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Position Regulation of Industrial Robots via Bounded Integral Terminal Sliding Mode control Algorithm

IEEE Transactions on Industrial Electronics 2024年第7期71卷 7403-7412页

作者： Cong, Yongzheng Du, Haibo Zhu, Wenwu Chen, Chih-Chiang Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

For the position regulation problem of six degrees of freedom (6-DOF) industrial robots, a novel bounded finite-time position regulate algorithm is developed and employed to improve the dynamic performance of the industrial robots. First, based on the model feature, a bounded finite-time position regulate law is proposed to overcome input saturation constraints for the considered systems. Rigorous analysis guarantees the finite-time stability of the closed-loop system in the absence of disturbance. Then, to improve the disturbance rejection property of the robot systems in the presence of disturbance, an improved bounded finite-time control law based on the integral terminal sliding mode is constructed. Finally, simulations and experimental results are provided to show the effectiveness of the proposed bounded control law. © 1982-2012 IEEE.

关键词： Asymptotic stability

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ReACT: Reinforcement Learning for controller Parametrization Using B-Spline Geometries

ReACT: Reinforcement Learning for Controller Parametrization...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Rudolf, Thomas Flögel, Daniel Schürmann, Tobias Süß, Simon Schwab, Stefan Hohmann, Sören FZI Research Center for Information Technology Embedded Systems and Sensors Engineering Department Karlsruhe76131 Germany Institute of Control Systems The Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350337020

Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables. © 2023 IEEE.

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An Improved Machine Learning Model for Pure Component Property Estimation

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Engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Microservice Migration in Hybrid Satellite-Terrestrial Networks for Autonomous Vehicles

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Journal of Communications and Information Networks 2025年第1期10卷 1-14页

作者： Xin Long Yuning Jiang Zixin Wang Xin Liu Yuanming Shi Yong Zhou School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Automatic Control Laboratory Ecole Polytechnique Federalede LausanneLaussane 1015Switzerland Department of ECE The Hong Kong University of Science and TechnologyHong Kong 999077China

Autonomous vehicles(AVs)have the potential to enhance road safety,reduce fuel consumption,and alleviate traffic ***,the computational demands of processing large volumes of sensory data for tasks like motion planning and trajectory forecasting impose critical challenges on limited onboard resources of *** edge computing(MEC)offers a solution by offloading these tasks to edge servers located in proximity of the *** AVs traverse remote areas lacking terrestrial infrastructures,low Earth orbit(LEO)satellites can fill this gap by providing edge computing *** this paper,we propose a microservice-based framework for MEC-enabled hybrid satellite-terrestrial networks to support *** decomposing monolithic applications into microservices deployed in containers,we enable scalable and flexible computing *** address the challenges of microservice migration due to the mobility of AVs and LEO satellites by formulating a long-term optimization problem aimed at minimizing task and migration *** online Lyapunov-based algorithm is developed to solve this problem,reducing the decision space by scheduling periodic migrations and decomposing it into mixed-integer linear *** results demonstrate that our proposed algorithm can achieve a nearly optimal results while maintaining a low execution time.

关键词： hybrid satellite-terrestrial network autonomous vehicles microservice migration

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Nonlinear Identification for control by Using HARMAX Models

Nonlinear Identification for Control by Using HARMAX Models

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Janetta Culita Dan Stefanoiu Andreea-Maria Nica Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest Romania Faculty of Automatic Control and Computers University “Politehnica” of Bucharest Romania

The purpose of this article is to perform a comparison between two identification models: a nonlinear one referred to as HARMAX and a classical linear one, of Box-Jenkins (BJ) type. The first model modifies a classical ARMAX model by applying Hammerstein polynomials on input, output, and noise signals. Identification of models was performed by means of Multi-Step Least Squares Method, as described into the article. The simulations on a real-world plant, namely ASTANK2, a fluidic system with two inputs, two outputs and nonlinear static characteristic, have proven that the HARMAX model performs better than the BJ model and provides more accurate useful models to be integrated into closed loop configurations.

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Foreign Object Debris detection at aerodromes using YOLOv5

Foreign Object Debris detection at aerodromes using YOLOv5

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Jakub Suder Tomasz Marciniak Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Jana Pawła II 24 Poznan University of Technology Poznań Poland

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

The latest EASA recommendations from 2024 indicate the possibility of using machine learning techniques in aerodrome monitoring. The aim of the work was to analyze solutions and prepare FOD (Foreign Object Debris) object detection software on aerodromes. In order to implement the issue, a dataset of photos and video recordings for testing algorithms was developed. The dataset consists of 1480 photos showing FOD at aerodromes and photos of the runway itself, according to EASA and FAA recommendations. FOD detection was implemented using a classical, k-means method and effective YOLOv5.

关键词： YOLO Reviews Neural networks Software algorithms Signal processing algorithms Software Real-time systems Video recording Monitoring Testing

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Variable Speed Limit control for Highway Scenarios a Multi-agent Reinforcement Learning Based Approach 2nd

Variable Speed Limit Control for Highway Scenarios a Multi...

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2nd International Conference on Cognitive Mobility, COGMOB 2023

作者： Kővári, Bálint Knáb, István Gellért Bécsi, Tamás Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (纸本)9783031876196

Modern road networks are critical in developing transportation infrastructures from the aspect of sustainability, thanks to the rapid increase in road users. The demand for mobility makes the existing infrastructure more crowded, boosting greenhouse gas emissions and delays in everyday commuting. Expanding the road network is only possible in some cases and is also not feasible, but Intelligent Transportation systems (ITS) can enhance the efficiency of the existing transportation network. From a management point of view, a proven algorithm is Variable Speed Limit control, which regulates the state of certain sectors by spatially distributing traffic in the form of dynamically varying speed limits. Combining this with a state-of-the-art predictive solution can make a big difference to performance. For the design of speed limits, this paper proposes an approach where deep reinforcement learning with the smallest industrial share not only resolves moving jams that arise during congested traffic situations, but also prevents them, thereby avoiding cumulative error from transients, all by abandoning physical equations and identified models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Highway traffic control

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Reaction Wheel Inverted Pendulum control System

Reaction Wheel Inverted Pendulum Control System

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International Conference on Soft Computing and Measurements (SCM)

作者： Ksenija S. Suravneva Grigorij V. Belskii Mihail M. Kopichev Automatic Control Systems department Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

This paper deals with the design of a laboratory test bench and control system for maintaining an unstable equilibrium position of an inverted pendulum with a reaction wheel. A review of technical solutions in the field of stabilization of an inverted pendulums of this type is given in order to find out solutions for designing a prototype of the plant and its mathematical model. Proposed mathematical model showed the controllability of the plant with regulator. The prototype of the laboratory test bench of an inverted pendulum including the arm fixed in the bearing support in the base, a DC motor with a reduction gear, a Hall sensor, a reaction wheel on the shaft and a driver to provide the motor current, microcontroller control system to control the rotation of the motor shaft and, accordingly, to control the torque on the shaft to change the angle of inclination of the arm of the test bench was assembled. Experimental study of the designed laboratory test bench with PID controller was carried out. The tests showed that the proposed solution is controllable and the unstable equilibrium position could be maintained by rotation of the reaction wheel on the shaft of the electric motor.

关键词： Shafts Torque Regulators Microcontrollers Gears Reviews Wheels

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation Engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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