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

作者： Chen, Zhengquan Huang, Ruirui Zhang, Zhiheng Hou, Yandong Henan University School of Computer and Information Engineering Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

This paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method. © 2024 ieee.

关键词： Adversarial machine learning

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Path Planning Algorithm for Manipulator Based on Manipulability-Maximizing 13

Path Planning Algorithm for Manipulator Based on Manipulabil...

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

作者： Zhang, Rui Liu, Shan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

An improved path planning algorithm based on RRT∗ is proposed for the manipulator to generate a collision-free path for end-effector in this paper, which maximizes the manipulator's manipulability throughout the entire path. The algorithm introduces manipulability information as prior information to guide the RRT node toward desired regions. By introducing hybrid cost into improved RRT* algorithm, the path length is minimized while the expected manipulability is maximized. Finally, the end-effector path is transformed into a configuration trajectory by solving a multi-objective optimization problem. The simulation results compared with RRT* demonstrates the effectiveness of the proposed algorithm. © 2024 ieee.

关键词： End effectors

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Wind Speed Prediction Based on ARMA and SVR 12

Wind Speed Prediction Based on ARMA and SVR

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

作者： Jiao, Xuguo Zhang, Daoyuan Yang, Qinmin Zhang, Zhenyong Liu, Wenfeng Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China

ISBN: (纸本)9798350321050

Wind power generation technology is one of the research hotspots of renewable energy nowadays. In order to ensure the stable and reliable operation of wind power generation equipment, wind speed prediction is very important. This paper provides a new idea for the wind speed prediction based on Autoregressive Moving Average (ARMA) and Support Vector Regression (SVR). First, to reasonably divide the original data into multiple historical data with strong correlation as features to predict the future wind speed, the ARMA model is employed and its partial autocorrelation coefficient is calculated. By this means, the input features can be optimally selected and the training set of the prediction model can be constructed. Further, SVR model is used to build the nonlinear relationship between the input features and future wind speed. Finally, through simulation, it proves that this method saves more time than try and error method in selecting input features, and through comparison with Backpropagation Neural Network (BPNN), it proves that this method can achieves higher wind speed prediction accuracy.

关键词： Wind Speed Prediction Time Series Autoregressive Moving Average (ARMA) Support Vector Regression (SVR)

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data driven Multi-Agent Deep Reinforcement learning Based Operational Optimization of Industrial Aerodynamic System 13

Data Driven Multi-Agent Deep Reinforcement Learning Based Op...

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

作者： Huang, Yunxiang Zhong, Lulu Liu, Yang Zhao, Jun Wang, Wei Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Industrial aerodynamic system (IAS) involves a series of compressors to provide the compressed air for consumer of steel industrial park, which consumes a substantial amount of electrical energy and attracts a considerable attention for energy conservation and efficiency enhancement. The usage pattern of these compressors, influenced by the cyclic and intermittent nature of production schedules, leads to varying operational profiles. Traditional manual adjustments are often inadequate due to the differ in expert knowledge, resulting in increased energy consumption. To tackle this challenge, we propose an innovative approach termed as data-driven multi-agent deep reinforcement learning (DMA-DRL) to achieve the operational optimization of IAS. Initially, we construct a multi-operational model for the air compressor group by leveraging historical data from industrial sites and understanding equipment operation principles;Considering the constraints in terms of production process conditions and equipment safety, we establish a data-driven optimization scheduling method for air compressor units based on multi-agent deep reinforcement learning, which ensures the secure and stable operation of the system under complex conditions. Finally, the proposed method is applied to the performance testing of an industrial park air compressor simulation system, which outperforms the various optimization strategies in aspect of operational cost and energy efficiency. © 2024 ieee.

关键词： Energy efficiency

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Fault Diagnosis and Location Research for Distributed control System of Offshore Wind Turbines 13

Fault Diagnosis and Location Research for Distributed Contro...

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

作者： Wang, Shaoping Wang, Ronghao Duan, Zhaoxia College of Electrical and Power Engineering Hohai University Nanjing211100 China College of Defense Engineering Army Engineering University of Pla 210007 China College of Artificial Intelligence and Automation Hohai University Nanjing211100 China

ISBN: (纸本)9798350361674

A communication fault diagnosis and positioning scheme is designed according to the characteristics of the distributed control system of offshore wind turbine. Firstly, to simplify the communication link between distributed system units, effectively control the topology between fan networks, and divide the wind farm of distributed wind turbines. Then, using heartbeat detection algorithm and heartbeat mechanism to diagnose the communication master-slave node of distributed system, the unit fault localization is completed. Finally, through simulation validation and comparison with traditional algorithms, it is shown that the distributed fault diagnosis and positioning scheme proposed in this paper has high diagnostic and positioning accuracy for communication faults in distributed control systems. © 2024 ieee.

关键词： Offshore wind turbines

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Finite-Set Direct Torque control via Edge-Computing-Assisted Safe Reinforcement learning for a Permanent-Magnet Synchronous Motor

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ieee TRANSACTIONS ON POWER ELECTRONICS 2023年第11期38卷 13741-13756页

作者： Schenke, Maximilian Haucke-Korber, Barnabas Wallscheid, Oliver Paderborn Univ Dept Power Elect & Elect Dr D-33098 Paderborn Germany

Advances in the field of reinforcement learning (RL)-based drive control allow formulation of holistic optimization goals for the data-driven training phase. The resulting controllers feature efficient drive operation without the necessity of an a priori known plant model but, so far, conduction of the corresponding training phase in real-world drive systems has been applied only sparsely due to safety concerns. This contribution targets the challenging problem of self-learning torque control for a permanent-magnet synchronous motor assuming a finite control set, i.e., the direct selection of switching actions instead of a modulator-based setup. In order to allow a secure and effective online training with real-world drive systems, the RL controller is monitored by a safeguarding algorithm that prevents application of unsafe switching actions, e.g., such that result in overcurrent. The accruing amount of measurement data is handled with the use of an edge-computing pipeline to outsource the RL training from the embedded control hardware. The inference of the utilized artificial neural network in hard real time is realized with the use of a reconfigurable field-programmable gate array architecture. The resulting RL-based algorithm is able to learn a torque control policy in just 10 min, which has been validated during comprehensive real-world experiments.

关键词： data-driven optimal control deep reinforcement learning (RL) direct torque control (DTC) edge computing electric drive field-programmable gate array (FPGA) Internet of Things neural network safe learning synchronous motors system identification

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Robustness Performance Improvement for Input-Delay systems: An Additive-State- Decomposition- Based control Method 13

Robustness Performance Improvement for Input-Delay Systems: ...

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

作者： Ren, Jinrui Du, Jiaxi Che, Yu Quan, Quan School of Automation Xi'an University of Posts & Telecommunications Xi'an710121 China Xi'an Microelectronics Technology Institute Xi'an710000 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350361674

Time delays exist in a large number of practical systems, and it is meaningful to study the robust performance improvement of time-delay systems. This paper proposes an additive-state-decomposition-based robust performance improvement control method for time-delay linear systems. First, additive state decomposition is performed on the studied time-delay system. Then, a state feedback controller is designed based on the decomposed primary system. In addition, the small-gain theorem is also used to prove that the additive-state-decomposition-based control method can enhance the robustness of time-delay systems compared with the traditional state feedback control method. Finally, simulations verify that the method based on additive state decomposition outperforms the traditional state feedback control method in terms of robustness performance improvement. © 2024 ieee.

关键词： Delay control systems

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Design and implementation of an experiential learning intervention for a graduate course in system identification

Design and implementation of an experiential learning interv...

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ieee conference on control Technology and Applications (CCTA)

作者： Wi, Yejin Cescon, Marzia Univ Houston Dept Mech & Aerosp Engn Houston TX 77204 USA

ISBN: (纸本)9798350370959;9798350370942

Teaching system identification for control to aerospace engineering students can significantly benefit from actual experiential learning activities in a laboratory environment. In this paper, we present the experience we gathered performing hands-on project-based experimental activities conducted with a multirotor Unmanned Aerial Vehicle (UAV) within a graduate-level course teaching how to learn linear models of dynamical systems from data. We developed the activities to complement face-to-face lectures to illustrate the steps involved in the process of identifying data-driven models in a hands-on manner, and implemented them in the form of a project during the last 8 weeks of the course, to consolidate the knowledge acquired in the theoretical lectures through practical experiences. We present the lecture material we prepared, provide an overview of the obtained results, discuss the pedagogical value of the experiential learning activities, summarize student's feedback and give an outline for further improvements.

关键词： Unmanned aerial vehicles (UAV)

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Automatic learning-Based Calibration of Assisted Motorcycle Gearshift: A Comparative Study

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ieee TRANSACTIONS ON control systems TECHNOLOGY 2025年

作者： Catenaro, Edoardo Sabug Jr, Lorenzo Panzani, Giulio Sette, Davide Ruiz, Fredy Fagiano, Lorenzo Savaresi, Sergio M. Politecn Milan Dept Elect Informat & Bioengn Milan Italy Ducati Motor Holding I-40132 Bologna Italy

A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from na & iuml;ve space-filling approaches to learning-based black-box optimization algorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result.

关键词： Engines Gears Torque Motorcycles Wheels Optimization Tuning Logic control systems Vehicles Automatic tuning global optimization algorithms machine learning powertrain control

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Neural ODEs for data-driven Automatic Self-Design of Finite-Time Output Feedback control for Unknown Nonlinear Dynamics

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ieee control systems LETTERS 2023年 7卷 3048-3053页

作者： Bachhuber, Simon Weygers, Ive Seel, Thomas FAU Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn D-91052 Erlangen Germany

Many application fields, e.g., robotic surgery, autonomous piloting, and wearable robotics greatly benefit from advances in robotics and automation. A common task is to control an unknown nonlinear system such that its output tracks a desired reference signal for a finite duration of time. A learning control method that automatically and efficiently designs output feedback controllers for this task would greatly boost practicality over time-consuming and labour-intensive manual system identification and controller design methods. In this contribution we propose Automatic Neural Ordinary Differential Equation control (ANODEC), a data-efficient automatic design of output feedback controllers for finite-time reference tracking in systems with unknown nonlinear dynamics. In a two-step approach, ANODEC first identifies a neural ODE model of the system dynamics from input-output data of the system dynamics and then exploits this data-driven model to learn a neural ODE feedback controller, while requiring no knowledge of the actual system state or its dimensionality. In-silico validation shows that ANODEC is able to -automatically- design competitive controllers that outperform two controller baselines, and achieves an on average & AP;30 % / 17 % lower median RMSE. This is demonstrated in four different nonlinear systems using multiple, qualitatively different and even out-of-training-distribution reference signals.

关键词： Autonomous systems data-driven modeling learning systems motion control neural networks

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