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arXiv 2025年

作者： Nabou, Yassine Toma, Lucian Necoara, Ion Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest060042 Romania Electrical Power Systems Department University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

In this paper, we consider a modified projected Gauss-Newton method for solving constrained nonlinear least-squares problems. We assume that the functional constraints are smooth and the the other constraints are represented by a simple closed convex set. We formulate the nonlinear least-squares problem as an optimization problem using the Euclidean norm as a merit function. In our method, at each iteration we linearize the functional constraints inside the merit function at the current point and add a quadratic regularization, yielding a strongly convex subproblem that is easy to solve, whose solution is the next iterate. We present global convergence guarantees for the proposed method under mild assumptions. In particular, we prove stationary point convergence guarantees and under Kurdyka-Lojasiewicz (KL) property for the objective function we derive convergence rates depending on the KL parameter. Finally, we show the efficiency of this method on the power flow analysis problem using several IEEE bus test cases. © 2025, CC BY.

关键词： Newton-Raphson method

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multi-Objective Approach

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IEEE Internet of Things Journal 2025年

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y.S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this paper, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multi-objective mixed-integer program. To solve the optimization problem, we develop a hybrid heuristic approach based on Non-Dominated Sorting Genetic Algorithm II and branch-and-bound algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Wang, Yanhu Afzal, Muhammad Muzammil Li, Zhengyang Zhou, Jie Feng, Chenyuan Guo, Shuaishuai Quek, Tony Q. S. Shandong University School of Control Science and Engineering Jinan250062 China EURECOM Department of Communication Systems Sophia Antipolis06410 France Singapore University of Technology and Design Singapore487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization. As large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering, network optimization will witness a revolutionary approach. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs, and the deployment of autonomous agents as a communication bridge to seamlessly connect the machine language based LLMs with human users using natural language. Furthermore, our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions, thereby enabling the customization and optimization of the BSS process. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease. This research first develops a novel LLM-empowered BSS optimization framework, and heuristically proposes three different potential implementations: the strategies based on Prompt-optimized LLM (PoL), LLM-empowered autonomous BSS agent (LaBa), and Cooperative multiple LLM-based autonomous BSS agents (CLaBa). Through evaluation on real-world data, the experiments demonstrate that prompt-assisted LLMs and LLM-based agents can generate more efficient and reliable network deployments, noticeably enhancing the efficiency of BSS optimization and reducing trivial manual participation. © 2015 IEEE.

关键词： systems analysis

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A meta-reinforcement learning method for adaptive payload transportation with variations

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Neurocomputing 2025年 638卷

作者： Chen, Jingyu Ma, Ruidong Xu, Meng Candan, Fethi Mihaylova, Lyudmila Oyekan, John Institute of Software Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Information Technology & Management University of International Business and Economics Beijing China Department of Computer Science University of York York United Kingdom

The safe transport of cable-suspended payloads by a group of Unmanned Aerial Vehicles (UAVs) depends on their capacity to effectively respond to fluctuations in the dynamics caused by external variations, such as wind gusts. For group transportation with obstacles, internal variations, such as changes in formation, can also alter the space occupancy of the system related to collision detection. However, traditional adaptive learning methods are challenging to adapt to these two variations. In this paper, we present a learning-based method for collision-free dual-UAV–payload transportation in the presence of varied wind force and formation change. It consists of an adaptive trajectory tracking controller based on meta-model-based reinforcement learning with online adaptation and a novel correction policy, and a path planner that can sample collision-free goal states of the system for the controller based on the meta-collision predictor. The simulation results demonstrate that the proposed trajectory tracking controller outperforms state-of-the-art model-free, model-based, and variational inference methods in terms of payload tracking error reduction and robustness when dealing with the variations mentioned above. Specifically, the proposed controller reduces the average payload tracking error to less than 0.1 metres in most tasks without obstacles. Furthermore, by following the adapted paths generated by the path planner, the trajectory tracking controller can effectively track the payload while ensuring collision-free safety of the dual-UAV–payload system during navigation among obstacles. The success rate of the proposed method is more than 80% in all scenarios with obstacles. Our project website can be seen at https://***/view/meta-payload-fly/ and the source code is available at https://***/wawachen/Meta-load-fly. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Convergence rates for an inexact linearized ADMM for nonsmooth nonconvex optimization with nonlinear equality constraints

arXiv

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arXiv 2025年

作者： Bourkhissi, Lahcen El Necoara, Ion Automatic Control and Systems Engineering Department National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

In this paper, we consider nonconvex optimization problems with nonsmooth nonconvex objective function and nonlinear equality constraints. We assume that both the objective function and the functional constraints can be separated into 2 blocks. To solve this problem, we introduce a new inexact linearized alternating direction method of multipliers (ADMM) algorithm. Specifically, at each iteration, we linearize the smooth part of the objective function and the nonlinear part of the functional constraints within the augmented Lagrangian and add a dynamic quadratic regularization. We then compute the new iterate of the block associated with nonlinear constraints inexactly. This strategy yields subproblems that are easily solvable and their (inexact) solutions become the next iterates. Using Lyapunov arguments, we establish convergence guarantees for the iterates of our method toward an ϵ-first-order solution within O(ϵ−2) iterations. Moreover, we demonstrate that in cases where the problem data exhibit e.g., semi-algebraic properties or more general the KL condition, the entire sequence generated by our algorithm converges, and we provide convergence rates. To validate both the theory and the performance of our algorithm, we conduct numerical simulations for several nonlinear model predictive control and matrix factorization problems. © 2025, CC BY.

关键词： Matrix factorization

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Design of a Navigation Function for a Polyhedral World

Design of a Navigation Function for a Polyhedral World

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16th APCA International Conference on automatic control and Soft Computing, controlO 2024

作者： Nicu, Theodor-Gabriel Stoican, Florin Ioan, Daniel Prodan, Ionela Department of Automatic Control and Systems Engineering Romania National University of Science and Technology "Politehnica" Bucharest 313 Splaiul Independenţei Bucharest060042 Romania Univ. Grenoble Alpes Grenoble INP Institute of Engineering and Management LCIS Valence26000 France

ISBN: (纸本)9783031817236

This work pertains to the generation of navigation functions for obstacle avoidance and target tracking. One initially defines a so-called spherical world which can be further mapped into many other different topologies using a series of diffeomorphic transformations. This specific configuration further allows one to exploit the main advantages of the sum function approach with respect to the obstacle collision avoidance capabilities of an agent, sum function which is used in order to induce an effective navigation function judging not only from computational perspective, but also from the local minima point of view. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Topology

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Fatemeh Ahmadvand Mahmood Hosseini Imani Hamid Taheri Andani Mansour Taheri Andani Department of Control and Computer Engineering Politecnico di Torino Torino Italy Department of Energy (DENERG) Politecnico di Torino Torino Italy Department of Electronic Engineering Isfahan University of Technology Isfahan Iran School of Manufacturing Systems and Networks Arizona State University Tempe AZ

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price.

关键词： Machine learning algorithms Accuracy Electricity Neural networks Predictive models Prediction algorithms Robustness Forecasting Long short term memory Optimization

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Precision Synchronous control of Multiple Motion systems: A Tube-Based MPC Approach

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IEEE/CAA Journal of automatica Sinica 2025年第5期12卷 974-988页

作者： Shuaiqi Chen Fazhi Song Yue Dong Ning Cui Yang Liu Xinkai Chen Department of Control Science and Engineering School of Aeronautics Harbin Institute of Technology Harbin China Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology Harbin China Department of Electronic and Information Systems Shibaura Institute of Technology Tokyo Japan

Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits (ICs), requiring high-precision synchronous motion between the reticle and wafer stages. Disturbances generated by each stage during high-acceleration movements are transmitted through the base frame, resulting in degradation of synchronization performance. To address this challenge, this paper proposes a tube-based model predictive control (tube-MPC) approach for synchronization in lithography machines. First, the proposed modeling method accurately characterizes the coupling disturbances and synchronization dynamics. Subsequently, a tube-MPC approach is developed to ensure that the states of the nominal system are constrained within the terminal constraint set. To reduce the complexity of online computations, an approach is employed to transform online optimization problems into offline problems by creating an online lookup table. This enables the determination of optimal control inputs via a simplified online optimization algorithm. The robustness and trajectory tracking performance of the proposed approach are verified through simulation experiments, demonstrating its effectiveness in enhancing the synchronization performance of multiple motion systems.

关键词： Semiconductor device modeling Trajectory tracking Lithography Optimal control Transforms Prediction algorithms Robustness Synchronization Optimization Predictive control

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Study of orography–MJO interaction and its impact on rainfall variability in West Sumatra

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Journal of Earth System Science 2025年第2期134卷 1-27页

作者： Satiadi, Didi Purwaningsih, Anis Harjupa, Wendi Saufina, Elfira Fathrio, Ibnu Trismidianto Rahmatia, Fahmi Pratama, Ridho Hashiguchi, Hiroyuki Shimomai, Toyoshi Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Disaster Prevention Research Institute Kyoto University Kyoto Japan Research Institute for Sustainable Humanosphere Kyoto University Kyoto Japan Department of Electronic and Control Systems Engineering Shimane University Shimane Japan

This study examines the impact of orography and the Madden–Julian Oscillation (MJO) on rainfall patterns in West Sumatra, Indonesia, using ERA5 reanalysis data. We analyzed variables such as precipitation, zonal and vertical winds, vertically integrated moisture convergence (VIMC), Froude number, lifting condensation level (LCL), level of free convection (LFC), level of non-buoyancy (LNB), convective available potential energy (CAPE), and convective inhibition (CINH), focusing on MJO phases 3 (MJO-3) and 4 (MJO-4). The ERA5 data was validated with observations from the Agam Observation Station, including data from the equatorial atmosphere radar, X-band Doppler Radar, optical rain gauge, microwave radiometer, and radiosonde. Our analysis, covering 22 years (2001–2022) of hourly data from five locations at varying elevations, reveals that topography significantly influences rainfall distribution, with windward slopes and mountain peaks receiving the most rainfall. This pattern closely aligns with VIMC, indicating that moisture convergence is a key driver of rainfall variability. MJO-3 significantly enhances rainfall by reducing zonal wind speeds, thereby increasing moisture convergence, while MJO-4 has a minimal impact, with increased zonal wind speeds limiting moisture convergence. The study concludes that VIMC patterns, influenced by wind dynamics, are central to understanding rainfall variability in West Sumatra’s mountainous terrain under different MJO phases.

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Hierarchical Multi-Objective Optimization for Precise Performance Design of Closed-Chain Legged Mechanisms

arXiv

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arXiv 2025年

作者： Guo, Long Zhang, Ying Qin, Qi Liu, Guanjun Chen, Hanyu Yao, Yan-An Institute of Intelligent Robots and Systems School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Department of Mechanical Engineering Tsinghua University Beijing100084 China School of Mechanical and Electrical Engineering Shanxi Datong University Shanxi 037003 China

Over the past decades, the performance design of closed-chain legged mechanisms (CLMs) has not been adequately addressed. Most existing design methodologies have predominantly relied on trajectory synthesis, which inadvertently prioritizes less critical performance aspects. This study proposes a hierarchical multi-objective optimization strategy to address this limitation. First, the numerical performance-trajectory mapping is derived based on a foot-ground contact model, aiming to decouple the performance characteristics. Subsequently, a hierarchical optimization strategy is employed for two CLM design scenarios: In trajectory shape-constrained scenarios, a coarse-to-fine optimization process, integrating Fourier descriptors, refines the design from overall shape to local features. In scenarios without trajectory shape constraints, a stepwise optimization process is proposed for reconfigurable CLMs to transition from primary motion to auxiliary motion. The robustness of the proposed design strategy is validated across three configurations and seven algorithms. The effectiveness of the proposed design strategy is verified by comparison with other existing CLM design methods. The applicability of the proposed strategy is confirmed through simulation and prototype experiments. The results demonstrate that the hierarchical strategy effectively addresses the challenges of precise performance design in CLMs. Our work provides a general framework for the CLM design and offers insights for the optimization design of other closed-chain linkages. © 2025, CC BY-NC-ND.

关键词： Trajectories

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