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Beyond Performance of learning control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and Information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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learning control for Networked Stochastic Systems With Random Fading Communication

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第6期52卷 3659-3670页

作者： Shen, Dong Qu, Ganggui Song, Qijiang Renmin Univ China Sch Math Beijing 100872 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning gain is replaced by a variable one to suppress the effect of various uncertainties. Strong convergence of the proposed scheme is established under random fading phenomena and system noise. The input error is shown convergent to zero as the cycle number increases. Two numerical examples demonstrate the applications of the proposed scheme.

关键词： Fading channels Convergence Uncertainty Indexes Additives Stochastic processes Target tracking Almost sure convergence fading channels learning control mean-square convergence networked stochastic systems

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Neural learning control for Discrete-Time Strict-Feedback Systems: An Error Estimate Method

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第9期70卷 3439-3443页

作者： Shi, Haotian Wang, Min Wang, Cong Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangdong Prov Key Lab Tech & Equipmentfor Macromo Guangzhou 510641 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

This brief addresses the issue of neural learning control for a kind of discrete-time strict-feedback system. Firstly, by using an error dynamics estimator, a new delay-free NN update law is proposed. Subsequently, after making the system output tracks a given recurrent tracking signal, the estimated NN weights can be demonstrated to converge to a small area of their ideal values, exponentially, which can be stored as constant NN weights. Then, a neural learning controller is proposed by using the constant NN weights and disturbance observer. Compared with the previous neural learning controller, the proposed scheme can not only avoid the chattering problem that may be caused by controller switching but also enhance the system robustness by implanting the disturbance observer.

关键词： Index Terms-Strict-feedback systems discrete-time systems learning control neural networks adaptive control

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Robust Higher-Order Spatial Iterative learning control for Additive Manufacturing Systems

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2023年第4期31卷 1692-1707页

作者： Afkhami, Zahra Hoelzle, David J. Barton, Kira Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA

In this article, a higher-order spatial iterative learning control (HO-SILC) scheme is proposed, targeting heightmap tracking for a class of 3-D structures fabricated by repetitive addition of material in a layer-by-layer fashion using additive manufacturing (AM) technology. AM processes are innately iteration-varying, resulting in large model uncertainties due to iteration-varying system parameters and surface variations. HO-SILC has been shown to be useful in repetitive systems with model uncertainties by improving system performance with respect to convergence speed and robustness. In this article, HO-SILC is used to iteratively construct a feedforward control signal to improve part quality in multilayered AM constructs. The system dynamics are approximated by discrete 2-D spatial convolution kernels that incorporate in-layer and layer-to-layer variations. The proposed HO-SILC framework incorporates data available from previously printed devices, as well as multiple previously printed layers, to enhance the overall performance. The condition for robust monotonic convergence (RMC) of the iteration-varying HO-SILC algorithm is based on the Lyapunov stability criteria. Simulation results of an AM process termed electrohydrodynamic jet (e-jet) printing demonstrate that a well-designed HO-SILC framework is effective and can improve the performance by 60%. In addition, HO-SILC is robust to iteration-varying model uncertainties, especially at higher layers where iteration-varying surface variations are more pronounced.

关键词： Intelligent and flexible manufacturing learning control Lyapunov methods stability of linear systems

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Adaptive Velocity Estimators for learning control 10

Adaptive Velocity Estimators for Learning Control

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

作者： Nikovski, Daniel Yerazunis, William Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA

ISBN: (纸本)9798350373981;9798350373974

The paper proposes a method for learning velocity estimators, in the form of finite impulse response (FIR) filters, from data collected from a system equipped with quantizing position encoders that is to be controlled by means of a full-state feedback controller making use of the velocity estimates. The resulting estimators are tailored to the properties of the controlled system and show empirically superior performance in comparison with commonly used baseline velocity estimators, both in terms of velocity estimation error as well as in terms of reduced regulation cost when tested on control problems. The proposed adaptive estimators are resistant to overfitting the training data, are easy to implement on embedded controller devices, and can be used in conjunction with various learning control methods.

关键词： learning control state estimation

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Neural-embedded learning control for fully-actuated flying platform of aerial manipulation system

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NEUROCOMPUTING 2022年第0期482卷 212-223页

作者： Ma, Le Yan, Yiming Li, Zhiwei Liu, Jie Northeast Elect Power Univ Sch Automat Engn Jilin 132000 Jilin Peoples R China

To address the problem of efficient task for the fully-actuated aerial manipulation system, the flight platform control scheme based on neural networks embedding is proposed. We embed the neural networks controllers (NNC) into some base controllers, which are Lyapunov stable for their system. Due to the learning and optimizing capability of NNC, the derivative controllers allow to improve control performance by updating the parameters of NNC under Lyapunov stability condition. The backstepping technology based on the mixture basis functions that approximates unknown system dynamics and the improved disturbance observer is designed for the base controller of platform attitude. And the linear active disturbance rejection controller (LADRC) is utilized as the base controller of platform position. Considering safety and cost, we made some virtual experiments in CoppeliaSim a software which offers high accuracy physical engine. The results of virtual experiments prove that compared with some state-of-the-art technologies the methods proposed are advanced in tracking errors performance due to NNC. (C) 2021 Elsevier B.V. All rights reserved.

关键词： learning control Mixture basic functions Deep learning technology Backstepping control Fully-actuated Linear active disturbance rejection controller

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learning control of Quantum Systems Using Frequency-Domain Optimization Algorithms

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2021年第4期29卷 1791-1798页

作者： Dong, Daoyi Shu, Chuan-Cun Chen, Jiangchao Xing, Xi Ma, Hailan Guo, Yu Rabitz, Herschel Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Princeton Univ Dept Chem Princeton NJ 08544 USA Cent South Univ Sch Phys & Elect Hunan Key Lab Super Microstruct & Ultrafast Proc Changsha 410083 Peoples R China Changsha Univ Sci & Technol Sch Phys & Elect Sci Hunan Prov Key Lab Flexible Elect Mat Genome Engn Changsha 410114 Peoples R China

We investigate two classes of quantum control problems by using frequency-domain optimization algorithms in the context of ultrafast laser control of quantum systems. In the first class of problems, the system model is known and a frequency-domain gradient-based optimization algorithm is applied for searching an optimal control field to selectively and robustly manipulate the population transfer in atomic rubidium. The other class of quantum control problems involves an experimental system with an unknown model. In this case, we introduce a differential evolution algorithm with a mixed strategy to search for optimal control fields and demonstrate the capability in an ultrafast laser control experiment for the fragmentation of Pr(hfac)(3) molecules.

关键词： Frequency-domain analysis Optimization Optimal control Laser modes Robustness Atomic beams Femtosecond laser frequency-domain optimization learning control quantum control quantum control experiment

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Heuristic dynamic programming-based learning control for discrete-time disturbed multi-agent systems

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control Theory and Technology 2021年第3期19卷 339-353页

作者： Yao Zhang Chaoxu Mu Yong Zhang Yanghe Feng School of Electrical and Information Engineering Tianjin UniversityTianjin300072China College of Systems Engineering National University of Defense TechnologyChangsha410073HunanChina

Owing to extensive applications in many fields,the synchronization problem has been widely investigated in multi-agent *** synchronization for multi-agent systems is a pivotal issue,which means that under the designed control policy,the output of systems or the state of each agent can be consistent with the *** purpose of this paper is to investigate a heuristic dynamic programming(HDP)-based learning tracking control for discrete-time multi-agent systems to achieve synchronization while considering disturbances in ***,due to the difficulty of solving the coupled Hamilton–Jacobi–Bellman equation analytically,an improved HDP learning control algorithm is proposed to realize the synchronization between the leader and all following agents,which is executed by an action-critic neural *** action and critic neural network are utilized to learn the optimal control policy and cost function,respectively,by means of introducing an auxiliary action ***,two numerical examples and a practical application of mobile robots are presented to demonstrate the control performance of the HDP-based learning control algorithm.

关键词： Multi-agent systems Heuristic dynamic programming(HDP) learning control Neural network Synchronization

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A Basic Study on Trajectory Accuracy Improvement of Hydraulic Excavator by learning control 13

A Basic Study on Trajectory Accuracy Improvement of Hydrauli...

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13th Asian control Conference (ASCC)

作者： Kosaka, Kohtaro Yoshimi, Takashi Izumikawa, Takeya Umeda, Takashi Shibaura Inst Technol Grad Sch Engn & Sci Tokyo Japan Shibaura Inst Technol Coll Engn Dept Elect Engn Tokyo Japan Sumitomo Construct Machinery Co Ltd Chiba Japan

ISBN: (纸本)9788993215236

learning control is a method of compensating for reproducible errors due to repetitive movements by feedforwarding. The greatest advantage of this control method is that even if the dynamics of the controlled object is unknown, compensation can be performed from the input / output relationship. However, the conventional learning control is a method for robot arms with little time delay and it was difficult for providing sufficient compensation of the trajectory error of the hydraulic excavator which includes large time delay in its control system. Therefore, we considered and proposed the effective learning control method which could be applied to a system with a large delay. It is averaging the output error from each control time to the system delay time ahead. In this paper, we verified the basic effectiveness of proposed learning control by applying it to the experimental set-up of a hydraulic excavator.

关键词： learning control hydraulic excavator large time delay

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Composite learning control of Overactuated Manned Submersible Vehicle With Disturbance/Uncertainty and Measurement Noise

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2021年第12期32卷 5575-5583页

作者： Fang, Xing Liu, Fei Gao, Xiang Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Natl Deep Sea Ctr Qingdao 266237 Peoples R China

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle. First, an MSOB is employed to reconstruct the real output signals from noise-contained measurements. Second, a composite estimation is developed where an NDOB is designed to estimate external disturbance and an NN is employed for model uncertainty. Furthermore, a control allocation technique is used to address the overactuated problem of the manned submersible vehicle. The rigorous stability analysis of the closed-loop manned submersible system is given via the Lyapunov theorem. Finally, several representative simulation results illustrate the superior control performance of the composite learning control scheme for the manned submersible vehicle.

关键词： Underwater vehicles Noise measurement Uncertainty Attitude control Resource management Vehicle dynamics Sea measurements learning control manned submersible vehicle model-based state observer (MSOB) neural network (NN) nonlinear disturbance observer (NDOB)

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