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4th International Conference on Industrial automation, Robotics and control engineering, IARCE 2024

作者： Gu, Tao Cong, Li Yang, Jing Guo, Yuzhu Wang, Lina School of Automation Science and Electrical Engineering Beihang University Beijing China School of Electronic Information Beihang University Engineering dept. Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China

ISBN: (纸本)9798350380323

Inertial navigation has gained extensive application because it solely relies on built-in Inertial Measurement Unit (IMU) devices. Nevertheless, due to its inherent accumulative integral error, inertial navigation is often integrated with other external devices in practical situations, which unfortunately leads to unavoidable privacy breaches. To address these issues, an Inertial Navigation Neural Network (INN) is designed to generate high-precision position estimations without relying on any other auxiliary information. The INN model consists of attitude correction and velocity regression modules. The attitude module employs a convolutional network to correct the attitude information, and the obtained rotation matrices are then fed into a recurrent neural network to regress incremental velocities and produce high-precision positions. The proposed model is validated on the simulated trajectories based on the Precise Strapdown Inertial Navigation System (PSINS) toolbox. Experimental studies demonstrated that the proposed method has a significant corrective effect on high-dynamic strapdown inertial navigation settings. The accumulated position errors are an order of magnitude smaller than those of traditional track reckoning algorithms and achieved a comparable performance with integrated navigation. © 2024 IEEE.

关键词： Inertial navigation systems

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Towards Reliable Uncertainty Quantification and High Precision with General Type-2 Fuzzy systems

Towards Reliable Uncertainty Quantification and High Precisi...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Batuhan Avcı Aykut Beke Tufan Kumbasar Control and Automation Engineering Dept. Istanbul Technical University Istanbul Turkey Control Systems Design Dept. ASELSAN Inc. Ankara Turkey

Deep learning models have been successfully developed to solve complex problems with the main focus on high precision. Yet, accurately assessing uncertainty and prediction is essential for making informed decisions, especially in high-risk tasks. In this paper, we present a step towards learning reliable uncertainty quantification and high precision performance via α- plane based General Type-2 Fuzzy Logic systems (GT2-FLSs). To balance between accuracy and uncertainty quantification, we propose a novel composite loss function consisting of an accuracy-focused and uncertainty-focused loss term that exploits the parameters of the Secondary Membership Functions (SMFs). For the uncertainty-focused term, we use only the type-reduced set of α 0 = 0 plane of the GT2-FLS, i.e. the size of the SMFs, which does not contribute to the output calculation directly. In the accuracy-focused part, we present two options for the error terms. One uses the aggregated output while the other uses only the output α K = 1 plane of the GT2-FLS. In both terms, we make the SMF shape parameters responsible for learning pointwise prediction. We present statistical comparisons and demonstrate that the learned GT2-FLSs generate reliable prediction intervals while also resulting in high-precision performance. The results show the potential of the proposed approach for GT2-FLS as a promising solution for making reliable predictions in real-world applications.

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Energy Management of Autonomous Electric Vehicles by Reinforcement Learning Techniques 2

Energy Management of Autonomous Electric Vehicles by Reinfor...

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2nd International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2022

作者： Alonso, Monica Amaris, Hortensia Martin, David De La Escalera, Arturo University Carlos Iii Dept. of Electrical Engineering Madrid28911 Spain University Carlos Iii Dept. of Systems Engineering and Automation Madrid28911 Spain

ISBN: (数字)9781665471466

ISBN: (纸本)9781665471466

The increase in e-mobility poses new challenges to power grid operators who must cope with the variability and uncertainty of renewable energy sources and customer demand and the electric vehicle integration into the grid. In this paper a Reinforcement Learning algorithm based on Principal Policy optimization is proposed for energy management of electric vehicles and PV storage units. The RL algorithm considers the vehicle battery constraints, range anxiety and battery aging constraints. Moreover, the algorithm controls the charging of the photovoltaic storage unit to minimize the PV energy curtailment. Results show the improvement of the proposed algorithm compared to Business-as-usual and value-iteration solutions. © 2022 IEEE.

关键词： Energy management

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Mitigating Non-linear DAC Glitches Using Dither in Closed-loop Nano-positioning Applications

Mitigating Non-linear DAC Glitches Using Dither in Closed-lo...

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American control Conference (ACC)

作者： Ahmad Faza John Leth Arnfinn A. Eielsen Dept. of Energy and Petroleum Engineering University of Stavanger Norway Dept. of Electronic Systems Automation & Control Aalborg University Denmark

Digital-to-analog conversion is essential in digital signal processing applications, including closed-loop control schemes. Noise and distortion in digital-to-analog converters result in reduced performance for high-precision mechatronics such as nano-positioning. Glitches are common in practical switched systems such as digital-to-analog converters; observed as an output disturbance. Due to the wide-bandwidth, impulse-like behavior, control law bandwidth is generally too low to provide adequate attenuation; deteriorating open and closed-loop performance. This article demonstrates how large-amplitude high-frequency periodic dither mitigates the effect of glitches in a nano-positioning system under closed-loop control. Simulations are performed using a model that includes significant non-linearities with a response fitted to an off-the-shelf commercial device, as well as using standard linear time-invariant models for other system components fitted to the responses of common, commercially available devices. The results highlight the significance of reconstruction filter design when applying dithering in this setting.

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Criterion for Sufficiently Large Dither Amplitude to Mitigate Non-linear Glitches

Criterion for Sufficiently Large Dither Amplitude to Mitigat...

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control Technology and Applications (CCTA),

作者： Ahmad Faza’ John Leth Arnfinn A. Eielsen Dept. of Energy and Petroleum Engineering University of Stavanger Norway Dept. of Electronic Systems Automation & Control Aalborg University Denmark

A model describing glitch disturbances is introduced and analysed. The model is used to find the response of glitch disturbances when adding a periodic dither to the input and applying low-pass filtering to the output. This averaged response is used to find a criterion to determine a sufficiently large dither amplitude that will make the averaged glitch disturbances appear as a constant on the output of the low-pass filter. An important resulting property is that the averaged response to glitch disturbances is independent of the model input. By fitting the model to the measured response of a common off-the-shelf digital-to-analogue converter, simulations are used to verify the analytical results.

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Target Capturing in an Ellipsoidal Region for a Swarm of Double Integrator Agents

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 801-811页

作者： Antonio Bono Luigi D'Alfonso Giuseppe Fedele Veysel Gazi IEEE Department of Informatics ModelingElectronics and Systems EngineeringUniversity of CalabriaRende 87036Italy Dept.Electrical and Electronics Eng. Faculty of EngineeringMarmara UniversityIstanbul 34722 Department of Control and Automation Engineering Faculty of Electrical and Electronics EngineeringYildiz Technical UniversityEsenler 34220Turkey

In this paper we focus on the target capturing problem for a swarm of agents modelled as double integrators in any finite space *** agent knows the relative position of the target and has only an estimation of its velocity and *** that the estimation errors are bounded by some known values,it is possible to design a control law that ensures that agents enter a user-defined ellipsoidal ring around the moving *** know the relative position of the other members whose distance is smaller than a common detection ***,in the case of no uncertainty about target data and homogeneous agents,we show how the swarm can reach a static configuration around the moving *** simulations are reported to show the effectiveness of the proposed strategy.

关键词： Agents-based systems cooperative control swarms target-capturing

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PV Reactive Power Impact on MV Distribution Grid Operating Conditions - Case Study 8

PV Reactive Power Impact on MV Distribution Grid Operating C...

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8th IEEE International Conference on Energy Smart systems, ESS 2022

作者： Varetsky, Yuriy Lviv Polytechnic National University Dept. of Power Engineering and Control Systems Lviv Ukraine

ISBN: (数字)9798350334418

ISBN: (纸本)9798350399202

This paper presents a study on photovoltaic reactive power impact on energy loss and voltage profiles in the medium-voltage distribution grid. The concern of contradiction in the requirements of reducing load energy loss and mitigating voltage fluctuations by photovoltaic reactive power injection in the grid is also considered. An algorithm based on actual profiles of grid load and photovoltaic output has been developed to simulate the grid operating conditions. The studies have shown that the choice of an adequate photovoltaic inverter control method should be carried out by correctly analyzing the photovoltaic impact on the voltage fluctuations and energy loss in the grid. © 2022 IEEE.

关键词： Reactive power

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Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks 4

Improving the Efficiency of a Multistage Evaporator Station ...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zaiets, Nataliia Lutska, Nataliia Vlasenko, Lidiia National University of Life and Environmental Sciences of Ukraine Det. of Automation and Robotic Systems Kyiv Ukraine National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9798350346831

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department. © 2022 IEEE.

关键词： Evaporation

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Distance Based Single-Channel Target Speech Extraction

Distance Based Single-Channel Target Speech Extraction

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Runwu Shi Benjamin Yen Kazuhiro Nakadai Dept. of Systems & Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper aims to achieve single-channel target speech extraction (TSE) in enclosures by solely utilizing distance information. This is the first work that utilizes only distance cues without using speaker physiological information for single-channel TSE. Inspired by recent single-channel Distance-based separation and extraction methods, we introduce a novel model that efficiently fuses distance information with time-frequency (TF) bins for TSE. Experimental results in both single-room and multi-room scenarios demonstrate the feasibility and effectiveness of our approach. This method can also be employed to estimate the distances of different speakers in mixed speech. Online demos are available at https://***/distance-demo-page/.

关键词： Time-frequency analysis Fuses Buildings Signal processing Physiology Acoustics Data mining Speech processing

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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