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Immersion and Invariance Adaptive SOSM control for Uncertain Nonlinear systems

Immersion and Invariance Adaptive SOSM Control for Uncertain...

引用

Chinese control and Decision Conference, CCDC

作者： Yan Gao Yafei Chang Jinlin Sun Hui Wang Chih-Chiang Chen Shihong Ding School of Electrical and Information Engineering Jiangsu University Zhenjiang China Beijing Institute of Control Engineering Beijing China Department of Systems and Naval Mechatronic Engineering National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper addresses the issue of designing a novel immersion and invariance adaptive second-order sliding mode (IIASM) control for uncertain nonlinear systems. First of all, an immersion and invariance (I&I) adaptive method based on the non-certainty equivalence (Non-CE) is analyzed to deal with the system uncertainty. On this basis, the second-order sliding mode (SOSM) control method is introduced to make the system state converge to zero in a finite time. Then, a new IIASM control strategy is designed to achieve excellent tracking performance in the case of system uncertainties. Further, the finite-time stability of the system is proved by strict Lyapunov analysis. Finally, a typical unmanned agricultural vehicle path-tracking simulation example is used to verify the superiority of the proposed control strategy.

关键词： Adaptive systems Uncertainty Parameter estimation Stability analysis Nonlinear systems Sliding mode control

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Design of Supervisory Model Predictive control for HVAC systems and Two Zones with Consideration of Energy Efficiency and Thermal Comfort

Design of Supervisory Model Predictive Control for HVAC Syst...

引用

International Conference on control, Automation and systems ( ICCAS)

作者： Panithan Srisurapanon David Banjerdpongchai Department of Electrical Engineering Center of Excellence in Intelligent Control Automation for Process Systems Faculty of Engineering Chulalongkorn University Pathumwan Bangkok Thailand

This paper presents the design of supervisory model predictive control for HVAC systems and two zones. The supervisory control (SC) is designed to find the optimal reference temperature of each zone. The design criteria consist of the total operating cost (TOC) and the thermal comfort cost (TCC). Then, the model predictive control (MPC) is designed to track the optimal reference signals. When SC and MPC are designed for two zones simultaneously, they are referred to centralized SC and centralized MPC. On the other hand, when SC and MPC are designed for two zones separately, they referred to decentralized SC and decentralized MPC. We then compare the results between the decentralized control and the centralized control. The trade-off curve shows that the performance of centralized control is better than that of decentralized control. In particular, centralized MPC improves the performance in tracking reference signals. Furthermore, TOC of centralized control is lower than that of decentralized control. It can be concluded that the centralized control is appropriate for two zones in terms of the tracking of reference and the electrical charge.

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Research to Improve the Quality of Servo Motor controller

Research to Improve the Quality of Servo Motor Controller

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IEEE International Conference on control in Technical systems (CTS)

作者： Hoang Thi Thu Giang Phan Thanh Chung Tran Thuy Van Nguyen Duy Khanh Faculty of Electrical Engineering HaNoi University of Industry Hanoi Vietnam Department for Standards Metrology and Quality Military Institute of Mechanical Engineering Hanoi Vietnam Department of Automatic Control Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

To overcome the disadvantages that traditional controllers bring, in this study, it is proposed to use fractional order controller $\mathrm{PI}^{\lambda}$ instead of conventional PI controller, the research also focuses on improving the quality of the controller. the order controller itself by selecting the computational methods, the discretization methods, and the implementation methods that give the best results. The study conducted simulations and experiments and compared the results with conventional PI controllers, in order to demonstrate the effectiveness of the fractional order controller.

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Upgrading and testing of the UPV/EHU Stellarator 13

Upgrading and testing of the UPV/EHU Stellarator

引用

13th International Conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Garrido, Aitor J. Marco, Aitor De La Sen, Manuel Oleagordia, Inigo Garrido, Izaskun University of the Basque Country - UPV/EHU Automatic Control Group - Acg Institute of Research and Development of Processes - Iidp Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao Po Rafael Moreno 3 Bilbao48013 Spain University of the Basque Country - UPV/EHU. Automatic Control Group - Ac Advanced Design and Analysis Department Idom Zarandoa Etorb. 23 Bilbao48015 Spain University of the Basque Country - UPV/EHU Automatic Control Group - Acg Institute of Research and Development of Processes - Iidp Department of Electricity and Electronics Faculty of Science and Technology B° Sarriena s/n Leioa48080 Spain

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

This paper deals with the upgrade and testing of the of the Ultra-Low Iota Super Elongated Stellarator of the University of the Basque Country. The main upgrades affect to the coil system but also to the microwave source and the data acquisition system. Therefore, it is necessary to update its State-Space model. The updated model is validated by means of experimental output data showing an accurate matching with the real system. Then, in order to test the new model, a Model Predictive control-MPC scheme is successfully implemented both in simulation and experimentally using a real-time control platform. © 2022 IEEE.

关键词： Model predictive control

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Multi-objective Optimization of Traffic Signal systems on Urban Arterial Roads 2nd

Multi-objective Optimization of Traffic Signal Systems on Ur...

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2nd International Symposium on Simulation and Process Modelling, ISSPM 2020

作者： Liu, Tao Qiao, Feng Guo, Lingzhong Chen, Yifan Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS13JD United Kingdom

ISBN: (纸本)9789813345744

In this paper, multi-objective optimization is used to solve the signal synchronization problem in arterial traffic roads, where a traffic dispersion module is introduced to further expand the solution space. By incorporating the models of delay time, queue length and stop times into the optimization, a first model called M1 is established. In the second model M2, the free flow speed assumption is replaced by a traffic dispersion module for better estimating the link travel time. A simulation study is then carried out on an arterial road, and the results show that the proposed strategy improves the performance of the traffic system compared to the current timing scheme and M2 has the best performance among all solutions in this paper, and the delay is reduced for about 24%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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Unsupervised Learning-Based Methodology for Detection of Postural Anomalies in Wheelchair Users

Unsupervised Learning-Based Methodology for Detection of Pos...

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IEEE International Conference on systems, Man and Cybernetics

作者： Patrick Vermander Aitziber Mancisidor Giancarlo Fortino Itziar Cabanes Raffaele Gravina Department of Automatic Control and System Engineering University of the Basque Country (UPV/EHU) Bilbao Spain Department of Informatics Modeling Electronics and Systems engineering University of Calabria (UNICAL) Rende Italy

Postural monitoring in wheelchair users is a topic of growing interest. The detection of changes in the sitting patterns of these patients may serve to detect changes in their functional status and be able to adapt rehabilitation early. For this reason, this paper presents a methodology for the detection of specific postural anomalies that, unlike previous works, adopts unsupervised learning. The proposed methodology involves data dimensionality reduction using Principal Component Analysis, and the application of K-means clustering to group different normal posture states. The anomalies are detected using a threshold approach, where data points that fall outside a certain threshold are considered as anomalies. The results show that the methodology is effective in identifying anomalies with a high degree of accuracy (around 90%).

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Distributed Sensor FD for Sensor Networks

Distributed Sensor FD for Sensor Networks

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jiarui Zhang Steven X. Ding Linlin Li Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

The main objective of this paper is to develop a distributed sensor fault detection (FD) approach for sensor networks in the presence of noise and transmission time of information exchange. To be specific, the approach optimizes the performance of FD by minimizing the detection uncertainty and the online implementation of the proposed method is in a distributed and recursive manner. Finally, a numerical example is illustrated to show that the distributed approach can successfully and efficiently accomplish the FD task.

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, control, and Computing (ICSTCC)

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 Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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The Classification Method of electrical Faults in Permanent Magnet Synchronous Motor Based on Deep Learning

The Classification Method of Electrical Faults in Permanent ...

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IEEE Conference on systems, Process & control (ICSPC)

作者： Hiba Z. Faraj Ayad Q. AL-Dujaili Amjad J. Humaidi Electrical Engineering Technical College Middle Technical University Baghdad Iraq Control and Systems Engineering Department University of Technology Baghdad Iraq

An accurate classification method is highly required in the development of a fault detection system. Various deep-learning techniques have recently been used for fault classification. However, optimally training deeper networks such as convolutional neural networks (CNN) on relatively few and non-uniform experimental data of electric machines is extremely difficult. The proposed classification method is based on wavelet and pre-trained convolution neural networks. This approach ensures the correctness of the diagnostic outcome while streamlining the testing procedure and does not require the collection of a lot of data. The continuous wavelet transform function converted the three-phase stator current signals to RGB images. It does not necessitate sophisticated signal processing methods or additional hardware detection equipment. This research will be useful in the development of an online fault detection system for engineering applications.

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