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Design of control for a serial robot with actuator failures

UPB Scientific Bulletin, Series D: Mechanical engineering 2021年第2期83卷 19-34页

作者： Truc, Le Ngoc Quang, Nguyen Phung Faculty Hung Yen University of Technology and Education Viet Nam Hanoi University of Science and Technology Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Viet Nam

The paper addresses the actuator failure issue and aims to design a satisfactory control scheme for an n-DOF serial robot under actuator failures. The kind of actuator failures that is put into investigation in this study is the proportional degradation of actuator torque. A sliding mode controller using adaptive techniques is developed to deal with the faults. The switching gains of the proposed controller are alterable and can be suitably regulated to satisfy the Lyapunov criterion. The effectiveness of the proposal control law applied for a 6-DOF serial robot is shown through comparison with a conventional sliding mode controller. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Manipulators

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Linear parameter-varying model predictive control for nonlinear systems using general polytopic tubes

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Automatica 2024年 160卷

作者： Abbas, Hossam Seddik Institute for Electrical Engineering in Medicine University of Lübeck Moislinger Allee 53-55 Lübeck23558 Germany Automation and Control Group Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kaiserstraße 2 Kiel24143 Germany

This paper deals with model predictive control (MPC) for nonlinear systems using linear-parameter varying (LPV) embedding of the nonlinear dynamics (LPVMPC). The proposed LPVMPC can incorporate information of the future evolution of the scheduling parameter over the MPC prediction horizon with uncertainty bounds, which are used to construct anticipated scheduling tubes for robustification. Therefore, this approach is less conservative than the methods that only consider knowledge of the bounds on the scheduling parameter's rate of variation. The scheduling tubes are employed to synthesize online general polytopic invariant state tubes. The optimization problem of the proposed LPVMPC is a single quadratic program. Recursive feasibility is proven. A numerical example is presented for demonstrating the effectiveness of the proposed LPVMPC algorithm compared to nonlinear MPC and other standard approaches. © 2023 Elsevier Ltd

关键词： Model predictive control

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Model predictive control of microgrid energy management system with optimal power flow constraints using second order cone programming 18

Model predictive control of microgrid energy management syst...

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18th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Boonpeng, Jirapong Maneeratpongsuk, Jitpanu Banjerdpongchai, David Chulalonkom University Intelligent Control Automation of Process Systems Research Unit Dept. of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9780738111278

Energy management system (EMS) is important for the planning and operation of power system as the power generation must supply enough energy for load with the minimum operating cost. This paper presents nonlinear MPC to dispatch power generation within the limitation of the power system. The objectives are to improve EMS by considering AC optimal power flow and provide a dispatch strategy to reduce total operating cost (TOC). To realize the proposed nonlinear MPC, we consider the power plant model of Mae Hong Son in the northern part of Thailand. Load data and power generation profile are utilized as the inputs to MPC of EMS to minimize TOC for one day ahead. In this work, the optimal power flow is incorporated as the second order cone constraints. It is shown that EMS with optimal power flow can operate within the rated voltage for all buses. When comparing the results in normal mode with one-shot optimization, the proposed MPC can reduce the TOC by 1.72% in the rainy season. © 2021 IEEE.

关键词： Second-order cone programming

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A K-Means Clustering-Based Optimization of IT2FL for Improved Regenerative Braking Performance 1

A K-Means Clustering-Based Optimization of IT2FL for Improve...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Wahid, Muhammad Rizalul Joelianto, Endra Budiman, Bentang Arief Tongroon, Manida Pratama, Mulia Gianti, Mahmudah Salwa Hernadi, Muhammad Fauzan Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Faculty of Mechanical and Aerospace Engineering Bandung Indonesia Kasetsart University Atae Research Unit Department of Mechanical Engineering Sriracha Thailand Pusat Riset Mekatronika Cerdas Organisasi Riset Elektronika dan Informatika-BRIN Bandung Indonesia

ISBN: (纸本)9798350368918

Optimizing regenerative braking systems in electric vehicles presents a complex challenge requiring precise and adaptable control. This research proposes employing K-Means Clustering to the Interval Type-2 Fuzzy Logic (IT2FL) membership functions, thereby improving overall regenerative braking efficiency and enhancing driving range. This research demonstrates that implementing a K-Means in the IT2FL control system in electric vehicles effectively recovers 35.84% of energy. By incorporating a K-means-based approach, the IT2FL controller further enhances vehicle range by 43 kilometers, achieving a total range of 108 kilometers. In comparison, an IT2FL system without K-means recovered only 26.67% of energy and achieved a total range of 93 kilometers. Notably, the IT2FL controller with K-means enabled the electric vehicle to attain an energy efficiency of 22 kilometers per kilowatt-hour. These findings underscore the significant improvement in maximum range offered by the IT2FL controller with K-means compared to alternative methods. © 2024 IEEE.

关键词： Regenerative braking

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on control and automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Integrating GPS and Reference Point Data for Localization: A Study on Probabilistic Observation and Transmission in Locational Context

Integrating GPS and Reference Point Data for Localization: A...

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Asian control Conference

作者： Aulia Rahma Jumardi Nadana Ayzah Azis Yul Yunazwin Nazaruddin Vebi Nadhira Faculty of Industrial Technology Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Faculty of Industrial Technology Engineering Physics Doctoral Program Institut Teknologi Bandung Bandung Indonesia Department of Engineering Physics Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

To enhance the accuracy of vehicle localization, this research proposes a map matching system with a distinct architecture involving four key stages. Each GPS point creates a local map, and the system detects reference points within that map, subsequently calculating Euclidean distances and determining observation and transmission probabilities. The matching results yield the actual position of the vehicle on the map. The localization algorithm incorporates distance computation using a sliding window technique, observation probability based on proximity between GPS and reference points, and transmission probability to minimize errors. The study employs real-world tests in Bandung, Indonesia, demonstrating successful correction of GPS positions using the proposed method. In conclusion, this research provides a significant contribution in enhancing the accuracy of autonomous vehicle localization. By employing a probabilistic approach to observation and transmission, the method effectively corrects the vehicle's position, compensating for potential inaccuracies introduced by GPS. The utilization of a local map also proves impactful by including reference point positions, resulting in more precise outcomes. From the conducted field tests, all GPS points are accurately detected in their actual positions where there are seven GPS points do not match to vehicle trajectory, after the matching process, all point positions are successfully corrected, ensuring accurate localization by the system.

关键词： Location awareness Accuracy Computer architecture Probability Probabilistic logic Trajectory Autonomous vehicles

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery

Unscented Kalman Filter for State of Charge Estimation of Li...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Joshua Chun-Ken Dardchuntuk David Banjerdpongchai Department of Electrical Engineering Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.

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A Fast and Precise Multi-Level Coarse-to-Fine Registration Method for 3d Point Clouds

SSRN

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SSRN 2023年

作者： Ervan, Osman Temeltas, Hakan Department of Control and Automation Engineering Faculty of Electric and Electronics Istanbul Technical University Ayazaga Campus Sariyer Maslak Istanbul34469 Turkey

Three-dimensional (3D) point cloud registration is a fundamental process in the fields of computer vision and robotics. It enables the alignment and fusion of multiple point cloud datasets acquired from diverse viewpoints or distinct temporal instances. The rapid progress of 3D sensing technologies in 3D LiDAR sensors has led to the widespread availability of point cloud data across various applications, including robotics, autonomous navigation, object recognition, and cultural heritage preservation. Nevertheless, the inherent presence of noise, occlusions, and varying scales within acquired point clouds presents significant challenges in achieving precise registration. In this paper, we proposed MultI-LEvel coarSe-TO-fiNe rEgistration algorithm, referred to as MILESTONE. Geometric features of the points have been extracted using eigenvalue and eigenvector-based multi-level similarity metrics. During pairwise registrations, changing the input point cloud between source and target can impact the registration performance. To address this issue, we introduced a correspondence intersection search mechanism, which effectively overcomes this problem. The similarity percentage metric calculation procedure generates the probability of two correspondence pairs being an exact match. These robust tools enhance the quality of the coarse registration, not only in terms of accuracy but also in terms of time consumption. Finally, the results of the coarse registration are updated in the fine registration step, completing the coarse-to-fine registration pipeline. The contribution of this proposed method lies in the enhancement of coarse registration accuracy, coupled with a reduction in the time required for fine registration. Comprehensive experimentation confirms that the proposed algorithm yields favorable outcomes, encompassing a high rate of successful registration, precise rotation estimation, accurate translation estimation, efficient runtime, and notably, surpasses the cap

关键词： Nearest neighbor search

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