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Automatic Drawbridge Scheduling by Integrating Highway and Waterway Traffic Flow Information

IFAC-PapersOnLine 2024年第10期58卷 290-295页

作者： M.J. Mooij J.R. Dominguez Frejo V. Reppa Department of Maritime and Transport Technology Delft University of Technology Netherlands Department of System Engineering and Automatic Control University of Seville Spain

As inland waterway usage intensifies, it increasingly intersects and conflicts with railway and road transportation modes, highlighting the need for efficient management of these critical junctures. In particular, movable bridges represent a key intersection of highway and waterway Traffic but also a potential source of conflict. This paper proposes and analyzes the use of automatic schedulers that consider both highway and waterway Traffic, reduce conflicts, and make key decisions about bridge use. The METANET macroscopic Traffic model is elaborated to allow the simulation of drawbridge openings on the highway mainline, based on the modelling of mainstream metering. Several MPC-based schedulers are proposed using the designed highway Traffic model and key vessel information, aiming to study the impact of the bridge opening and scheduling on highway Traffic. The simulation results indicate a significant reduction in Traffic conflicts at drawbridge intersections due to the implementation of these schedulers. The functioning of the schedulers is shown to be robust and demonstrate verifiable behavior, indicating their high potential in real-world applications.

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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An Automotive Headlight Leveling control Strategy with Rough Road Identification 42

An Automotive Headlight Leveling Control Strategy with Rough...

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42nd Chinese control Conference, CCC 2023

作者： Zhao, Xinyi Zhao, Linhui Deng, Zhichong Ji, Guohuang Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Intelligent Power System Co. Ltd. Wuhan430056 China

ISBN: (纸本)9789887581543

The Automotive Headlamp Leveling system (ALS), aims to ensure the stability of the illumination range of the headlamp by vertically rotate the headlamp using motors when the body attitude changes. This paper proposes an ALS scheme which obtains the body attitude from body height sensors and accordingly calculates the output angles of left headlamp and right headlamp respectively based on Euler transformation. Different from existing ALS, this scheme uses several strategies to improve the performance of ALS, including anti-shake function, working mode division and rough road identification. The purpose of rough road identification is to avoid unexpected vibration of headlamp caused by ALS on rough road and decision tree model is chosen for identification to improve the accuracy. Moreover, several field tests are performed to verify the validity and stability of the system. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision trees

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Learning control Applied to a Digital-to-analogue Converter

Learning Control Applied to a Digital-to-analogue Converter

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Van Rijt, Noa Faza, Ahmad Oomen, Tom Eielsen, Arnfinn A. Eindhoven University Control System Technology Research Group EindhovenAZ 5612 Netherlands Stavanger University Department of Energy and Petroleum Engineering Stavanger4021 Norway

ISBN: (纸本)9798350335446

Digital-to-analogue converters (DACs) exhibit several non-ideal effects that deteriorate performance. Methods in feedback control can reduce such effects. Due to implementation limitations, the feedback signal in existing schemes is produced by open-loop observers, known as ΔΣ-modulation, that mitigate the observed adverse effects only partially. Measurement feedback can compensate for non-ideal behaviour and disturbances that are difficult to model. Learning control (LC) is introduced to overcome practical problems of measurement feedback in DACs, therewith omitting the need for accurate open-loop observers. Experimental results demonstrate a 95% improvement in RMS error when using LC with measurement feedback, compared to ΔΣ-modulation using observer feedback. © 2023 IEEE.

关键词： Learning algorithms

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Torque Ripple Effects According to Hysteresis Motor Closed-Slot Structures Geometry with Finite Element Analysis

Torque Ripple Effects According to Hysteresis Motor Closed-S...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Jong-Hoon Park Ji-Hoon Han Seung-Min Song Sun-Ki Hong Department of Information Control Engineering Hoseo University Asan Korea Department of System and Control Engineering Hoseo University Asan Korea

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

FEA (Finite Element Analysis) of conventional hysteresis motors has been difficult due to the specificity of the rotor. Equivalent circuit-based analysis is common, and it is difficult to expect the torque ripple due to the slot structure. However, recent advances in FEM (Finite Element Method) technology have made it possible to consider the nonlinear characteristics of the rotor. By utilizing this, the torque ripple and torque depending on the stator bridge angle, which affects the torque ripple of the motor, were compared. This is expected to enable geometry optimization of hysteresis motors, which is difficult with conventional methods.

关键词： Geometry Torque Shape Rotors Hysteresis motors Stators Finite element analysis Torque measurement Optimization Equivalent circuits

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yu, Guanfeng Zhang, Han Wang, Zehao Wang, Jingchuan Shanghai Jiao Tong University Institute of Medical Robotics Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities. © 2024 IEEE.

关键词： Iterative methods

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Electromagnetic Field Analysis Using Physics Informed Neural Network Considering Eddy Current

Electromagnetic Field Analysis Using Physics Informed Neural...

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IEEE Conference on Electromagnetic Field Computation

作者： Ji-Hoon Han Jong-Hoon Park Seung-Min Song Sun-Ki Hong Department of information control engineering Hoseo University Asan Korea Department of system and control engineering Hoseo University Asan Korea

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

The exploration of PINN (Physics Informed Neural Network) in research is still in its nascent stages globally, with a notable dearth of studies focusing on electromagnetic field analysis. In response to this gap, this paper introduces a novel approach for eddy current analysis employing a transfer learning-based discrete differentiation method within the framework of a PINN. While discrete differentiation methods offer the advantage of low model complexity and rapid analysis, they encounter challenges in eddy current analysis due to the need for learning at each time step. This paper addresses these challenges through the application of transfer learning techniques. Our findings demonstrate that the proposed method significantly reduces the total analysis time in time-dependent scenarios compared to traditional Finite Element Method (FEM) approaches.

关键词： Analytical models Computational modeling Transfer learning Neural networks Focusing Mathematical models Finite element analysis

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Neural Network Approaches for Magnetization Surface Prediction in Switched Reluctance Motors: Classical, Radial Basis Function, and Physics-Informed Models 3

Neural Network Approaches for Magnetization Surface Predicti...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Demidova, Galina Bogdanov, Andrey Iaremenko, Andrei Xirong, Zhu Chen, Hao Anuchin, Alecksey Moscow Power Engineering Institute Electric Drive Department Moscow Russia Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9798331516321

This paper investigates the prediction of magnetization surfaces in Switched Reluctance Motors using three distinct methods: classical Neural Networks, Radial Basis Function networks, and Physics-Informed Neural Networks. The study utilizes experimental data from a 7.5 kW SRM to assess the performance of these approaches, with all methods implemented in Python. Among the methods analyzed, PINN demonstrates the highest accuracy in predicting the magnetization surfaces, effectively capturing the underlying physical behavior of the motor. The computational costs of each method are evaluated, providing insights into training times, inference speeds, and floating-point operations per sample. The findings reveal that while NN offers a reasonable balance between performance and efficiency, the RBF method excels in training speed but shows limitations in capturing the physical dynamics of the system. In contrast, the PINN not only delivers superior predictive performance but also emphasizes the significance of incorporating physical principles into machine learning frameworks. The results highlight the applicability of these methods across various types of SRMs, suggesting their potential for enhancing control strategies in electrical and power engineering applications. © 2024 IEEE.

关键词： Radial basis function networks

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A Study of Motor Fault Condition Classification Using an Unsupervised Learning-Based Ensemble Model

A Study of Motor Fault Condition Classification Using an Uns...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Eui-Jin Choi Seung-Min Song Ji-Hoon Han Sun-Ki Hong Department of Information Control Engineering Hoseo University Asan Korea Department of System and Control Engineering Hoseo University Asan Korea

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

In motor fault diagnosis, auto-encoder based methods are effective in detecting abnormal patterns by utilizing only normal data. However, this approach has limitations in classifying various fault types, as it is primarily trained with normal data. To solve this problem, a voting technique using an ensemble model based on an auto-encoder is proposed in this study. The proposed method combines several auto-encoder models to synthesize the prediction results of each model, showing higher performance in fault type classification than traditional single model approaches. This method has the advantage of more clearly distinguishing the boundaries between failure types and maintaining stable performance even in various failure scenarios. The experimental results confirm that the proposed method can classify various types of faults with high accuracy, even though it was trained only on normal data.

关键词： Fault diagnosis Accuracy Gears Predictive models Motors Data models Unsupervised learning

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Computing the explicit MPC solution in the constrained zonotope case 63

Computing the explicit MPC solution in the constrained zonot...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Stoican, Florin Mihai, Stefan S. Monnigmann, Martin Ciubotaru, Bogdan D. University 'Politehnica' of Bucharest Department of Automatic Control and Systems Engineering 313 Splaiul Independenţei Sector 6 Bucharest060042 Romania Ruhr University Bochum Martin Mönnigmann Is with Automatic Control and System Theory Bochum44801 Germany

ISBN: (纸本)9798350316339

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem's constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution. © 2024 IEEE.

关键词： State space methods

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