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Output Tracking control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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DESIGN OF PID controlLER FOR NONLINEAR MAGNETIC LEVITATION SYSTEM USING FUZZY-TUNING APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2022年第2期84卷 45-62页

作者： Ismail, Laith S. Lupu, Ciprian Alshareefi, Hamid Luu, Duc Lich Dept. of Automatic Control University POLITEHNICA of Bucharest Romania Computer Technology Engineering Department Al Turath University Collage Baghdad Iraq Faculty of Mechanical Dynamics Vinh University of Technology Education Nghe an Viet Nam

This paper presents a dynamic model of a magnetic levitation system (Maglev) and suggests a Fuzzy Tuning of a proportional—integral--derivative controller (PID) that uses a fuzzy approach system to determine the PID controller's settings. The controller allows a steel ball to be suspended in free fall in the chosen position in reference to the electromagnet bottom end. The electromagnet levitation system can be controlled with a traditional PID controller, however because of the highly nonlinear of this system, it is unpredictable in the case of load and air gap changes. The fuzzy rules are developed to fuzzy tune PID while considering the control response and behavior of the system to find a solution to this problem. This paper shows excellent stability, rising time, settling time overshoot, and robust response results. The results reveal that this fuzzy PID controller solution can stabilize the ball's location and has strong disturbance rejection. The real time platform is designed for educational laboratory and test all the proposed control systems. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： controllers

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Design and Realization of Unmanned Vehicle Autonomous Navigation System Based on Binocular Camera 3

Design and Realization of Unmanned Vehicle Autonomous Naviga...

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3rd International Conference on computer Science, Electronic Information engineering and Intelligent control Technology, CEI 2023

作者： Guo, Tianxing Lin, Lianlei Liu, Huanyu Li, Junbao School of Electronics and Information Engineering Harbin Institute of Technology Department of Automatic Test and Control Harbin China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350372052

Aiming at the navigation problem of unmanned vehicles in extreme environments such as communication interference and limited GPS signals, this study proposes an autonomous navigation method based on binocular cameras. The method enables the unmanned vehicle to complete the task of localization and map building using only binocular images, without the need for other sensors or signal sources. Meanwhile, this study also proposes a local path planning obstacle avoidance method based on depth map, which, when combined with global localization information, can take into account both local obstacle avoidance and global path navigation, and can cope with random environmental changes. The system uses a single sensor for autonomous obstacle avoidance and navigation, which reduces the computational requirements and ensures the low cost of the navigation system. In order to verify the reliability and effectiveness of the system under signal-constrained conditions, the system is evaluated in a simulation environment and a real field scenario, respectively. The experimental results show that the system is able to achieve reliable localization and path planning under signal-constrained conditions. © 2023 IEEE.

关键词： Cameras

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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Lyapunov-Inspired Deep Reinforcement Learning for Robot Navigation in Obstacle Environments

SSRN

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SSRN 2024年

作者： Kayacan, Erdal Ugurlu, Halil Ibrahim Redder, Adrian Department of Electrical and Computer Engineering Aarhus University Aarhus C Denmark Department of Automatic Control Paderborn University Paderborn Germany

After its success in games and simulated control tasks, deep reinforcement learning (DRL) has gained prominence in robotics for developing neural network-based planning and control algorithms. However, the inherent black-box nature of neural network controllers poses challenges in ensuring safety, prompting the need for innovative, explainable solutions. This research paper introduces a DRL algorithm design inspired by Lyapunov stability theory to solve safe robot navigation problems in the presence of obstacles. The robot planning problem is formulated as a state-space control problem with obstacle locations integrated into the state representation. To ensure a high probability of obstacle avoidance and, hence, safety, we introduce a reward-shaping strategy utilizing a Lyapunov function tailored for obstacle avoidance. Additionally, an early episode termination is employed to constrain exploration and add more valuable samples to the DRL training replay memory. Simulation results demonstrate the effectiveness of the method in achieving superior exploration and learning with higher mission completion rates while maintaining speeds closer to a desired target speed. Real-world experiments conducted with a quadrotor in a laboratory environment validate the applicability of our method. The findings underscore the potential of incorporating Lyapunov stability theory into DRL approaches for advancing safe and efficient robot navigation. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems 10

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Santosh, Kathari Kholmukhamedov, Timur Kumar, M. Sandeep Aarif, Mohd Muda, Iskandar Bala, B Kiran Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Turin Polytechnic University In Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350353068

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4's robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot's enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support. © 2024 IEEE.

关键词： Electronic commerce

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Learn to Bid as a Price-Maker Wind Power Producer

arXiv

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arXiv 2025年

作者： Singhal, Shobhit Fochesato, Marta Aolaritei, Liviu Dörfler, Florian Automatic Control Laboratory ETH Zürich Switzerland Department of Electrical Engineering and Computer Sciences UC Berkeley United States

Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model bidding as a bilevel optimization problem, but these methods require complex market models, estimating other participants’ actions, and are computationally demanding. To address these challenges, we propose an online learning algorithm that leverages contextual information to optimize WPP bids in the price-maker setting. We formulate the strategic bidding problem as a contextual multi-armed bandit, ensuring provable regret minimization. The algorithm’s performance is evaluated against various benchmark strategies using a numerical simulation of the German day-ahead and real-time markets. Copyright © 2025, The Authors. All rights reserved.

关键词： Wind power

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Towards experimental insight into magnetic flux distribution within the air gap of permanent magnet synchronous drive

Towards experimental insight into magnetic flux distribution...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Hubert Milanowski Adam Krzysztof Piłat Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on analyzing the distribution of magnetic flux within the motor’s spatial air gap, as well as the amplification of harmonics resulting from changes in air gap orientation. Drawing upon experimental findings, a model is proposed to illustrate the three-dimensional distribution of magnetic flux within the gap.

关键词： Solid modeling Magnetic flux density Automation Atmospheric modeling Air gaps Rotors Drives

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

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

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

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

ISBN: (纸本)9798350339918

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. © 2023 IEEE.

关键词： Wastewater treatment

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