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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Li, Xin Li, Junhao Yu, Xiyu Zhang, Ruikun Qingdao Univ Sci & Technol Sch Math & Phys Qingdao 266061 Peoples R China

ISBN: (纸本)9798350321050

This paper investigates the composition and identification of ancient glass artefacts. First, the collected data were pre-processed to determine the chemical composition content of the glass artefacts before weathering. Then, based on the variable control method, the effects of decoration and colour of two types of glass, high potassium glass and lead-barium glass, on the weathering of artefacts were determined. The statistical law of the presence or absence of weathering chemical composition on the surface of artefact samples was summarized by establishing a weathering change rate solution model. Meanwhile, based on the weathering change rate, the solution model for the content of chemical substances before weathering is summarized, and the content of different chemical components in ancient glass artefacts before weathering is predicted. Next, the second problem is carried out, which is to classify the glass and select the appropriate chemical composition for subclassification. Ancient glass artefacts were classified into the high-potassium glass and lead-barium glass based on their chemical composition and content, and the classification rules were analyzed. On this basis, the top eight indicators of chemical composition differences between the two types of artefacts were determined by comparison and subclassified by the K-means algorithm. Finally, model experiment results verified the correctness and validity of the models proposed in this paper.

关键词： control Variates Subclass Division Rate of Change in Weathering K-means Clustering

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Prediction of HVAC Energy Consumption Using PSO Optimized Deep Neural Network 12

Prediction of HVAC Energy Consumption Using PSO Optimized De...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Du, Linhui Li, Xiaoli Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Beijing 100124 Peoples R China CUC Sch Int Studies Beijing 100024 Peoples R China

ISBN: (纸本)9798350321050

Heating, Ventilation and Air Conditioning (HVAC) system is a highly nonlinear system with a large amount of complex, coupled inputs. This paper presents a novel prediction method for HVAC energy consumption based on deep neural network (DNN). In order to solve the problem that traditional neural networks tend to fall into local optima, batch normalization and Adam optimization algorithm are significantly incorporated in the DNN. In addition, particle swarm optimization (PSO) is utilized to search for the optimal number of hidden layer nodes and increase the accuracy of prediction model. The cooling tower data of HVAC is used to validate the network. The results show the mean absolute error and the mean square error of the PSO-DNN model, from which it can be seen obviously that our proposed prediction model performs better than traditional ones. Accordingly, DNN optimized by PSO algorithm is of great validity and superiorities for energy consumption prediction of HVAC system.

关键词： Energy Consumption Deep Neural Network HVAC data-driven PSO Algorithm

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LTV System Identification via a Kernel-based Regularization Method under Savitzky-Golay Filtering 12

LTV System Identification via a Kernel-based Regularization ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yu, Xian Ji, Xiaoqiang Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9798350321050

An important task in the identification community for time-varying systems is to track the possible changes in system dynamics as well as possible. For the currently applied identification techniques, this task is usually implemented at the cost of increasing the computational quantity as the time increases, or of adding a forgetting factor that requires to be determined a priori. In this paper we develop an estimation approach for linear time-varying systems with additive disturbances, which achieves a major computational advantage without determining the additional factor. In particular, we integrate the idea behind Savitaky-Golay filtering into the kernel-based regularization method under the framework of regularized least squares. It is found that the developed approach remains the same computational quantity during the whole time interval in the estimating procedure. A numerical example is simulated to validate the effectiveness of the developed approach.

关键词： System Identification Linear Time-varying System Savitaky-Golay Filtering Kernel-based Regularization Method

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Boundary Optimal control for Parabolic Distributed Parameter systems With Value Iteration

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ieee TRANSACTIONS ON CYBERNETICS 2024年第3期54卷 1571-1581页

作者： Sun, Jingyi Luo, Biao Xu, Xiaodong Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China

A reinforcement learning-based boundary optimal control algorithm for parabolic distributed parameter systems is developed in this article. First, a spatial Riccati-like equation and an integral optimal controller are derived in infinite-time horizon based on the principle of the variational method, which avoids the complex semigroups and operator theories. Using state data along the system trajectory, a value iteration algorithm via the Bellman optimality principle is proposed to obtain the solution of the spatial Riccati-like equation and the optimal control law. The convergence of the value iteration algorithm is proved. Subsequently, an approximation scheme based on weighted residuals is developed to implement the value iteration algorithm, where radial basis functions are chosen as the basic functions to approximate the solution of the spatial Riccati-like equation. Simulations on the diffusion-reaction process demonstrate the effectiveness of the developed method.

关键词： Boundary control distributed parameter systems (DPSs) optimal control reinforcement learning (RL) value iteration

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Intelligent fault diagnosis for unknown faults of rotating machinery based on the CNN and the DCGAN 12

Intelligent fault diagnosis for unknown faults of rotating m...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yu, Gongye You, Yapeng Ma, Bo Han, Yongming Beijing Univ Chem Technol Key Lab Engine Hlth Monitoring Control & Networki Minist Educ Beijing 100029 Peoples R China Beijing Univ Chem Technol Beijing Key Lab Hlth Monitoring & Self Recovery H Beijing 100029 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9798350321050

The fault diagnosis model based on machine learning can only achieve accurate recognition of the fault types included in the training, but in practical applications, it is limited by the classification mechanism of the diagnosis model and cannot achieve the recognition of new faults, which is unknown faults. To address this problem, this paper proposes a fault identification method for rotating machinery based on the Convolutional Neural Networks (CNN) and the Deep Convolutional Generative Adversarial Network (DCGAN) to identify unknown faults of rotating machinery. The method first trained CNN with each known class data to build the initial diagnosis model, and trained DCGAN to obtain the discriminative network of each known class to build the confidence probability calculation model, and then the results of the initial diagnosis were corrected according to the confidence probability, and finally the intelligent diagnosis of the known and unknown class faults was realized. The analysis results of the centrifugal pump fault simulation showed that the proposed method achieved an average diagnostic accuracy of 96.16% and 91.79% for known and unknown faults, respectively.

关键词： Fault Diagnosis Unknown Faults Generating Adversarial Networks Confidence Probability

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Iterative learning Model Predictive control With Fuzzy Neural Network for Nonlinear systems

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ieee TRANSACTIONS ON FUZZY systems 2023年第9期31卷 3220-3234页

作者： Han, Hong-Gui Wang, Chen-Yang Sun, Hao-Yuan Yang, Hong-Yan Qiao, Jun-Fei Beijing Univ Technol Fac Informat Technol Beijing Key Lab Computat Intelligence & Intelligen Engn Res Ctr Digital CommunityMinist EducBeijing Beijing 100021 Peoples R China Beijing Univ Technol Beijing Lab Urban Mass Transit Beijing 100021 Peoples R China

Due to the existence of strong nonlinearity and external disturbances, the controller design of complex nonlinear systems is a challenging problem. Therefore, it is necessary to design an effective robust predictive controller for this issue. In this article, based on a fuzzy neural network, an iterative learning model predictive control (FNN-ILMPC) is designed for complex nonlinear systems. First, a dynamic linearization technique is used to establish a data-driven model, which only relies on input and output data. Since the established model contains an unknown disturbance term that may have an impact on the control performance, an FNN is used to evaluate the disturbance so that the uncertainty of the system is captured. Subsequently, based on the above data-driven model, an FNN-ILMPC strategy, considering the impact of external disturbances, is developed to eliminate the influence of disturbances. Then, it is proved that the designed controller can make both modeling error and tracking error decrease gradually and ensure the closed-loop system stability. Finally, the experimental results verify the effectiveness and superiority of the designed controller.

关键词： data-driven dynamic linearization fuzzy neural network (FNN) iterative learning control (ILC)

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A Reinforcement learning-Based Distributed control Scheme for Cooperative Intersection Traffic control

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ieee ACCESS 2023年 11卷 57037-57045页

作者： Guzman, Jose A. Pizarro, German Nunez, Felipe Pontificia Univ Catolica Chile Dept Elect Engn Santiago 7820436 Chile

Traffic congestion is a major source of discomfort and economic losses in urban environments. Recently, the proliferation of traffic detectors and the advances in algorithms to efficiently process data have enabled taking a data-driven approach to mitigate congestion. In this context, this work proposes a reinforcement learning (RL) based distributed control scheme that exploits cooperation among intersections. Specifically, a RL controller is synthesized, which manipulates traffic signals using information from neighboring intersections in the form of an embedding obtained from a traffic prediction application. Simulation results using SUMO show that the proposed scheme outperforms classical techniques in terms of waiting time and other key performance indices.

关键词： Predictive models Detectors Real-time systems data models Urban areas Graph neural networks Decentralized control Reinforcement learning cyber-physical systems intersection control distributed control

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data-driven Guidance and control for Asteroid Landing Based on Real-Time Dynamic Mode Decomposition

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ieee ACCESS 2023年 11卷 52622-52635页

作者： Kajikawa, Taiga Shiotsuka, Tatsuya Bando, Mai Hokamoto, Shinji Kyushu Univ Dept Aeronaut & Astronaut Fukuoka 8190395 Japan

Navigation and control near asteroids are challenging due to high levels of uncertainty, necessitating a high degree of autonomy and safety. To address this challenge, we propose a novel method for leveraging data-driven approaches, which have garnered significant attention in recent years. Traditional data-driven and machine learning techniques have been applied to space exploration problems;however, they often require large amounts of data in advance and are not robust to unknown environments. Hence, they are typically used in auxiliary roles and are not easily adaptable to real-time modeling and control. We propose the use of the theory of dynamic mode decomposition (DMD) and delayed embedding to construct a data-driven real-time guidance and control system for asteroid landings. Our approach employs low-dimensional information obtained from onboard sensors to construct nonlinear dynamics in the vicinity of asteroids. Based on the constructed dynamical model, we propose a guidance control strategy for accurate and reliable spacecraft landing. Our proposed method has the potential to improve guidance and control systems for space exploration by enabling real-time modeling and control with reduced computational cost.

关键词： Asteroids Satellites Space vehicles Analytical models Real-time systems data models Planets Aerospace control data-driven landing control real-time performance delayed embedding

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Active Disturbance Rejection control of Diesel Engine Air System Based on Online Optimization of Observation Bandwidth and control Gain 12

Active Disturbance Rejection Control of Diesel Engine Air Sy...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Liu, Xingyi Ren, Yuru Jiang, Nan Feng, Jianwei Wang, Biao Song, Kang Xie, Hui Weichai Power Weifang 262599 Peoples R China Tianjin Univ Tianjin 300192 Peoples R China

ISBN: (纸本)9798350321050

In this paper, a parameter self-learning multivariable active disturbance rejection control (ADRC) algorithm is proposed for the Diesel engine air system equipped with a variable exhaust gas turbocharger (VGT), exhaust gas recirculation (EGR), and intake throttle valve (TVA). Firstly, based on the principle of simplified Diesel engine operation, a three-in-three-out linear variable parameter air system prediction model is established, with the model deviation equivalent to the total disturbance. A multivariable extended state observer is employed for active observation to enhance the model's adaptability. Secondly, to address the issues of strong noise and variable signal-to-noise ratio of the air signal, the gain matrix of the extended state observer is dynamically adjusted to achieve a real-time trade-off between observation speed and disturbance fluctuation amplitude. Finally, in order to improve tracking accuracy while compensating for the total disturbance, extremum seeking is utilized to optimize the control input gain online and enhance the algorithm's adaptability to nonlinearities. The algorithm is verified on a Diesel engine stand, and compared with a PID controller with global parameter optimization, it achieves the same response speed while reducing the overshoot of intake manifold pressure and EGR rate tracking by 24.9% and 10.3%, respectively. The proposed algorithm's parameters can be automatically adjusted with the change of working conditions, avoiding the need for sectional calibration.

关键词： air system extremum seeking parameter self-learning

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Dynamic Iterative learning control for Relative Trajectory Tracking with Nonrepetitive Leader Path 13

Dynamic Iterative Learning Control for Relative Trajectory T...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Wang, Yiming Wang, Guina Tao, Hongfeng Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China

ISBN: (纸本)9798350361674

This paper proposes a collaborative iterative learning control scheme with optimal leader path planning strategy. Due to the demonds on repetitive tasks and static references under traditional ILC framework, it cannot fit the complex circumstances with multiple collaborative systems or satisfy certain control request on specific application. This paper introduces a dynamic norm-optimal iterative learning control (NOILC) framework based on an improved coordinate descent algorithm. Under the aforementioned framework, an optimal leader path can be found to fit certain performance requirements, i.e. energy reduction. Finally, the simulation results on a combined complex system verify the effectiveness of the proposed algorithm. © 2024 ieee.

关键词： Convex optimization

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