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IEEE 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Huangfu, Yigeng Yu, Tianying Zhang, Ruiheng Du, Yuhua Fan, Aili Northwestern Polytech Univ Sch Automat Xian Peoples R China Shanghai Electromech Engn Inst Shanghai Peoples R China

ISBN: (纸本)9798350360875;9798350360868

This paper proposes a de-centralized control method for distributed electric propulsion (DEP) systems in Unmanned Aerial Vehicles (UAVs). The traditional DEP system adopts a centralized control method and is equipped with similar redundancy. Such a control configuration suffers from single-point-of-failure and common mode faults. In this paper, a distributed control architecture is designed for the electric propulsion system, and a de-centralized control algorithm based on the pinning consensus is proposed to realize the synchronous control of the electric propeller speed and ensure the overall dynamic peribrmance of the whole system. In addition, to address the problem that the cyber network may be disturbed by noise or gain loss, a disturbance reconfiguration approach based on continuous signal compensation is designed to autonomously compensate for the steady-state error and enhance the stability under system disturbance. The performance of the proposed controller has been validated using fine-tuned motor models.

关键词： Distributed electric propulsion system Decentralized control Disturbance suppression Multi-motor synchronous control

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End-to-End Deep Reinforcement Learning-based Nano Quadcopter Low-level controller 24

End-to-End Deep Reinforcement Learning-based Nano Quadcopter...

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24th International conference on control, Automation and systems

作者： Doi, Truong-Dong Xuan-Mung, Nguyen Lee, Yong-Seok Hong, Sung-Kyung Sejong Univ Dept Aerosp Syst Engn Seoul 05006 South Korea Sejong Univ Dept Convergence Engn Intelligent Drone Seoul 05006 South Korea Sejong Univ Fac Mech & Aerosp Engn Seoul 05006 South Korea

ISBN: (纸本)9798331517939;9788993215380

Quadcopters have been studied for decades thanks to their maneuverability and capability of operating in a variety of circumstances. However, quadcopters suffer from dynamical nonlinearity, actuator saturation, as well as sensor noise that make it challenging and time consuming to obtain accurate dynamic models and achieve satisfactory control performance. Fortunately, deep reinforcement learning came and has shown significant potential in system modelling and control of autonomous multirotor aerial vehicles, with recent advancements in deployment, performance enhancement, and generalization. In this paper, an end-to-end deep reinforcement learning-based controller for quadcopters is proposed that is secure for real-world implementation, data-efficient, and free of human gain adjustments. First, a novel actor-critic-based architecture is designed to map the robot states directly to the motor outputs. Then, a quadcopter dynamics-based simulator was devised to facilitate the training of the controller policy. Finally, the trained policy is deployed on a real Crazyflie nano quadrotor platform, without any additional fine-tuning process. Experimental results show that the quadcopter exhibits satisfactory performance as it tracks a given complicated trajectory, which demonstrates the effectiveness and feasibility of the proposed method and signifies its capability in filling the simulation-to-reality gap.

关键词： deep reinforcement learning quadcopter controller end-to-end sim2real and actor-critic network

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SDN-Based Critical Infrastructure Resilience: A Smart Grid Perspective

SDN-Based Critical Infrastructure Resilience: A Smart Grid P...

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Chiu, Steve C. Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA

ISBN: (纸本)9798350361513;9798350372304

Software-defined network (SDN) systems in their recent development has enabled innovative methods and systems to facilitate control of large and complex infrastructures critical to both regional and national interests. The advent of cloud computing allows the convergence of operation and information aspects of the traditional industrial control systems (ICS), forming the core functionalities of many critical infrastructures ranging from energy, transportation, communication to supply chains. Resilience and the mitigation of natural, manmade and cyber-based attacks are at the center of these infrastructures given the vast amount of data (both friendly and malicious) encountered in the systems. To address these issues, a holistic approach to examine the major subsystems of the infrastructure is thus essential. This paper provides a cross-sectional study of the cloud-based Smart Grid, which utilizes policy-based network management methods in runtime monitoring and operation at both the control and data-plane levels. We present an experimental 5G simulation platform that connects a network of SDN embedded 5G devices with a PLC network via the classical Ethernet as Internet of Things (IoT). Basic functionality of this experimental platform is verified for performance and scalability for future extension in system complexity.

关键词： Cloud computing critical infrastructure (CIP) big data software-defined network cyberattack SDN ICS IoT 5G communication systems policy-based network management

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Adaptive prescribed performance neural control of flexible-joint manipulators with unmodeled dynamics and output constraints 36

Adaptive prescribed performance neural control of flexible-j...

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36th Chinese control and Decision conference (CCDC)

作者： Zhang, Jiu Sha, Yifan Xia, Meizhen Qiu, Rongyang Yangzhou Univ Coll Informat Engn Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper proposes an adaptive neural control strategy for flexible-joint manipulator systems with unmodeled dynamics, output constraints and error compensation mechanism. By using time-varying barrier Lyapunov function, the tracking errors are constrained within a time-varying boundary. Through pre-setting the parameters of the time-varying boundary function, the steady-state and transient performance of the system can be guaranteed. The command filtered backstepping with compensation mechanism is used to ensure the trajectory tracking accuracy. Radial basis function neural network is utilized to approximate the unknown nonlinear functions. The uncertainties caused by unmodeled dynamics are dealt with a first-order dynamic signal. Furthermore, all signals in the closed-loop system are proved to be semi-globally uniformly ultimately bounded, and tracking errors can be retained within a prescribed bound. A simulation example demonstrates the effectiveness of the proposed strategy.

关键词： prescribed performance flexible-joint manipulator adaptive control unmodeled dynamics output constraints command filter

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An evolutionary approach with reliability priority to design Scada systems for water reservoirs

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EVOLVING systems 2022年第3期13卷 499-517页

作者： Zand, Ali Dolatshahi Khalili-Damghani, Kaveh Raissi, Sadigh Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran

In this paper, a new evolutionary method designs and improves the reliability of Supervisory control and Data Acquisition (SCADA) of reservoir station systems in the water transfer network. The proposed mathematical model uses a reliability Block Diagram (RBD) and redundancy policies. Then a bi-objective non-linear mathematical RAP model considering cost and reliability optimizes the number of redundant components in each subsystem. A customized hybrid dynamic NSGA ii mixed with the MOPSO algorithm solves the proposed RAP. The customized algorithm uses a dynamic repository to save the elites for each generation. These elites will form the final solutions. Also, the parameters will dynamically change with the progress of the algorithm. This approach was compared to the mathematical method, meta-heuristic method and it had a better performance. Finally, the mathematical relations of control centers and stations calculate the total reliability of the SCADA system concerning the k-out-of-n-systems regarding minimum stations for acceptable system performance.

关键词： SCADA Reliability optimization Multi-objective optimization Water transfer network Genetic algorithm

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Advancing Occupational Safety and Health Training: A Safety-ii Integration of the ADDIE Model for Virtual Reality 14th

Advancing Occupational Safety and Health Training: A Safety-...

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14th International conference on Methodologies and Intelligent systems for Technology Enhanced Learning

作者： Bentivenga, Rosina Bernabei, Margherita Carli, Marco Colabianchi, Silvia Costantino, Francesco Ferrarotti, Anna Neri, Michael Pietrafesa, Emma Sorrentino, Edvige Stabile, Sara INAIL Dept Occupat & Environm Med Epidemiol & Hyg Piazzale Giulio Pastore 6 I-00144 Rome Italy Sapienza Univ Rome Dept Mech & Aerosp Engn Via Eudossiana 18 I-00184 Rome Italy Univ Roma Tre Dept Ind Elect & Mech Engn Via Vito Volterra 63 I-00146 Rome Italy Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Ruber Via Ariosto 25 I-00185 Rome Italy

ISBN: (纸本)9783031735370;9783031735387

This paper examines the enhancement of occupational safety and health (OSH) training in manufacturing through the integration of Safety-ii principles within a Virtual Reality (VR) training framework, applying the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. Amidst the digital transformation in manufacturing, innovative training methods such as VR have become instrumental in improving operational safety and efficiency. However, the incorporation of Safety-ii, a philosophy emphasizing the complexity of organizational systems and the need for resilience, has not been systematically applied to VR training designs. Through a series of focus group sessions, this study presents a new method for creating VR training materials. Designed to foster systemic thinking, resilience, proactivity, learning, flexibility, and leverage human performance variability, these courses are in line with Safety-ii's transition from merely preventing negative outcomes to understanding and increasing positive capacities. The results lead to a newintegration of the ADDIE model within a Safety-ii framework for VR-based OSH training, enhanced by Skill-Rules-Knowledge (SRK) specific guiding principles.

关键词： VR training ADDIE Safety-ii SRK occupational safety and health

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Design and Test of Hardware-in-the-loop Platform for Vehicle Chassis CAN System with Intermittent Open and Short Connection Faults 29

Design and Test of Hardware-in-the-loop Platform for Vehicle...

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29th International conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Wang, Longkai Du, Shilun Gong, Guofang Lei, Yong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China

ISBN: (纸本)9798350325621

controller Area network (CAN) plays an important role in vehicle chassis control system, hence the reliability of CAN network relates to driving performance and even passenger safety. However, intermittent connection (IC) faults, including intermittent open connection (IOC) and intermittent short connection (ISC), can potentially affect the performance of the vehicle CAN network. In this work, a hardware-in-the-loop (HIL) real-time simulation platform for the in-vehicle CAN system is constructed, and the effects of IC faults on the system are studied. First, based on the virtualized vehicle dynamics model and the actual bus hardware, the HIL platform of the vehicle CAN system is built. Then, typical connectivity problems such as IOC and ISC faults are emulated on the vehicle chassis CAN system. Finally, the influence law of different electrical character (type, frequency, and intensity) of IC faults on the vehicle speed control performance is analyzed. The results demonstrate the varying performance of CAN-based control systems in terms of signal delay, control performance, system stability, and tolerable fault intensity in the presence of IC faults with different electrical characteristics.

关键词： controller area network (CAN) hardware-in-the-loop (HIL) intermittent connection (IC) fault vehicle chassis dynamics

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An Optimal control Method of IPM Motor Based on Multilayer Neural network 2

An Optimal Control Method of IPM Motor Based on Multilayer N...

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IEEE 2nd International conference on Power Science and Technology (ICPST)

作者： Zhao, Yongxin Chen, Xun Xu, Song Jiang, Wei Hashimoto, Seiji Jiangsu Univ Sci & Tech Coll Automat Zhenjiang Jiangsu Peoples R China Yangzhou Polytech Inst Coll Intelligent Mfg Yangzhou Jiangsu Peoples R China Gunma Univ Sch Sci & Technol Gunma Japan

ISBN: (纸本)9798350349047;9798350349030

The built-in permanent magnet (IPM) motor is a type of motor that positions permanent magnets within the rotor. It combines reluctance torque with magnet torque, making it extensively utilized in household appliances, industrial equipment, automobiles, and other domains. Neural networks are computational models with non-linear characteristics and potent learning capabilities, particularly adept at solving identification problems in complex, non-linear, uncertain, and ambiguous systems. During the network training process, the network progressively optimizes its performance through backward propagation and weight adjustment. This paper proposes a learning method that leverages a multi-layer neural network structure to optimize the efficiency of IPM motors, capitalizing on the characteristics of neural networks. The control method achieves optimal efficiency control by deriving and compensating the d-axis and q-axis current phase angles in real time so that the IPM motor can maintain efficient performance under various conditions. This method can effectively control the motor and improve control efficiency without relying on identification or estimation methods, even when the parameters of the controlled object change. The method's effectiveness is validated through comparative simulations with two traditional control techniques.

关键词： IPM motor vector control high-efficiency control neural network

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Railway Level Crossing control System Based on Video Analysis and Train-Road Collaboration 26

Railway Level Crossing Control System Based on Video Analysi...

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IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Wu, Suining Li, Yang Chen, Xiaoguang Wang, Peichuan Lin, Jinfeng China Railway Signal & Commun Res & Design Inst G Percept Lab Basic Equipment Res Inst Beijing Peoples R China

ISBN: (纸本)9798350399462

Safety and security are the most concerned aspects in railway level crossing. Existing method are mainly focus on danger evaluation or obstacle detection of level crossing traffic. This research work present a railway level crossing control system based on a combination of video analysis and position methods to deal with the problems: autonomous control of level crossing gate and over-the-horizon visual perception for train driver. We proposed a video analysis method based on object detection and prediction network to make up the open-gate decision the trackside subsystem. To increase the detection accuracy, an improved YOLOv5 network based on difference feature fusion module (DFFM) is proposed. Furthermore, a prediction network (TPD-net) using 1D convolution is presented to predict whether train has left, therefore the gate of level crossing can be opened without risk. Experiments show that proposed DFFM is able to improve detection performance, and TPD-net works accurately and stably in train passage detection. Our system can be efficient and safe for autonomous control in railway level crossing system.

关键词： control systems

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Formation control of Swarms of Unmanned Aerial Vehicles 24

Formation Control of Swarms of Unmanned Aerial Vehicles

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24th International conference on control, Automation and systems

作者： Tahir, Anam Univ Turku Fac Technol Automat Engn Turku 20014 Finland

ISBN: (纸本)9798331517939;9788993215380

The objective of this doctoral thesis [1] is to design a distributed formation control system for swarms of unmanned aerial vehicles which addresses the challenges of scalability, collision avoidance, failure recovery, energy efficiency, and control performance. The swarms are arranged in tightly/loosely coupled architectures, which are based on homogeneous nodes in a distributed network of leader-follower/leaderless structures. The model of each node in the swarm formation is based on the nonlinear/linear dynamic model of a quadcopter, i.e. an unmanned aerial vehicle. The goal is to design the formation control of swarms of unmanned aerial vehicles, which is divided into high- and low-level control. From the high-level control perspective, the main contribution is to propose continuous path planning which can quickly react to events. Setpoints are generated for the swarms of unmanned aerial vehicles considering the complex movement of a hierarchical formation, soft landing, and failure recovery. The hierarchical formation and soft landing are executed using a fixed formation. Reconfiguration of the formation after node failures is implemented using a shortest path algorithm, combinatorial algorithms, and a thin plate spline. Besides this, from the low-level control perspective, the main contribution is to manoeuvre the nodes smoothly. The tracking of setpoints and stabilisation of each node is handled by a nonlinear sliding mode control with proportional derivative control and a linear quadratic regulator with integral action. The proposed strategies are evaluated using simulations, and the obtained results are compared and analysed both qualitatively and quantitatively using different scenario-relevant metrics. In addition, this doctoral thesis (Anam Tahir. Formation control of Swarms of Unmanned Aerial Vehicles. Doctoral Dissertation, University of Turku, Turku, Finland, September 2023, ISBN: 978-951-29-9411-3. Available: https://***/URN:ISBN:978-951-29-9411-3

关键词： Unmanned aerial vehicles multi-drone systems swarm intelligence formation maintenance distributed control hierarchical systems soft landing failure recovery system

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