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arXiv 2025年

作者： Yang, Meng Chen, Jun Gong, Wenping Wei, Longsheng Tian, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Faculty of Engineering China University of Geosciences Wuhan430074 China Electronic Information School Wuhan University Wuhan430072 China

Complicated nonlinear intensity differences, nonlinear local geometric distortions, noises and rotation transformation are main challenges in multimodal image matching. In order to solve these problems, we propose a method based on Frequency-domain Information of Local Energy Response called FILER. The core of FILER is the local energy response model based on frequency-domain information, which can overcome the effect of nonlinear intensity differences. To improve the robustness to local nonlinear geometric distortions and noises, we design a new edge structure enhanced feature detector and convolutional feature weighted descriptor, respectively. In addition, FILER overcomes the sensitivity of the frequency-domain information to the rotation angle and achieves rotation invariance. Extensive experiments multimodal image pairs show that FILER outperforms other state-of-the-art algorithms and has good robustness and *** Codes 68U10 (Primary), 68T45, 68Wxx (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Mathematical transformations

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Design of Supervisory Model Predictive control for Building HVAC System Subject to Time-Varying Operating Points

Design of Supervisory Model Predictive Control for Building ...

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International Conference on control, automation and Systems ( ICCAS)

作者： Sitthiphat Promchaiwattana David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Intelligent Control Automation of Process Systems Research Unit Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

This paper presents the design of supervisory model predictive control for HVAC systems with consideration of peak demand shaving and thermal comfort. In this work, we analyze the operating points with the thermal comfort requirement. Afterwards, we linearize the nonlinear dynamic model to a linear time-varying model. In particular, we specify that the operating point of the open-loop HVAC system satisfies the thermal comfort condition. This makes the predicted mean vote (PMV) equal to zero or nearly to zero. Subsequently, we calculate the zone temperature, the zone humidity ratio, and the volumetric flow rate. When considering the time-varying disturbance profiles, it appears that the operating points are time-varying. We observe that the coefficient of performance (COP) and the operating points are time-varying which lead to time-varying model. Moreover, we design supervisory control (SC) and model predictive control (MPC) for linear time-varying model. In the SC design, we aim to find the optimal reference zone temperature which is used as the reference signal for MPC. In the MPC design, the objectives are twofold. First, the zone temperature tracks the reference signal. Second, both electrical energy cost and deviation of desired thermal comfort are minimized. The computer simulation shows that MPC of time-varying HVAC system yields the best reference tracking. In addition, it reduces total electrical energy cost the most. We conclude that the optimal temperature reference can be effectively applied to the building HVAC systems while the occupants feel comfortable and the total electrical energy cost is the lowest.

关键词： HVAC Costs Input variables Buildings Predictive models Supervisory control Thermal analysis

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An Optimizing Method Based on Combining Edge Computer and Long Short-Term Memory Networks Applied to Solar Power Forecasting

An Optimizing Method Based on Combining Edge Computer and Lo...

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2021 IEEE International Future Energy Electronics Conference, IFEEC 2021

作者： Nguyen, Minh-Ngoc Pham, Manh-Hai Wu, Yuan-Kang Nguyen, Ngoc-Trung Phenikaa University Faculty of Electrical and Electronic Engineering Hanoi Viet Nam Electric Power University Faculty of Energy Technology Hanoi Viet Nam National Chung-Cheng University Department of Electrical Engineering Chiayi Taiwan Electric Power University Control and Automation Faculty Hanoi Viet Nam

ISBN: (纸本)9781665434485

Along with the rapid development of solar energy sources on the global power system,the challenges related to the weather instability of this power type are also of increasing concern. Intending to solve these challenges,the study of solar power forecasting has become one of the most important problems in the power system field. Most of the published forecast algorithms aim to minimize prediction errors. However,in reality,convenience and data processing time are two necessary goals to commercialize a forecasting software. Recently,IoT is emerging as an effective solution to solve the above multi-objective problem. Especially,in the current trend of real-time forecasting,the application of Edge Computer in the IoT environment is bringing new opportunities for the goal of reducing data processing time while ensuring acceptable forecast errors. Moreover,Edge Computer also reduces the risk of network congestion due to its distributed processing characteristics. This paper introduces a new methodology using Edge Computer combined with the LSTM algorithm applied to the solar power forecasting problem. Real-time calculation results show that the accuracy can be improved by the good choice of the converted data type while the execution time is halved compared to without using Edge Computer. © 2021 IEEE.

关键词： Solar energy

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Signal Processing for Acoustic Emission Signature Analysis and Defect Detection

Signal Processing for Acoustic Emission Signature Analysis a...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Dmitry O. Kuzivanov Dinara A. Altay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation Department of Natural Sciences Military Engineering Institute of Radio Electronics and Communications Almaty Kazakhstan

This article presents the results of implementing a digital signal processing method for analyzing the acoustic emission (AE) signature and detecting defects such as cracks and pores. The signal processing method is based on a cascade connection of digital high-pass filters and ensures the selection of informative signals and increases the signal-to-interference ratio. In order to control the formation of defects in the process of selective laser melting, the paper presents the results of experimental testing of the cascade filtration method for detecting defects in the internal structure such as cracks and pores. The signal components recorded during the development of defects during the growth of additive manufacturing products were extracted. AE signatures were analyzed and statistical relationships were assessed. The relationship between the parameters of the AE signal and the values of laser radiation power has been identified, which characterizes the process of defect formation.

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Data-driven control of consensus tracking for discrete-time multi-agent systems

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Journal of the Franklin Institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

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DriveRP: RAG and Prompt engineering Embodied Parallel Driving in Cyber-Physical-Social Spaces

DriveRP: RAG and Prompt Engineering Embodied Parallel Drivin...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Jun Huang Hao Ma Tengchao Zhang Fei Lin Siji Ma Xiao Wang Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Artificial Intelligence Anhui University Hefei China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In recent years, numerous technological advancements in Artificial Generative Intelligences (AGIs) have demonstrated significant potential to transform the intelligence acquisition mechanisms in connected autonomous vehicles (CAVs). Integrating technologies like ChatGPT into CAVs can enhance human-machine interactions. However, the emergence of such new traffic entities may introduce unforeseen hallucinations and complex risks that surpass our current understanding. To address these challenges, Retrieval-Augmented Generation (RAG) and prompt engineering technologies are being explored to enhance the reliability and safety of autonomous driving systems. RAG retrieves relevant contextual information, such as driving experiences and real-time road network status, from external databases to ensure that foundation models have access to accurate and timely data for informed decision-making. Prompt engineering optimizes the performance of large language models in autonomous driving systems by designing and refining prompts that guide the models’ responses, thereby improving their relevance and accuracy in various driving scenarios. Together, these technologies enhance the robustness and trustworthiness of autonomous driving systems. This paper proposes DriveRP, a framework that integrates RAG and prompt engineering within the Descriptive-Predictive-Prescriptive Intelligence framework of Parallel Driving theory. DriveRP aims to enhance the safety and interpretability of autonomous vehicle trajectory planning, decision-making, and motion control, ultimately achieving the "6S" goals. Grounded in Digital Twins and Metaverse-embodied parallel driving theory, DriveRP provides the infrastructure and foundational intelligence for parallel driving with Multi-modal Large Lange Models(MLLMs). Additionally, the paper discusses future trends and potential research directions, focusing on the "6S" goals of parallel driving: Smart, Safe, Secure, Sensitive, Sustainable, and Serviceable.

关键词： Accuracy Trajectory planning Decision making Transportation Transforms Safety Digital twins Prompt engineering Motion control Autonomous vehicles

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On Complexity Reduction in a Variable Terminal Set Setpoint-Tracking MPC Scheme

On Complexity Reduction in a Variable Terminal Set Setpoint-...

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IEEE Conference on Decision and control

作者： Bogdan Gheorghe Florin Stoican Ionela Prodan Faculty of Automation Control and Computer Science University Politehnica of Bucharest Romania Univ. Grenoble Alpes Grenoble INP LCIS Valence France Institute of Engineering and Management Univ. Grenoble Alpes

We applied and adapted the linear encodings from [1] to the feasible-reference tracking model predictive control (MPC) formulation from [2] to reduce its computational cost. The improvements come from avoiding to explicitly use the vertex-based representation of the variable terminal set in testing its inclusion in the constraint set. We considered both polytopic and zonotopic formulations. For the later we have also proposed a positive invariant (PI) zonotopic approximation of the maximal PI set.

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A Swarm Model for Target Capturing in a Polygonal Strip

A Swarm Model for Target Capturing in a Polygonal Strip

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Antonio Bono Luigi D' Alfonso Giuseppe Fedele Veysel Gazi Department of Informatics Modeling Electronics and Systems Engineering University of Calabria Rende Italy Department of Control and Automation Engineering Faculty of Electrical and Electronics Engineering Yildiz Technical University Esenler Turkey

This paper presents a methodology to generate proper state trajectories of a swarm of agents, described as a single integrator model. The main goal of the proposed methodology is to route the agents into planar polygons like areas, so that to accomplish for cooperative perimeter surveillance or target capturing tasks. In particular, the agents are driven into a polygonal strip defined by a given containment external convex polygonal region and an internal forbidden convex polygonal region around a prescribed target. The control law is designed looking at a circular strip, then a conversion from circular to polygonal areas is applied to achieve the prescribed goal. Steady-state conditions are analyzed and sufficient conditions derived in terms of the control law parameters. Simulation results put in light the main properties of the resulting kinematic state description.

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Regenerative Braking System in E-Trike: Interval Type-2 Fuzzy Logic control to Enhance Driving Range

Regenerative Braking System in E-Trike: Interval Type-2 Fuzz...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Rizalul Wahid Endra Joelianto Bentang Arief Budiman Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Electric vehicles (EV s) boast zero tailpipe emissions, directly improving air quality and lowering greenhouse gas emissions. Despite the promise of EVs for sustainable transportation, their limited range is a key challenge. Regenerative braking is a promising approach for enhancing the driving range. This research proposes a study of Interval Type-2 Fuzzy Logic (IT2FL) control to improve regenerative braking efficiency in an electric trike. Employing IT2FL control for regenerative braking recovers 35.84% of energy, translating to a 91.053 km increase in driving range. The IT2FL method achieved a range of 50 km/kWh, representing a significant 41.7% improvement. Furthermore, utilizing the Nie-Tan Method reducer within the IT2FL framework could extend the range by an additional 2.3 km, resulting in a 3 % increase in energy recovery. These findings demonstrate the effectiveness of IT2FL control in enhancing regenerative braking efficiency for the electric trike.

关键词： Fuzzy logic Electric potential Transportation Electric vehicles Air quality Informatics Greenhouse gases

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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