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

作者： Claudiu-Ionel Nicola Marcel Nicola Cosmin Ionete Monica Roman Department of Automatic Control and Electronics National Institute for Research Development and Testing in Electrical Engineering – ICMET Craiova University of Craiova Craiova Romania Department of Automatic Control and Electronics University of Craiova Craiova Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Starting from the fact that the Field Oriented control (FOC) control strategy of the Permanent Magnet Synchronous Motor (PMSM) gives good results in terms of the usual indicators of response time quality, overshoot, steady-state error, but only in the usual operating range of the PMSM. The Maximum Torque Per Ampere (MTPA) control type strategy is utilised to limit current during operation while maintaining the required load torque, while the Field Weakening (FW) control type strategy is used to achieve speeds above the rated speed of the PMSM. These strategies are also complemented by the use of a Model Reference Adaptive System (MRAS) observer for PMSM rotor speed estimation and a Butterfly Optimization Algorithm (BOA) for achieving optimal values of the PI speed controller according parameters. The PMSM operating equations and the FOC, MTPA, and FW control strategies are also presented, together with the implementation structures in the Matlab/Simulink software programming environment. The features of these PMSM control systems in the corresponding operating regimes are presented through numerical simulations accomplished through Matlab/Simulink.

关键词： Torque Software algorithms Observers Numerical simulation Mathematical models Steady-state Time factors MATLAB Optimization Tuning

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Adaptive Sliding Mode control for a Boost Converter with Constant Power Load

Adaptive Sliding Mode Control for a Boost Converter with Con...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： D. Zambrano-Prada A. El Aroudi L. Vázquez-Seiszdedos O. Lopez-Santos R. Haroun L. Martínez-Salamero Department of Electrical Electronic and Automatic Control Engineering School of Electrical and Computer EngineeringCampus Sescelades Group of Automatic Control and Industrial Electronics (GAEI) Rovira i Virgili University Tarragona Spain

Output voltage regulation in a boost converter with Constant Power Load (CPL) can be obtained by means of sliding-mode control with a linear estimation loop of the output power. The estimation procedure is a simple integrator of the output voltage error and confers an adaptive nature on the switching regulator. Two switching surfaces of quadratic type are considered to induce the sliding motions, and the corresponding conditions for the existence of both sliding mode and stability of the equilibrium point are derived. The resulting controller can be implemented using simple analog electronics requiring operational amplifier-based circuits in both cases plus a multiplier in one case and a multiplier and a divider in the other case. PSIM and MATLAB simulations show a fast recovery and zero steady-state output voltage error in response to large-signal disturbances in the output voltage and the load power.

关键词： Renewable energy sources Estimation Switches Stability analysis Steady-state Circuit stability Behavioral sciences

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Neurophysiological Test with Use of EEG, GSR and Pulse Measurements Data for Focus Group Beverage Preference Aggregation 11

Neurophysiological Test with Use of EEG, GSR and Pulse Measu...

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11th International Scientific Conference on Computer Science, COMSCI 2023

作者： Andonov, Stanimir Georgiev, Stiliyan Tsenov, Georgi Mladenov, Valeri Management Faculty Technical University Economics Industrial Engineering and Managemen Department Sofia Bulgaria Validation - Vision Analysis Team Visteon Electronics Bulgaria Sofia Bulgaria Automatic Control Faculty Technical University Fundamentals of Theoretical Electrical Engineering Department Sofia Bulgaria

ISBN: (纸本)9798350325256

The primary objective in this case study is introduction of a testing methodology framework for assessing the effectiveness of different drinks and flavors through neuromarketing. The framework involves use of focus groups for evaluation of the neurophysiological reactions of participants to various stimuli and drawing statistically significant conclusions based on the results. In the last past decade, there has been an increase in the availability of affordable and easy-to-use biosensors that provides the possibility for affordable and easy to use real-time measurements of bio-signals, such as electroencephalography (EEG), galvanic skin response (GSR), and pulse. Also, with the recent ongoing increase of the computational capabilities the signal processing and analysis of the recorded biometric data can also be processed in real-time with relatively affordable and mobile hardware. By analyzing these signals and identifying an informative features that are derived statistically from group averaging and independent of individual user's deviations, it is possible to classify and then apply the measured brainwaves and other biometrics data in various applications. In this paper, the authors apply data from voltage potential electrodes EEG brainwaves, along with GSR and pulse in order to recognize the variation response levels among a sample focus group and the results for determination of their aggregated preferences on tested sample drinks. © 2023 IEEE.

关键词： Electroencephalography

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Enhanced Single Pixel Imaging in Atmospheric Turbulence

Enhanced Single Pixel Imaging in Atmospheric Turbulence

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Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Cheng, Yin Liao, Yusen Zhou, Shuaijun Chen, Jingan Lam, Edmund Y. Ke, Jun School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Beijing Aerospace Automatic Control Institute China Aerospace Science and Technology Corporation Beijing100070 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong

The imaging quality of single pixel imaging in atmospheric turbulence is enhanced by combining a deep learning model with classical methods. The effectiveness of this method was confirmed by optical experiments. ©... 详细信息

关键词： Pixels

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ML-based Expert Products Scoring System

ML-based Expert Products Scoring System

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Progress in Applied electrical engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

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Online Residual Learning from Offline Experts for Pedestrian Tracking

Online Residual Learning from Offline Experts for Pedestrian...

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IEEE Conference on Decision and control

作者： Anastasios Vlachos Anastasios Tsiamis Aren Karapetyan Efe C. Balta John Lygeros Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich Switzerland Control and Automation Group inspire AG 8005 Zürich Switzerland and with the Automatic Control Laboratory

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance.

关键词： Pedestrians Aggregates Prediction algorithms Trajectory Drones

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A Model Predictive control Framework for Improving Risk-Tolerance of Manufacturing Systems

A Model Predictive Control Framework for Improving Risk-Tole...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Anbarani, Mostafa Tavakkoli Balta, Efe C. Meira-Goes, Romulo Kovalenko, Ilya Pennsylvania State University Department of Mechanical Engineering PA United States Automatic Control Laboratory Eth Zürich Zürich Switzerland Pennsylvania State University Department of Electrical Engineering PA United States Pennsylvania State University Department of Industrial and Manufacturing Engineering PA United States

ISBN: (纸本)9798350335446

The need for control strategies that can address dynamic system uncertainty is becoming increasingly important. In this work, we propose a Model Predictive control for quantifying the risk of failure in our system model. The proposed control scheme uses a Priced Timed Automata representation of the manufacturing system to promote the fail-safe operation of systems under uncertainties. The proposed method ensures that in case of unforeseen failure(s), the optimization-based control strategy can still achieve the manufacturing system objective. In addition, the proposed strategy establishes a trade-off between minimizing the cost and reducing failure risk to allow the manufacturing system to function effectively in the presence of uncertainties. An example from manufacturing systems is presented to show the application of the proposed control strategy. © 2023 IEEE.

关键词： Model predictive control

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Research to Improve the Quality of Servo Motor controller 5

Research to Improve the Quality of Servo Motor Controller

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5th International Conference on control in Technical Systems, CTS 2023

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

ISBN: (纸本)9798350304664

To overcome the disadvantages that traditional controllers bring, in this study, it is proposed to use fractional order controller PIλ 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. © 2023 IEEE.

关键词： controllers

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PMSM Sensorless control System Based on DTC Using FLC and Luenberger-PLL Observer

PMSM Sensorless Control System Based on DTC Using FLC and Lu...

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Global Power, Energy and Communication Conference (GPECOM)

作者： Claudiu-Ionel Nicola Marcel Nicola Andreea Iacob Cristian Pîrvu Research Department National Institute for Research Development and Testing in Electrical Engineering – ICMET Craiova Department of Automatic Control and Electronics University of Craiova Craiova Romania Department of Automatic Control and Electronics University of Craiova Craiova Romania

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

Starting from the desire to achieve a good control performance of the Permanent Magnet Synchronous Motor (PMSM), but with a relatively medium complexity of the control system, this paper presents a sensorless control structure of a PMSM using the Direct Torque control (DTC) control strategy. In order to improve the performance of the Luenberger observer used for speed estimation, a Phase Locked Loop (PLL) observer is additionally used which, due to the integral nature of the control system, ensures a better estimation of the PMSM rotor speed. In addition, the classic PI controller used for speed control is continuously adjusted by a Fuzzy Logic controller (FLC). In contrast to the usual case where the input to the fuzzy controller is given by the speed error and its derivative, this paper proposes a second fuzzy controller which additionally uses an input provided by a load torque observer. The superior performance of the proposed PMSM sensorless control system is demonstrated by numerical simulations in both stationary and dynamic modes.

关键词： Fuzzy logic Torque PI control Velocity control Estimation Rotors Sensorless control

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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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