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Adaptation of Symbolic Discrete Control Synthesis for Energy-Efficient Multi-Pocket Milling

PROCESSES 2024年第3期12卷 584页

作者： Caska, Serkan Ozbaltan, Mete Manisa Celal Bayar Univ Hasan Ferdi Turgutlu Technol Fac Dept Mech Engn TR-45400 Manisa Turkiye Erzurum Tech Univ Fac Engn & Architecture Dept Comp Engn TR-25050 Erzurum Turkiye

In engineering, cost minimization, especially in Computer Numerical Control (CNC) machining like pocket milling, is crucial. Existing tool path definition software often lacks optimization, particularly at critical starting and ending points. This study optimizes CNC machine tool paths for energy-efficient multi-pocket milling, utilizing the Symbolic Discrete Control Synthesis (SDCS) method for formal correctness. In our work, the tool path generation is formulated as a traveling salesman problem. We introduce a modeling framework to adapt SDCS to multi-pocket-milling processes, aiming to enhance precision and efficiency for potential cost savings, including energy and time, in engineering applications. This study reports experimental and comparative results, where comparative evaluations were made using metaheuristic algorithms. Our proposed approach improves CNC machining processes for multi-pocket milling. We experimentally evaluate our control algorithms and demonstrate and validate our approach through case studies.

关键词： symbolic discrete controller synthesis computer numerical control machine multi-pocket milling energy efficiency optimization algorithms

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Experimental Validation of a Predictive Control Co-Design Algorithm

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ASME Letters in Dynamic Systems and Control 2025年第3期5卷 031001页

作者： Nash, Austin L. Department of Mechanical Engineering Rose-Hulman Institute of Technology Terre Haute IN 47803

Control co-design (CCD) represents a promising solution for coordinating the physical design and control of dynamic engineering systems as technological demands become more stringent. Predictive control co-design (pCCD), recently introduced to the CCD literature, optimizes combinations of feedforward and feedback static gain sets at the system design stage to combine the robustness and preview control afforded using state-of-the-art control methods, like model predictive control (MPC), in CCD with the computational efficiency of open-loop CCD methods that solve CCD problems with a single optimization level. This work contributes the first experimental validation of pCCD to the literature. First, pCCD is performed offline on a spring-mass-damper system. The co-designed system’s optimal response is then experimentally validated online. Results are compared to an analogous system co-designed with an open-loop CCD method. The experimental system co-designed using pCCD yielded a sum squared error with respect to a desired reference signal40times smaller than the system co-designed using open-loop CCD. The results indicate that pCCD yields co-designed systems with superior online robustness in comparison to open-loop CCD methods. Moreover, systems co-designed using pCCD are more robust to both modeling error and unexpected disturbance inputs or changes in desired reference signals encountered online.

关键词： Design Predictive control Signals Dampers optimization algorithms Springs optimization algorithms optimal controls emerging control approaches predictive control for linear systems control applications

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MODELING THE BURNING OF POLYMER MATRIX: TRAINING COLLOCATION PHYSICS-INFORMED NEURAL NETWORK

MODELING THE BURNING OF POLYMER MATRIX: TRAINING COLLOCATION...

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Ghaderi, Aref Dargazany, Roozbeh Michigan State Univ Dept Civil & Env Engn E Lansing MI 48824 USA

ISBN: (纸本)9780791886717

The combined impacts of thermal, chemical, and physical processes play a significant role in the pyrolysis problems in polymeric materials. Thermal energy is transported into the material via thermal convection when the charring materials are subjected to high-temperature loading. Decomposition of the resin will result in pyrolytic gases and solid leftovers. The material can be split into three zones based on the degree of pyrolysis of the material: (i) charring zone, in which the material entirely decomposes;(ii) pyrolysis zone, in which the material is disintegrating;and (iii) virgin material zone, in which the material has not yet begun to decompose. Physics-informed neural networks (PINNs) are neural networks whose components contain model equations, such as partial differential equations (PDEs). A multi-task learning approach has emerged in which a NN must fit observed data while decreasing a PDE residual. This article introduces PINN architectures to forecast temperature distributions and the degree of burning of a pyrolysis problem in a one-dimensional (1D) and two-dimensional (2D) rectangular domain. The complex, non-convex multi-objective loss function presents substantial obstacles for forward problems in training PINNs. We discovered that adding several differential relations to the loss function causes an unstable optimization issue, which may lead to convergence to the trivial null solution or significant deviation of the solution. To address this problem, the dimensionless form of the coupled governing equations that we find most beneficial to the optimizer is used. The numerical results are compared with results obtained from PINN to show the performance of the solution. Our research is the first to explore fully coupled temperature-degree-of-burning relationships in pyrolysis problems. Unlike classical numerical methods, the proposed PINN does not depend on domain discretization. In addition to these characteristics, the proposed PINN achieves good a

关键词： Combustion Physics Modeling Artificial neural networks Polymers Pyrolysis Temperature Architecture Resins Temperature distribution High temperature Metamodeling optimization algorithms Pareto optimization Partial differential equations Numerical analysis optimization Density Specific heat Gases Thermal energy Thermal properties Thermal conductivity Convection

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A Control Theoretical Approach to Online Constrained optimization

arXiv

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arXiv 2023年

作者： Casti, Umberto Bastianello, Nicola Carli, Ruggero Zampieri, Sandro University of Padova Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden

In this paper we focus on the solution of online problems with time-varying, linear equality and inequality constraints. Our approach is to design a novel online algorithm by leveraging the tools of control theory. In particular, for the case of equality constraints only, using robust control we design an online algorithm with asymptotic convergence to the optimal trajectory, differently from the alternatives that achieve non-zero tracking error. When also inequality constraints are present, we show how to modify the proposed algorithm to account for the wind-up induced by the nonnegativity constraints on the dual variables. We report numerical results that corroborate the theoretical analysis, and show how the proposed approach outperforms state-of-the-art algorithms both with equality and inequality constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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A cost-benefit secured DAG based analysis of distributed energy generation in urban settings

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SUSTAINABLE CITIES AND SOCIETY 2024年 107卷

作者： Jing, Tianjun Zhang, Liqing Ma, Zhicheng Wang, Weizhou China Agr Univ Beijing 100083 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730070 Gansu Peoples R China State Grid Gansu Elect Power Co Lanzhou 730050 Gansu Peoples R China

This study presents a groundbreaking approach to energy management in urban areas, focusing on smart building frameworks. The proposed method integrates a Directed Acyclic Graph (DAG) architecture to enhance data security, transparency, and integrity within the smart building ecosystem. To optimize the energy management system, advanced algorithms, namely Harmony Search (HS) and Artificial Bee Colony (ABC), are employed. Notably, the HS and ABC algorithms undergo modifications to improve their efficiency and convergence properties. The innovative use of these algorithms aims to achieve global solutions in the complex optimization landscape of energy management. The study explores the synergistic effects of DAG, HS, and ABC, providing a robust and secure solution for urban energy challenges. The proposed framework is designed to address the dynamic nature of smart buildings, allowing for adaptive and efficient energy consumption. The findings contribute to the advancement of sustainable urban development, emphasizing the importance of secure data transactions and optimized energy utilization in the context of smart cities. The modified HS and ABC algorithms play a key role in fine-tuning the energy management process, offering a novel and effective approach to address the evolving energy needs of urban areas.

关键词： Smart buildings Energy management Demand response Secured architecture Time-of-use pricing optimization algorithms

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Solar Cell Parameter Estimation Using School-Based optimization Algorithm

Solar Cell Parameter Estimation Using School-Based Optimizat...

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IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Abdelghany, Reem Y. Kamel, Salah Ramadan, Abdelhady Sultan, Hamdy M. Rahmann, Claudia Aswan Univ Dept Elect Engn Aswan Egypt Minia Univ Dept Elect Engn Al Minya Egypt Univ Chile Dept Elect Engn Santiago Chile

ISBN: (纸本)9781665401272

Recently, great challenges emerged in the field of using renewable energy sources, and one of the most important of these challenges is the use of solar energy, so many researches have been done to show the behavior of solar photovoltaic (PV) cells through the current-voltage characteristic (I-V), which is determined by the circuit model's parameters. Therefore, the importance of extraction of the cell model parameters is necessary for conducting studies of controlling solar PV systems. The main goal of this article is to identify solar cell parameters using school-based optimization (SBO) algorithm. By comparing the results obtained by the SBO with those obtained by other previously published algorithms, the effectiveness and accuracy of SBO are proved. The proposed parameter estimation algorithm is applied for each of the two mathematical models (single-diode and double-diode) of the solar cell. The accuracy of the proposed algorithm was checked through current-voltage (I-V) and power-voltage (P-V) characteristics regarding the corresponding measured ones.

关键词： Photovoltaic Models Estimated Parameter optimization algorithms School-Based optimization algorithm

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A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)

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ENERGY 2024年 294卷

作者： Korkmaz, Mehmet Aksaray Univ Dept Elect & Elect Engn Aksaray Turkiye

Deep learning-based (DL) methods have recently come to the forefront among the data-driven models due to their success in capturing the complexities of the battery. Many previous DL-based studies for SoC estimation have almost exclusively focused on improving DL structure by proposing various architectures. Questions regarding the outlier or atypical predictions have yet to be adequately addressed. Furthermore, few works benefit from optimization algorithms to determine the hyperparameters of DL. In this study, we have addressed the problem of how to obtain the hyperparameter of DL and fix the improper DL predictions. To this aim, we used two different optimization algorithms to determine the hyperparameters of DL and proposed a novel algorithm that considers the previous SoC estimations. The algorithm either approves or rejects the DL predictions for the relevant step and offers new values for the rejected ones. The proposed scheme is evaluated using a battery dataset which includes different driving cycles. According to the results, it is observed that the optimized DL outperforms the empirical one by at least 35% in terms of performance indices. Moreover, the proposed novel algorithm successfully integrates into all variations and significantly improves the performance index scores.

关键词： Lithium-ion battery Deep learning Outlier removal optimization algorithms State of charge (SoC) estimation

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Migrant Resettlement by Evolutionary Multi-objective optimization

arXiv

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arXiv 2023年

作者： Liu, Dan-Xuan Gu, Yu-Ran Qian, Chao Mu, Xin Tang, Ke National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China Peng Cheng Laboratory Shenzhen518000 China Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly in recent years, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem. We implement MR-EMO using three MOEAs, the popular NSGA-II, MOEA/D as well as the theoretically grounded GSEMO. To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO-SR, using matrix-swap mutation and repair mechanism, which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Load frequency control in smart grids: A review of recent developments

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2024年第PartA期189卷

作者： Wadi, Mohammed Shobole, Abdulfetah Elmasry, Wisam Kucuk, Ismail Istanbul Sabahattin Zaim Univ Elect Engn Dept Halkalı Cad 281 Halkali Kucukcekmece TR-34303 Istanbul Turkiye Istanbul Kultur Univ Comp Engn Dept Atakoy Yerleskesi E5 Karayolu Uzeri Bakirkoy TR-34158 Istanbul Turkiye

This study provides a comprehensive and fresh review of load frequency control (LFC) in multi-area interconnected power systems (MAIPSs). The central tasks of LFC are to keep frequency variations as minimum as possible to achieve an acceptable level of stability. This research provides a complete view, from early classical control to recent technologies and modern techniques considering strategies, robust, optimal, self -tuning, and adaptive controllers for LFC in MAIPSs. Fuzzy control and earlier and recent optimization algorithms also are analyzed. The linearity, nonlinearity, and uncertainty of LFC models are also investigated. This review emphasizes recent technological advances and novel control strategies. LFC is also considered with the integration of wind, photovoltaic, electric vehicles, and storage devices. Besides, the utilization of machine learning and reinforcement techniques is examined. Further, LFC in smart grids and modern complex power systems concerning limited communication bandwidth, communication failure, and cyber-attacks are also investigated. This review provides an in-depth and detailed diagnosis of the challenges associated with LFC in modern and complex power systems. This work may be valuable for studies and practitioners interested in LFC. It, in detail, investigates future efforts and directions to enhance LFC performance, stability, and reliability in the face of increasing complexity and uncertainty.

关键词： Multi-area interconnected power system (MAIPS) Load frequency control (LFC) optimization algorithms Renewable energy Artificial intelligence (AI) Smart grids Electric vehicles (EVs) Communications failures Cyber attacks

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Accurate prediction of electric vehicles mileage for proactive battery management in varied conditions using multi-criteria approach

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： V. Jog, Vivek Simu, Shreyas Don Bosco Coll Engn Comp Engn Dept Margao 403602 Goa India Don Bosco Coll Engn Elect & Comp Engn Dept Margao 403602 Goa India

This research addresses several critical challenges in managing battery aging within Electric Vehicles (EVs). Key challenges include minimizing capacity degradation while maximizing vehicle performance and accurately predicting State of Charge (SoC) under diverse operational conditions. Additionally, integrating factors such as vehicle and battery age, driving cycles, environmental conditions, and regional climate variations posed significant hurdles in achieving comprehensive battery management. To tackle these challenges, the research presents a novel framework integrating an advanced Multihead cross attention-based optimizer with a bidirectional long short-term memory network, further enhanced by the coati optimization algorithm. This innovative approach aims to improve prediction accuracy for critical battery performance metrics, such as SoC and battery lifetime. Implemented and rigorously evaluated using Python, the framework demonstrates a substantial 20% increase in battery performance through detailed SoC and temperature analysis, coupled with an impressive 40% enhancement in battery lifetime. These results highlight the ability to overcome previous challenges and provide robust solutions for effective battery management in real-world EV applications.

关键词： Battery decaying monitoring Electric vehicles Lithium-ion batteries State of charge Machine learning optimization algorithms

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