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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Niu, Yiming Cui, Can Yang, Guo Wu, Wen Nanjing Univ Sci & Technol Ministerial Key Lab JGMT Nanjing 210014 Jiangsu Peoples R China

ISBN: (纸本)9783037858646

In a passive millimeter wave (PMMW) imaging system, the resolution of the acquired image is limited by the antenna size. The Richardson-Lucy (rl) algorithm is a simple and nonlinear method, which can improve the resolution of the image. However, when the noise can not be neglected, it is difficult for rl algorithm to get good restoration of the corrupted image. To the best of our knowledge, the block-matching with 3D transform domain collaborative filtering (BM3D) algorithm achieves very good performance in image de-noising. In order to improve the resolution of passive millimeter wave images, a rl imaging algorithm for passive millimeter wave based on BM3D is proposed in this paper. The modified algorithm effectively reduces the influence of noise on rl algorithm by using de-noise algorithm based on BM3D. Experimental results demonstrate that the proposed algorithm improves the performance of rl algorithm. Furthermore, the algorithm can be easily implemented for passive millimeter wave imaging.

关键词： Passive millimeter wave (PMMW) imaging super-resolution rl algorithm BM3D

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Reinforcement learning based model-free optimized trajectory tracking strategy design for an AUV

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NEUROCOMPUTING 2022年 469卷 289-297页

作者： Duan, Kairong Fong, Simon Chen, C. L. Philip Univ Macau Fac Sci & Technol Macau Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

Considering the fact that it is very difficult to fully model an autonomous underwater vehicle (AUV) in the complex water environment, this paper presents a model-free tracking control strategy for an AUV in the presence of unknown disturbances. We first formulate an optimized control problem by defining a track -ing Hamilton-Jacobi-Isaac (HJI) equation. Then, we present a reinforcement learning (rl) algorithm to compute an optimized solution by learning from the HJI equation online. It is noted that during the learn-ing period, no information about the AUV's dynamics is needed. In order to demonstrate the efficiency of the proposed strategy, numerical simulation is considered, results are validated and discussed. (c) 2021 Elsevier B.V. All rights reserved.

关键词： AUV HJI equation rl algorithm Robust control Model-free

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Saturated confocal fluorescence microscopy with linear polarization modulation

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OPTICAL AND QUANTUM ELECTRONICS 2023年第1期55卷 1-12页

作者： Le, Vannhu Le Quy Don Tech Univ 236 Hoang Quoc Viet St Hanoi Vietnam

In confocal scanning fluorescence microscopy, the effective modulation transfer function with Gaussian plane wave illumination covers very few high-frequency components, which prohibits further improvement of the spatial resolution. In this study, we propose saturated confocal scanning fluorescence microscopy with linear polarization to achieve super-resolution imaging. In saturated confocal scanning fluorescence microscopy with linear polarization, the effective modulation transfer function in the Fourier domain is extended in comparison with that of Gaussian plane wave illumination. The digital algorithm is adapted to retrieve the super-resolved image from the modulated recordings. The simulation results demonstrated that saturated confocal scanning fluorescence microscopy with linear polarization could be used to increase the resolution in confocal scanning fluorescence microscopy.

关键词： Confocal fluorescence microscopy (CFM) Super-resolution rl algorithm

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An improved Richardson-Lucy algorithm based on local prior

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OPTICS AND LASER TECHNOLOGY 2010年第5期42卷 845-849页

作者： Wang Yongpan Feng Huajun Xu Zhihai Li Qi Dai Chaoyue Zhejiang Univ Hangzhou 310027 Zhejiang Peoples R China

Ringing is one of the most common disturbing artifacts in image deconvolution. With a totally known kernel, the standard Richardson-Lucy (rl) algorithm succeeds in many motion deblurring processes, but the resulting images still contain visible ringing. When the estimated kernel is different from the real one, the result of the standard rl iterative algorithm will be worse. To suppress the ringing artifacts caused by failures in the blur kernel estimation, this paper improves the rl algorithm based on the local prior. Firstly, the standard deviation of pixels in the local window is computed to find the smooth region and the image gradient in the region is constrained to make its distribution consistent with the deblurring image gradient. Secondly, in order to suppress the ringing near the edge of a rigid body in the image, a new mask was obtained by computing the sharp edge of the image produced using the first step. If the kernel is large-scale, where the foreground is rigid and the background is smoothing, this step could produce a significant inhibitory effect on ringing artifacts. Thirdly, the boundary constraint is strengthened if the boundary is relatively smooth. As a result of the steps above, high-quality deblurred images can be obtained even when the estimated kernels are not perfectly accurate. On the basis of blurred images and the related kernel information taken by the additional hardware, our approach proved to be effective. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Motion deblurring rl algorithm Local prior

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Finite-time adaptive optimal control of uncertain strict-feedback nonlinear systems based on fuzzy observer and reinforcement learning

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2024年第8期55卷 1553-1570页

作者： Sun, Yue Chen, Ming Peng, Kaixiang Wu, Libing Liu, Cungen Univ Sci & Technol Liaoning Sch Elect & Informat Engn 185 Qianshan Middle RdQidashan St Anshan Liaoning Peoples R China Univ Sci & Technol Beijing Sch Automat Beijing Peoples R China Univ Sci & Technol Liaoning Sch Sci Anshan Liaoning Peoples R China Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Shandong Peoples R China

This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (rl) based an optimal control scheme is employed to design a optimal controller, to achieve global optimisation. Additionally, considering the unmeasurable states, we construct a fuzzy observer and utilise fuzzy logic systems to approximate the unknown functions. Meanwhile, the inclusion of command filtering and time-based control simplifies the controller design and enhances the system's response rapidity. Finally, the effectiveness and feasibility of the proposed approach are validated through a numerical simulation and a single link-robot system simulation.

关键词： Adaptive optimal control rl algorithm finite-time fuzzy observer

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Reinforcement learning for optimal policy learning in condition-based maintenance

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2020年第4期2卷 182-188页

作者： Adsule, Aniket Kulkarni, Makarand Tewari, Asim Indian Inst Technol Dept Mech Engn Mumbai Maharashtra India

Condition-based maintenance (CBM) involves taking decisions on maintenance or repair based on the actual deterioration conditions of the components. The long-run average cost is minimised by choosing the right maintenance action at the right time. In this study, the CBM decision-making problem is modelled as a continuous semi-Markov decision process (CSMDP). It consists of a chain of states representing various stages of deterioration, a set of maintenance actions, their costs and scheduled inspection policy. The application of a reinforcement learning (rl) algorithm based on the average reward for CSMDPs in CBM is described. The rl algorithm is used to learn the optimal maintenance decisions and inspection schedule based on the current health state of the component.

关键词： maintenance engineering scheduling condition monitoring optimisation inspection Markov processes decision making decision theory cost reduction inspection policy reinforcement learning optimal maintenance decisions optimal policy learning condition-based maintenance deterioration conditions average cost maintenance action CBM decision-making problem continuous semiMarkov decision process CSMDP rl algorithm inspection schedule component health state

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Control by interconnection of a manipulator arm using reinforcement learning

Control by interconnection of a manipulator arm using reinfo...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： Nageshrao, S. P. Lopes, G. A. D. Jeltsema, D. Babuska, R. Delft Univ Technol DCSC Mekelweg 2 NL-2628 CD Delft Netherlands Delft Univ Technol Delft Inst Appl Math NL-2628 CD Delft Netherlands

ISBN: (纸本)9781479977888

Control by interconnection (CbI) is a dynamic output-feedback approach used to control port-Hamiltonian (PH) systems. Here, both the plant and the controller are modelled in PH form, in terms of their own Hamiltonians. However, obtaining an appropriate controller Hamiltonian is generally difficult. In this paper, we address this issue by using reinforcement learning (rl). Additionally due to the semi-supervised optimization nature of the rl algorithms, a performance criterion can be readily included in CbI. We demonstrate the usefulness of the proposed learning algorithm for stabilization of a manipulator arm.

关键词： feedback learning (artificial intelligence) manipulators optimisation stability CbI PH system rl algorithm control by interconnection controller Hamiltonian dynamic output-feedback approach learning algorithm manipulator arm performance criterion port-Hamiltonian system reinforcement learning semi-supervised optimization stabilization Adaptation models Control systems Cost function Learning (artificial intelligence) Manipulators Mathematical model Symmetric matrices robotic arm Manipulators learning (artificial intelligence) Performance metrics learning algorithms Symmetric matrix Cost functions control systems Mathematical Model Feedback Adaptation models Learning

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Heterogeneous reinforcement learning vibration control of coupling system with four flexible beams connected by springs

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MECHATRONICS 2023年 95卷

作者： Qiu, Zhi-cheng Yang, Yang Zhang, Xian-min South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China

Aiming at studying the vibration characteristics and active control of a coupling system with four flexible beams connected by springs, an experimental platform is built. The dynamic equation of the system is solved by finite element method (FEM), and the parameter model based on state space equation is deduced. In order to ensure the accuracy of the parameter model, an experimental identification method based on wavelet transform and optimization algorithm is adopted. The state matrix, observation matrix and control force coefficient matrix in the parameterized model are solved in turn. A multi-agent based Heterogeneous-Agent Trust Region Policy Optimization (HATRPO) reinforcement learning (rl) algorithm is designed. The HATRPO rl algorithm interacts with the identified parameter model. After several rounds of training, the HATRPO rl vibration controller is finally obtained. The simulation and experimental results show that the HATRPO rl controller can well compensate for the nonlinearity and uncertainty in the multi-flexible beam coupling system. In addition, the nonlinear characteristics of the HATRPO rl algorithm effectively solve the problem of insufficient control power of traditional linear controller in small vibration amplitude, and realize faster vibration suppression.

关键词： Four-flexible beam coupling system Vibration control rl algorithm Experimental identification HATRPO

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Reinforcement learning vibration control of a multi-flexible beam coupling system

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年 129卷

作者： Qiu, Zhi-cheng Yang, Yang Zhang, Xian-min South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China

An active vibration control algorithm based on reinforcement learning (rl) is applied to suppress the coupling vibration of a multi-flexible beam coupling system. The experimental setup of four-flexible beam coupling system is constructed. Piezoelectric sensors/actuators are used to detect vibration signals and suppress vibration. The finite element method (FEM) is used to establish the system dynamics model, and the model is modified by identifying parameters using the experimental data to obtain an accurate system model. The identified model is used as the simulation environment of rl algorithm. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is designed to train the rl vibration controller through interaction with the simulation environment. The trained rl vibration controller is used to suppress the vibration of the four-flexible beam coupling system in simulation and experimental environment. Simulation and experimental results show that compared with proportional and derivative (PD) controller, the rl controller trained by the MATD3 algorithm has better control effect, especially for small amplitude vibration. (C) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Multi-flexible beam coupling system Active vibration control rl algorithm Model identification MATD3

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