data from T1. Our approach applies to tasks T2 which are composed of subtasks contained in T1. In this paper we formally define the task decomposition problem, and provide a feasibility proof for the resulting policy. The proposed algorithm reduces the computational burden for linear time-varying systems with piecewise convex constraints. Simulation results demonstrate the improved efficiency of the proposed method on a robotic path-planning task. Copyright (C) 2020 The Authors.

关键词： Data-based control iterative and repetitive learning control Linear Model Predictive control Convex Optimization

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Overcoming Output Constraints in iterative learning control Systems by Reference Adaptation 21st

Overcoming Output Constraints in Iterative Learning Control ...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Meindl, Michael Molinari, Fabio Raisch, Joerg Seel, Thomas Hsch Karlsruhe Tech & Wirtschaft Karlsruhe Germany Tech Univ Berlin Control Syst Grp Berlin Germany

iterative learning control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output constraints. The topic of this paper is the design of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system and capable of complying with output constraints. The underlying idea is to scale the reference at every trial by using a conservative estimate of the output's progression. Properties as the monotonic convergence above a threshold and the respect of output constraints are formally proven. Numerical simulations and experimental results reinforce our theoretical results. Copyright (C) 2020 The Authors.

关键词： iterative and repetitive learning control Linear systems learning for control control of constrained systems

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A Hybrid Adaptation Strategy for repetitive control of an Uncertain-Delay Lagrangian System 21st

A Hybrid Adaptation Strategy for Repetitive Control of an Un...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Tilli, Andrea Ruggiano, Elena Conficoni, Christian Bosso, Alessandro Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

In this work, we present a novel repetitive control (RC) strategy to achieve accurate position tracking of a 1-DOF Lagrangian system. Such controller is able to cope with model uncertainties and unknown transmission delays in the control architecture. The classic repetitive structure is augmented with an observer of the residual disturbance, to be compensated by means of the RC action. The repetitive unit is updated at hybrid instants, so that the disturbance observer is close to its steady-state before a new repetitive correction is applied. In addition, communication delay is also estimated by the proposed control structure. This way, practical asymptotic stability of the overall system can be achieved with a simple proportional correction of the RC, also under perturbations of the steady-state estimate due to model uncertainties. In light of the aforementioned properties, the proposed RC-based controller is shown to be an easy-to-tune, robust solution capable of improving the tracking performance for the given case of study. Copyright (C) 2020 The Authors.

关键词： iterative and repetitive learning control Systems with time-delays Motion control Systems Design methodologies Mechatronic systems Stability of hybrid systems

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Practical method to complete learning Model Predictive control with generalization capability 21st

Practical method to complete Learning Model Predictive Contr...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Torok, Ferenc Peni, Tamas Inst Comp Sci & Control SZTAKI Kende U 13-17 H-1111 Budapest Hungary

The paper presents a practical method to complete learning Model Predictive control (LMPC) with generalization capability. LMPC has been developed by F. Borrelli and his co-authors for systems performing iterative tasks. The method is based on saving the state trajectories of successful runs and using this database to improve the control performance in the future iterations. When the controller faces a new task, the database is cleared and the learning phase starts over. This paper addresses the question of how a general knowledge base can be built to warm start the learning process. As a potential solution, a practical method is proposed. The algorithm is tailored specifically to the autonomous racing application, but the concept can be extended to a wider class of control problems. The procedure includes the construction of special teaching tracks, on which the trajectory database is generated and a multi-step migration procedure for transferring the learned trajectories onto any new track. The efficiency of the method is demonstrated by numerical simulations. Copyright (C) 2020 The Authors.

关键词： model predictive control nonlinear control iterative and repetitive learning control autonomous racing

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