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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. This study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 The Author(s).

关键词： Offshore wind turbines

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Situational control Based on Fuzzy Neural Networks in Continuous Production Management 17

Situational Control Based on Fuzzy Neural Networks in Contin...

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17th International Conference on Management of Large-Scale System development, MLSD 2024

作者： Baryshnikova, Elena Ivashchenko, Vladimir Fursov, Sergey Research Institution Saratov Federal Scientific Centre of the Russian Academy of Sciences Institute of Precision Mechanics and Control - Subdivision of the Federal State Budgetary Saratov Russia V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Large-Scale Systems Development Moscow Russia

ISBN: (纸本)9798350375718

The article considers the problem of continuous production management. The technological process of sheet glass production is chosen as an example. We have analyzed the process of sheet glass forming. We have described the main stages of production. They propose a model based on situational control to develop a control system for glass production. © 2024 IEEE.

关键词： Production control

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Frontiers in robotics and Electromechanics 1

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丛书名： Smart Innovation, systems and Technologies

2023年

作者： Andrey Ronzhin Viacheslav Pshikhopov

ISBN: (数字)9789811976858

ISBN: (纸本)9789811976841;9789811976872

This book introduces intellectual control systems and electromechanics of heterogeneous robots. The book uncovers fundamental principles of robot control and recent developments in software and hardware of robots. The book presents solutions and discusses problems of single robotic devices as well as heterogeneous robotic teams while performing technological tasks that require informational, physical or energetic interaction with human users, environment and other robots. The book considers model–algorithmic and software–hardware control of ground, water and underwater robots, unmanned aerial vehicles, as well as their embedded and attached sub-systems, including manipulators, end-effectors, sensors, actuators, etc. The book will be useful for researchers of interdisciplinary issues related to robotics, electromechanics and artificial intelligence. The book is recommended for graduate students with a major/minor in the areas of robotics and mechatronics, management in technical systems, Internet of Things, artificial intelligence, electrical engineering, mechanical engineering and computer science.

关键词： control, robotics, Mechatronics Artificial Intelligence Computational Intelligence Mechanical Engineering

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Lifted Bilinear Model-based Linear Model Predictive control with Scalability

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IFAC-PapersOnLine 2023年第2期56卷 9405-9410页

作者： Masaki Kanai Masaki Yamakita Controls and Robotics Innovation Center Research & Development Group Ltd. Hitachi Ibaraki Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

We propose a novel linear model predictive control (MPC) using a lifted bilinear model based on Koopman theory, which is computationally scalable against the dimension of the target system and the prediction horizon length. In MPC, the accuracy of the prediction model determines control performance, but it is a challenge to reduce the computational cost especially when considering nonlinearity of the model. To address this, a method has been proposed which represents the nonlinear input affine system as a lifted bilinear model and utilizes linear approximation and prediction error correction regarding the lifted state to achieve a low-computational-cost linear MPC with equivalent performance to nonlinear MPC. However, although the previous studies have shown its effectiveness for relatively low-order systems, it has not been applied to practical systems with higher dimensions. In this study, we extend the conventional method and propose a scalable linear MPC using a lifted bilinear model and apply it to higher-order nonlinear systems. In this paper, a quadrotor system operating in three-dimensional space is considered and its analytical lifted bilinear model is derived. In the formulation of linear MPC using the lifted bilinear model, an error correction method is newly introduced to feedback error for adjustment of the numerical relationships among the elements in the lifted state. The effectiveness of the proposed method is demonstrated through numerical simulations.

关键词： Model predictive control Koopman theory Lifting linearization Bilinear system

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet 42

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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42nd Chinese control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electronic nose

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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Parallelizable Parametric Nonlinear System Identification via tuning of a Moving Horizon State Estimator 63

Parallelizable Parametric Nonlinear System Identification vi...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Simpson, Leo Asprion, Jonas Muntwiler, Simon Kohler, Johannes Diehl, Moritz Research and Development Tool-Temp Ag Switzerland University of Freiburg Department of Mathematics Freiburg79104 Germany Institute for Dynamic Systems and Control Eth Zürich Zürich8092 Switzerland Freiburg79110 Germany

ISBN: (纸本)9798350316339

This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used for linear system identification, we extend its capabilities to nonlinear systems. The predictions are computed using a moving horizon state estimator with a constant arrival cost. Eventually, both the system parameters and the arrival cost are estimated by minimizing the sum of the squared prediction errors. Since the predictions are induced by the state estimator, the method can be viewed as the tuning of a state estimator, based on its predictive capacities. The present extension of the prediction error method not only enhances performance for nonlinear systems but also enables learning from multiple trajectories with unknown initial states, broadening its applicability in practical scenarios. Additionally, the novel formulation leaves room for the design of efficient and parallelizable optimization algorithms, since each output prediction only depends on a fixed window of past actions and measurements. In the special case of linear time-invariant systems, we show an important property of the proposed method which suggests asymptotic consistency under reasonable assumptions. Numerical examples illustrate the effectiveness and practicality of the approach, and one of the examples also highlights the necessity for the arrival cost. © 2024 IEEE.

关键词： Estimation Nonlinear systems Identification Predictive control for nonlinear systems

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Cloud Edge Collaborative Interaction Technology Based On Compzxutility Boards 4

Cloud Edge Collaborative Interaction Technology Based On Com...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Xie, Yehua Zhang, Chuanyu Chang, Bo Zhou, Jianxin Liu, Hongkun Wang, Xiaojin Nari Group Corporation State Grid Electric Power Research Institute Jiangsu China Nari Technology Development Co. Ltd Jiangsu China NARI-TECH Nanjing Control Systems Ltd Jiangsu China

ISBN: (纸本)9798350363586

The cloud-edge collaborative interaction technology, leveraging computing power boards, comprises cloud application management services, edge application management clients, and computing power boards. This technology and system serve as a bridge for application and data interaction between cloud and edge systems. The application management service is hosted in the cloud, while the application management client resides on the edge, and the computing power board is integrated into the physical server on the edge. The application management service encompasses modules like application management and edge management. The computing power board facilitates the subscription and deployment of lightweight AI large model applications at the edge. This technology enables the publication, subscription, downloading, and deployment of applications, standardizes application management, facilitates seamless upgrades, enables remote operation and maintenance, and enriches the application ecosystem. © 2024 IEEE.

关键词： Mobile edge computing

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Incremental Model Predictive control for Velocity-controlled Robot Manipulators 50

Incremental Model Predictive Control for Velocity-Controlled...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Wang, Yongchao Li, Hengrui Li, Cong Liu, Fangzhou School of Aerospace Science and Technology Xidian University Xi’an China Research and Development Department Agile Robots Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665464543

In this article, an incremental model predictive controller is proposed for the velocity-controlled robot manipulator. First, the time-delay estimation (TDE) technique is used to approximate the unknown discrepancy between the command and the real joint velocity due to velocity dynamics, and the equation of motion in the incremental form is obtained. Then, on the basis of the resulting equation of motion and taking into account joint position and velocity constraints, the incremental model predictive controller is developed by formulating a constrained optimal control problem (OCP). The constrained OCP is cast to a quadratic programming (QP) problem, making it possible to employ computationally efficient QP solvers to solve the constrained OCP. As a result, real-time control is guaranteed. Finally, experiments on a robot manipulator are implemented to verify the effectiveness of the developed incremental model predictive controller. © 2024 IEEE.

关键词： Predictive control systems

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Node Classification in CAD Data: A Graph Learning Approach for Machining Feature Localization 4

Node Classification in CAD Data: A Graph Learning Approach f...

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4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Bohm, Stefan Andreas Neumayer, Martin Song, Rui Rib, Fabian Knoll, Alois Technical University of Munich Chair of Robotics Artificial Intelligence and Real-Time Systems Munich Germany Rosenheim Technical University of Applied Sciences Research and Development Department Rosenheim Germany Fraunhofer Institute for Transportation and Infrastructure Systems Ingolstadt Germany Rosenheim Technical University of Applied Sciences Faculty of Engineering Sciences Rosenheim Germany

ISBN: (纸本)9798350391183

Pattern recognition is becoming increasingly important in many sectors, such as the manufacturing industry. With rising customer demands and advancing technology, product data, often represented as 3D meshes, is becoming highly complex. Therefore, intelligent approaches are needed to recognize and localize features in 3D meshes to determine complex manufacturing processes. However, previous approaches solely work on voxel-, pixel-or. STEP-based formats and not directly on mesh-based formats such as STL data. To overcome these issues, we present a configurable data generator and customize graph neural network architectures for the problem of localizing machining features directly in 3D meshes. After comparing the graph neural networks against each other, the best resulting network is compared to an existing state-of-The-Art approach, i.e., SsdNet. Our results indicate that the best-performing graph neural network exhibits superior classification accuracies on meshes with few machining features compared to SsdNet with an average F1 score of 80%. While the accuracy slightly falls behind SsdNet with more features, the graph neural network shows substantially better runtime performance results, which are, on average, 70 times faster. © 2024 IEEE.

关键词： Graph neural networks

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