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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Xu, Xiaoyi Luo, Hao Wang, Hao Chow, Mo-Yuen Huang, Cong-Sheng Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9781665401159

Lithium battery has been widely used in electric vehicle (EV) and microgrid applications as an outstanding energy storage device. To optimally control the batteries, the nonlinear battery dynamics and the battery State-of-Charge (SOC) need to be real-time and accurately estimated by the battery management systems (BMSs). Compared with traditional estimation algorithms, the observer-based adaptive residual generator has the advantages of small calculation amount, stability, and adaptability. It can accurately estimate the SOC of a battery with a high excited load current. However, in the case of a low excited load current, the SOC estimation effect is poor. Besides, as a data-driven algorithm, the adaptive residual generator's estimation effect will deteriorate when affected by measurement noise. This paper proposes a moving window scheme for the adaptive residual generator and analyzes the parameter setting range to ensure the stability and convergence of the algorithm, which provides guidance for parameter selection when applying the algorithm. On the one hand, the proposed moving window scheme effectively reduces the high excitation requirement of the adaptive residual generator for the load current. Even under low excitation, the moving window adaptive residual generator can still accurately estimate the SOC. On the other hand, the addition of a moving window effectively improves the anti-disturbance ability of the adaptive residual generator. The performance of the proposed method is verified by lithium battery simulation experiments. © 2021 IEEE.

关键词： Lithium-ion batteries

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DECENTRALIZED DIFFUSION-BASED LEARNING UNDER NON-PARAMETRIC LIMITED PRIOR KNOWLEDGE

arXiv

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arXiv 2023年

作者： Wachel, Pawel Kowalczyk, Krzysztof Rojas, Cristian R. Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wroclaw Poland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

We study the problem of diffusion-based network learning of a nonlinear phenomenon, m, from local agents’ measurements collected in a noisy environment. For a decentralized network and information spreading merely between directly neighboring nodes, we propose a non-parametric learning algorithm, that avoids raw data exchange and requires only mild a priori knowledge about m. Non-asymptotic estimation error bounds are derived for the proposed method. Its potential applications are illustrated through simulation experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Electronic data interchange

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

引用

Iranian Conference on electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Tabular Dyna-Q Algorithm for Online Calculation of LQR Gains: A Simulation Study

Tabular Dyna-Q Algorithm for Online Calculation of LQR Gains...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Kian Farooghi Mahdieh Samadi Hamid Khaloozadeh Department of Systems and Control K. N. Toosi University of Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

This paper presents a novel approach for the online calculation of Linear Quadratic Regulator (LQR) gains using the Tabular Dyna-Q algorithm. By leveraging Q-learning, this technique enables the determination of gains for unknown dynamic systems by using input-output data only. The proposed algorithm is particularly advantageous for situations where acquiring input-output data is expensive or time-consuming, as it effectively reduces computation time by reusing previous data. To validate the accuracy of the calculations and control scheme, a simulation example is provided. The results indicate the effectiveness of the Tabular Dyna-Q algorithm in accurately computing LQR gains and its potential application in real-world scenarios. This research contributes to advancing the field of control theory by offering an efficient and reliable method for online gain calculation in dynamic systems.

关键词： Q-learning Regulators System dynamics Heuristic algorithms Computational modeling Riccati equations Data collection

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Design, techno-economic evaluation, and experimental testing of grid connected rooftop solar photovoltaic systems for commercial buildings

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Frontiers in Energy Research 2024年 12卷

作者： Habib, Salman Tamoor, Muhammad Gulzar, Muhammad Majid Chauhdary, Sohaib Tahir Ahmad, Hasnain Alqahtani, Mohammed Khalid, Muhammad Control and Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering and Technology Government College University Faisalabad Faisalabad Pakistan King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical and Computer Engineering College of Engineering Dhofar University Salalah Oman Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences PIEAS Islamabad Pakistan Department of Industrial Engineering and Center for Engineering and Technology Innovations King Khalid University Abha Saudi Arabia Dhahran Saudi Arabia

This study aims to investigate the potential of rooftop solar photovoltaic systems for commercial buildings. Helio-Scope software is utilized to perform simulations to determine the ideal rooftop area for photovoltaic panels. The efficiency of photovoltaic systems is impacted by the shading effects of photovoltaic modules installed in parallel rows. To enhance energy output, the optimal distance between rows is determined, and it is found that 5-feet inter-row spacing provides the best results. The simulation results indicate that with 5-feet inter-row spacing, photovoltaic system has an energy generation of 371.6 MWh, specific yield of 1508.0 kWh/kWp, performance ratio of 82.1%, solar access rate of 98.9%, total solar resource fraction of 96.3% and a total irradiance of 1655.9 kWh/m2. The annual nameplate energy is 425.1 MWh, output energy at irradiance levels is 423.1 MWh, optimal DC output is 378.5 MWh, inverter output is 373.5 MWh, and total energy delivered to the national power grid is 371.6 MWh. The average daily DC inverter input power is 158881.5110 W and the average daily AC inverter output power is 152231.6311 W, showing an inverter efficiency of approximately 95.93%. Moreover, detailed testing of the installed PV system is performed on-site to make sure that equipment’s performance guarantees are achieved, the system is properly installed and its configuration is suitable for commercial operations. The maximum daily output energy generation of an installed photovoltaic (PV) system is 1.33 MWh, and its average energy generation is 1.09 MWh. The voltage of all strings is within the rated range of the inverter, with a maximum voltage of 835 V and a minimum of 698 V, as tested by PV string open-circuit voltage. The inverter efficiency test is also performed, with a maximum efficiency of 98.83% and fill factors ranging from 81.37% to 82.34%. The payback period of a photovoltaic system is 4.22 years and LCOE is 0.0229$/kWh. PV system saved 215569.818 metric to

关键词： Solar power generation

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Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication

arXiv

引用

arXiv 2023年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. The Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States Hong Kong The Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used;the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples. © 2023, CC BY.

关键词： Directed graphs

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Observer-Based Consensus of Nonlinear Positive Multi-Agent Systems with Saturated control Input

arXiv

引用

arXiv 2022年

作者： Zaman, Amirreza Birk, Wolfgang Atta, Khalid Tourkey Control Engineering Group Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea Sweden

This paper presents the distributed pinning consensus solution for nonlinear positive multi-agent systems with nonlinear control input by applying observer-based control protocols. The network topology is considered as a directed and fully connected structure. By considering sector input nonlinearities and various forms of topologies, two kinds of state observers involving standard observer and distributed pinning observer are presented for each regarded nonlinear agent by applying a novel analysis directly dealing with the nonlinear input and nonlinear system dynamics. The measured local output detail outlines the first observer, and the other observer is achieved via the corresponding output detail of its adjacent agents. Based on further observed state details, a distributed pinning observer-based strategy is derived for the leader-follower nonnegative global consensus of the nonlinear positive multi-agent system. Additionally, two multi-step algorithms are proposed to set up the observer gains and each protocol criterion. Performance evaluations are provided to confirm the proposed control method and illustrate the effectiveness of the derived non-negative consensus observer-based protocols. © 2022, CC BY.

关键词： Convex optimization

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Deep Learning for Improved Aircraft Identification in Shared Airspace

SSRN

引用

SSRN 2024年

作者： Ahmed, Aisha Sir Elkhatem Engin, Seref Naci Malik, Zainab Abdalgadir, Shahd Aeronautical Engineering Department Sudan University of Science and Technology Khartoum407 Sudan Computer Engineering Department Yildiz Technical University Istanbul34220 Turkey Department of Electrical and Electronics Engineering Sakarya University of Applied Science Sakarya54050 Turkey Control and Automation Engineering Department Yildiz Technical University Istanbul34220 Turkey

Ensuring accurate identification of approaching aircraft is a crucial task for Air Traffic control (ATC) systems. This is essential to prevent misidentification accidents, occurring especially in shared airspace. Misidentification incidents may occur due to "Mistaken Identity" and a system malfunction that results in the aircraft crew being unable to respond to a radar control sector call, as well as radio signal problems due to bad weather. While military Identification Friend or Foe (IFF), and Automatic dependent surveillance-broadcast (ADS-B) systems have traditionally served this purpose, their reliability diminishes in cases where stealthy aircraft, during extreme weather conditions and spacious oceanic regions, are involved. In addition, radio signal- based surveillance systems are reliance on aircraft transponders. Under certain circumstances, this may pose a serious problem for the ATC. This paper introduces a novel approach utilizing deep Convolutional Neural Networks (CNN) for the identification of a type of approaching aircraft as either military or civilian, specifically applied to remote-sensing satellite imagery. The objective is to robustly and accurately determine the type of aircraft whether hostile or friendly to enhance radio signal-based surveillance systems detectability. Experimental evaluation incorporates three distinct datasets: noise-free datasets, noisy datasets, and images affected by varying illumination conditions. The experimental results reveal a remarkably high identification accuracy, averaging 99.50%, across both noise-free and augmented datasets. The introduction of noisy datasets is accomplished through the application of a median filter. However, the results also point out an intrinsic challenge posed by illumination variations that affect CNN classification accuracy. © 2024, The Authors. All rights reserved.

关键词： Air traffic control

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret

arXiv

引用

arXiv 2024年

作者： Hua, Youqing Liu, Shuai Hong, Yiguang Johansson, Karl Henrik Wang, Guangchen School of Control Science and Engineering Shandong University Jinan China Nanyang Technological University Singapore Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player. Only the loss value at the current round, rather than the entire loss function or any other information (e.g. gradient), is privately revealed to the player. Players aim to minimize a sequence of global loss functions, which are the sum of local losses. We observe that traditional multi-point bandit algorithms are unsuitable for online optimization, where the data for the loss function are not all a priori, while the one-point bandit algorithms suffer from poor regret guarantees. To address these issues, we propose a novel one-point residual feedback distributed online algorithm. This algorithm estimates the gradient using residuals from two points, effectively reducing the regret bound while maintaining O(1) sampling complexity per iteration. We employ a rigorous metric, dynamic regret, to evaluate the algorithm's performance. By appropriately selecting the step size and smoothing parameters, we demonstrate that the expected dynamic regret of our algorithm is comparable to existing algorithms that use two-point feedback, provided the deviation in the objective function sequence and the path length of the minimization grows sublinearly. Finally, we validate the effectiveness of the proposed algorithm through numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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