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International Symposium on Intelligent systems and Informatics (SISY)

作者： Máté Fazekas Péter Gáspár Systems and Control Lab HUN-REN Institute for Computer Science and Control Hungarian Research Network (HUN-REN SZTAKI) Budapet Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics (BME) Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

In the era of autonomous vehicles, state estimation is crucial for planning and control. While various sensors like GNSS, IMU, cameras, and wheel encoders provide essential data, each has limitations. Sensor fusion, especially combining GNSS and IMU, addresses these challenges. Recently, data-based techniques using machine learning tools have emerged to enhance tuning processes. However, if an architecture that has a practical impact is utilized, the training of the neural net results in a complicated task. This paper explores different state estimation architectures in the era of data-based techniques and proposes an algorithm generating reference tuning values. The presented methods are tested with real vehicle measurements.

关键词： Training Global navigation satellite system Neural networks Wheels Sensor fusion Sensors Planning State estimation Tuning Autonomous vehicles

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Learning-to-control relaxation systems with the step response

Learning-to-control relaxation systems with the step respons...

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American control Conference (ACC)

作者： Ross Drummond Hamed Taghavian Pablo R. Baldivieso-Monasterios The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The problem of learning-to-control relaxation systems from data is considered. It is shown that the equi-librium of the relaxation system's step response defines the solution of a class of robust control problems and provides a good suboptimal solution to a class of linear quadratic regulator problems. These results demonstrate the potential to efficiently learn policies for these control problems from a single, easy-to-implement trajectory data point, being the step response. More broadly, these results highlight how the system structure and problem definition of the control problem can be exploited to generate data efficient learning- to-control methods.

关键词： Robust control Linear systems Regulators System dynamics Optimal control Trajectory

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Insight study of rare earth elements in PM_(2.5)during five years in a Chinese inland city:Composition

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Journal of Environmental Sciences 2024年第4期138卷 439-449页

作者： Yi-Wen Shen Chang-Xian Zhao Hao Zhao Shuo-Fei Dong Qi Guo Jiao-Jiao Xie Mei-Ling Lv Chun-Gang Yuan Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department ofEnvironmental Science&EngineeringNorth China Electric Power UniversityBaoding 071000China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China Agilent Technologies Co.Ltd.(China) Beijing 100102China

The booming development of rare earth industry and the extensive utilization of its products accompanied by urban development have led to the accelerated accumulation of rare earth elements(REEs)as emerging pollutants in atmospheric *** this study,the variation of REEs in PM_(2.5)with urban(a non-mining city)transformation was investigated through five consecutive years of sample *** compositional variability and provenance contribution of REEs in PM_(2.5)were characterized,and the REEs exposure risks of children and adults via inhalation,ingestion and dermal absorption were also *** results showed an increase in the total REEs concentration from 46.46±35.16 mg/kg(2017)to 81.22±38.98 mg/kg(2021)over the five-year period,with Ce and La making the largest *** actual increment of industrial and traffic emission source among the three pollution sources was 1.34 ng/m^(3).Coal combustion source displayed a downward *** was the main exposure pathway for REEs in PM_(2.5)for both children and *** contributed the most to the total intake of REEs in PM_(2.5)among the population,followed by La and *** exposure risks of REEs in PM_(2.5)in the region were relatively low,but the trend of change was of great *** was strongly recommended to strengthen the concern about traffic-related non-exhaust emissions of particulate matter.

关键词： Rare earth elements Exposure risks Pollution sources Traffic-related non-exhaust emissions

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Using Optimal Filters for Plug-in Type Repetitive controllers for Periodic Inputs 28

Using Optimal Filters for Plug-in Type Repetitive Controller...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Diaconu, Larisa Vilanova, Ramon Barbu, Marian Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Universitat Autonoma de Barcelona Department of Telecommunications and Systems Engineering Barcelona Spain

ISBN: (纸本)9798350339918

This paper proposes some solutions to optimize the Butterworth filter from the structure of plug-in type repetitive controllers, as well as the use of the Bessel filter, which offers a simpler way of choosing the anticipatory factor from the repetitive controller structure. The comparative analysis of the two types of filters was made on the basis of three criteria: 1) the standard deviation of the error in the control loop, where the first type of filter proved to be superior;2) ease of obtaining the optimal value of the anticipative parameter, in which case the Bessel filter is preferred;3) sensitivity to changes in the filter parameters and/or process model, where the second filter is also preferred © 2023 IEEE.

关键词： Butterworth filters

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Parallel Domain-Decomposition Algorithms for Complexity Certification of Branch-and-Bound Algorithms for Mixed-Integer Linear and Quadratic Programming

arXiv

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arXiv 2025年

作者： Shoja, Shamisa Arnström, Daniel Axehill, Daniel Division of Automatic Control Department of Electrical Engineering Linköping University Sweden Division of Systems and Control Department of Information Technology Uppsala University Sweden

When implementing model predictive control (MPC) for hybrid systems with a linear or a quadratic performance measure, a mixed-integer linear program (MILP) or a mixed-integer quadratic program (MIQP) needs to be solved, respectively, at each sampling instant. Recent work has introduced the possibility to certify the computational complexity of branch-and-bound (B&B) algorithms when solving MILP and MIQP problems formulated as multi-parametric MILPs (mp-MILPs) and mp-MIQPs. Such a framework allows for computing the worst-case computational complexity of standard B&B-based MILP and MIQP solvers, quantified by metrics such as the total number of LP/QP iterations and B&B nodes. These results are highly relevant for real-time hybrid MPC applications. In this paper, we extend this framework by developing parallel, domain-decomposition versions of the previously proposed algorithm, allowing it to scale to larger problem sizes and enable the use of high-performance computing (HPC) resources. Furthermore, to reduce peak memory consumption, we introduce two novel modifications to the existing (serial) complexity certification framework, integrating them into the proposed parallel algorithms. Numerical experiments show that the parallel algorithms significantly reduce computation time while maintaining the correctness of the original framework. © 2025, CC BY.

关键词： Integer linear programming

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Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent systems: A Distributed Optimization Approach (extended version)

arXiv

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arXiv 2025年

作者： Mehdifar, Farhad Dhullipalla, Mani H. Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Division of Systems and Control The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents’ objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

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CDQKL: Consensus-Based Distributed Quantum Kernel Learning

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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Quantum Communications, networking, and Computing (QCNC), International Conference on

作者： Wenxuan Ma Mengxiang Liu Ruilong Deng State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (数字)9798350366778

ISBN: (纸本)9798350366785

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL.

关键词： Training Adaptation models Quantum computing Accuracy Training data Machine learning Market research

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Phase of Multivariable systems: A Revisit via H2T-Dissipativity 62

Phase of Multivariable Systems: A Revisit via H2T-Dissipativ...

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62nd IEEE Conference on Decision and control, CDC 2023

作者： Wang, Dan Chen, Wei Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science Beijing China

ISBN: (纸本)9798350301243

A new notion of phase of multi-input multi-output (MIMO) systems was recently defined and studied, leading to new understandings in various fronts including a formulation of small phase theorem, a performance criterion named H∞ phase sector, and a sectored real lemma, etc. In this paper, we define a new notion of H2T-dissipativity and show the connection between the phase of a multivariable linear time-invariant (LTI) system and the H2T-dissipativity. The H2T-dissipativity, roughly speaking, is dissipativity restricted to the time-domain H2 space which consists of L2 signals with only positive frequency components. In addition, by exploiting the newly defined H2T- dissipativity, we also study the phase of a feedback system and provide a physical interpretation of the sectored real lemma. © 2023 IEEE.

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Green Routing Game: Pollution-Aware Mixed Fleet Logistics with Shared Charging Facilities

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IEEE Transactions on Automatic control 2025年第7期70卷 4330-4343页

作者： Sasahara, Hampei Dan, Gyorgy Amin, Saurabh Sandberg, Henrik Institute of Science Tokyo Department of Systems and Control Engineering Tokyo152-8552 Japan KTH Royal Institute of Technology Division of Network and Systems Engineering StockholmSE-100 44 Sweden Massachusetts Institute of Technology Laboratory of Information and Decision Systems CambridgeMA02139 United States KTH Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

Eco-friendly freight operations are crucial for decarbonizing the transportation sector. Systematic analysis of policy measures requires a principled modeling approach. While the commonly used model referred to as routing game considers the congestible nature of transportation facilities, exiting models fail to account for environmental factors. This paper aims at providing a mathematical framework to study strategic interaction between owners of mixed fleets comprising of both internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. This study introduces a "green"routing game with incomplete information that models strategic interaction among multiple logistic operators. These players face a pollution tax imposed on ICEVs and a potential delayed delivery cost due to EV charging requirements with uncertainty. In contrast to existing models, this novel model captures the players' trade-off between lengthier congestion delay at charging stations as the share of EV trucks increases and higher pollution costs with increased ICEVs usage, with uncertainty determined by a latent state. We first provide equilibrium characterization and present a condition for essential uniqueness. We show that this equilibrium can be computed in a distributed manner using a gradient projection method. We then introduce a public information system that broadcasts real-time information about the latent state. Importantly, we analyze value of information for providing a condition for the public information to be beneficial. Finally, we present numerical examples to illustrate settings where environmental taxation and information dissemination can improve social welfare. © 1963-2012 IEEE.

关键词： Trucks

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Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models

arXiv

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arXiv 2025年

作者： van Hulst, J.S. Heemels, W.P.M.H. Antunes, D.J. Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Netherlands

Reinforcement learning (RL) is a powerful tool for decision-making in uncertain environments, but it often requires large amounts of data to learn an optimal policy. We propose using prior model knowledge to guide the exploration process to speed up this learning process. This model knowledge comes in the form of a model set to which the true transition kernel and reward function belong. We optimize over this model set to obtain upper and lower bounds on the Q-function, which are then used to guide the exploration of the agent. We provide theoretical guarantees on the convergence of the Q-function to the optimal Q-function under the proposed class of exploring policies. Furthermore, we also introduce a data-driven regularized version of the model set optimization problem that ensures the convergence of the class of exploring policies to the optimal policy. Lastly, we show that when the model set has a specific structure, namely the bounded-parameter MDP (BMDP) framework, the regularized model set optimization problem becomes convex and simple to implement. In this setting, we also show that we obtain finite-time convergence to the optimal policy under additional assumptions. We demonstrate the effectiveness of the proposed exploration strategy in a simulation study. The results indicate that the proposed method can significantly speed up the learning process in reinforcement learning. © 2025, CC BY-NC-ND.

关键词： Uncertainty analysis

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