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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Borau-Bernad, Javier Ramajo-Ballester, Álvaro María Armingol Moreno, José Sanchis de Miguel, Araceli Intelligent Systems Lab University Carlos III of Madrid Av. de la Universidad 30 Leganés Madrid28911 Spain Control Learning and Systems Optimization Group University Carlos III of Madrid Av. de la Universidad 30 Leganés Madrid28911 Spain

ISBN: (纸本)9789897587450

As smart cities continue to develop, they require scalable and efficient traffic monitoring systems. This paper presents a modular detection system that switches between monocular and multimodal modes, depending on the available sensors. The monocular mode, based on the MonoLSS algorithm, offers a cost-effective vehicle detection solution using a single camera, ideal for simpler or low-budget setups. In contrast, the multimodal mode integrates camera and LiDAR data via the MVX-Net model, enhancing 3D accuracy in complex traffic scenarios. This dual-mode flexibility allows smart cities to adapt the system to their infrastructure and budgetary needs, ensuring scalability as urban demands evolve. Inference results demonstrate the superior accuracy of the multimodal approach in challenging environments while validating the efficiency of the monocular mode for simpler settings. Therefore, the modular detection system offers a flexible solution that optimizes both cost and performance, effectively addressing the varied requirements of smart city traffic management. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Budget control

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Self-Tuning Network control Architectures

arXiv

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arXiv 2023年

作者： Summers, Tyler Ganapathy, Karthik Shames, Iman de Badyn, Mathias Hudoba The Control Optimization and Networks Lab University of Texas Dallas United States The Australian National University Australia The Automatic Control Laboratory ETH Zürich Switzerland

We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all available information about the time-varying network state and dynamics to optimize a performance criterion. We propose a general solution structure analogous to the classical self-tuning regulator from adaptive control. We show that a special case with full-state feedback can be solved in principle with dynamic programming, and in the linear quadratic setting the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for joint architecture-policy design. We demonstrate in numerical experiments that self-tuning architectures can provide dramatically improved performance over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

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Design of Recursive Filtering Scheme via An Improved Weighted Try-Once Discard Protocol 6

Design of Recursive Filtering Scheme via An Improved Weighte...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Zheng, Kaiwen Hu, Jun Du, Junhua Yu, Hui Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China School of Automation Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9798350356618

This paper investigates the recursive filtering (RF) problem for stochastic multi-rate (MR) systems, where the information transmission is regulated by an improved weighted try-once-discard protocol (IWTODP). In order to reduce communication overhead and mitigate network congestion, the IWTODP is firstly proposed and designed to schedule the order of data transmission from the sensors. The main objective of this paper is to design a new RF scheme to minimize the upper bound (UB) on the filtering error (FE) covariance in each iteration step in the presence of IWTODP and MR sampling. In particular, an iterative way is given to provide the expression form of the gain of RF. Finally, the effectiveness of the proposed RF method is illustrated through simulation example. © 2024 IEEE.

关键词： Stochastic systems

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

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Stability Verification of Neural Network controllers using Mixed-Integer Programming

arXiv

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arXiv 2022年

作者： Schwan, Roland Jones, Colin N. Kuhn, Daniel The Automatic Control Lab EPFL Switzerland The Risk Analytics and Optimization EPFL Switzerland

We propose a framework for the stability verification of Mixed-Integer Linear Programming (MILP) representable control policies. This framework compares a fixed candidate policy, which admits an efficient parameterization and can be evaluated at a low computational cost, against a fixed baseline policy, which is known to be stable but expensive to evaluate. We provide sufficient conditions for the closed-loop stability of the candidate policy in terms of the worst-case approximation error with respect to the baseline policy, and we show that these conditions can be checked by solving a Mixed-Integer Quadratic Program (MIQP). Additionally, we demonstrate that an outer and inner approximation of the stability region of the candidate policy can be computed by solving an MILP. The proposed framework is sufficiently general to accommodate a broad range of candidate policies including ReLU Neural Networks (NNs), optimal solution maps of parametric quadratic programs, and Model Predictive control (MPC) policies. We also present an open-source toolbox in Python based on the proposed framework, which allows for the easy verification of custom NN architectures and MPC formulations. We showcase the flexibility and reliability of our framework in the context of a DC-DC power converter case study and investigate its computational complexity. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Load Margin Assessment of Electricity-heat System Based on the Improved CPF

Load Margin Assessment of Electricity-heat System Based on t...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Dong, Hongxin Han, Zhongyang Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665402453

Energy supply security is the basis to ensure a stable operation of integrated energy system (IES). The selection of appropriate indicators and calculation methods play a pivotal role in the field of security assessment. Considering the strong coupling of multi-energy flows, large system scale and complex structure, the traditional methods which only consider single energy flow and a fixed load mode are not accurate enough to evaluate the energy supply security. To solve the above problems, a load margin assessment method based on the improved continuation power flow (CPF) is proposed in this study. Firstly, according to the steady state of electricity-heat system, a general mechanism-based model related to load increase is developed as the basic algorithm for the CPF process. Next, the CPF method is extended to the IES, where the directions of the load increase are set by the first-order difference to describe the states of the system in real-time. Considering that the load increase should be restricted in a practical range for real-world application, this paper proposes a non-negative lower load limit setting method based on empirical probability distribution. Finally, experiments on both a typical IES and an IES of an industrial park in China are performed, which validates the effectiveness and practicability of the proposed method for quantitative security assessment of electricity-heat system. © 2021 IEEE.

关键词： Probability distributions

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Policy Iteration for Multiplicative Noise Output Feedback control

Policy Iteration for Multiplicative Noise Output Feedback Co...

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IEEE Conference on Decision and control

作者： Benjamin Gravell Matilde Gargiani John Lygeros Tyler H. Summers Control Optimization and Networks Lab University of Texas Dallas Automatic Control Laboratory ETH Zürich Switzerland

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem. The algorithm solves a set of coupled Riccati equations for estimation and control arising from a partially observable Markov decision process (POMDP) under a class of linear dynamic control policies. We show in numerical experiments far faster convergence than a value iteration algorithm, formerly the only known algorithm for solving this class of problem. The results suggest promising future research directions for policy optimization algorithms in more general POMDPs, including the potential to develop novel approximate data-driven approaches when model parameters are not available.

关键词： Heuristic algorithms Riccati equations Process control Markov processes Approximation algorithms Numerical models Newton method

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Multi-robot task allocation for safe planning against stochastic hazard dynamics

Multi-robot task allocation for safe planning against stocha...

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European control Conference (ECC)

作者： Daniel Tihanyi Yimeng Lu Orcun Karaca Maryam Kamgarpour ETH Z¨urich Switzerland ABB Corporate Research Switzerland Systems Control and Multiagent Optimization Research (sycamore) lab at EPFL Switzerland

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in safety-critical exploration, surveillance, and emergency rescue missions. The multi-robot optimal control problem is challenging because of the dynamic uncertainties and the exponentially increasing problem size with the number of robots. Leveraging recent works obtaining a tractable safety maximizing plan for a single robot, we propose a scalable two-stage framework. Specifically, the problem is split into a low-level single-agent problem and a high-level task allocation problem. The low-level problem uses an efficient approximation of stochastic reachability for a Markov decision process to derive the optimal control policy under dynamic uncertainty. The task allocation is solved using forward and reverse greedy heuristics and in a distributed auction-based manner. Properties of our safety objective enable provable performance bounds on the safety of the approximate solutions of the two heuristics.

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Hierarchy-based extended CBBA for multi-robot task assignment

Hierarchy-based extended CBBA for multi-robot task assignmen...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jiajun Xia, Weiguo Li, Fuqiang Wang, Peng China National Aeronautical Radio Electronic Research Institute National Key Laboratory of Science and Technology on Avionics System Integration China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9781665465335

This paper studies the task assignment problem of a multi-robot system with complex constraints including the robot speed limit, the task execution time window constraints, the arrival time, the trip cost, and different priorities of the tasks. A new bidding function is then designed for each robot. Based on the k-means algorithm, a hierarchy-based extended consensus-based bundle algorithm (CBBA) is proposed to find the optimal solution for the task allocation problem. Finally, simulation examples are presented to show the feasibility of the proposed algorithm and comparisons with existing algorithms show that the proposed algorithm can reduce the task assignment time with guaranteed reward values. © 2022 IEEE.

关键词： Multipurpose robots

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Policy Iteration for Multiplicative Noise Output Feedback control

arXiv

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arXiv 2022年

作者： Gravell, Benjamin Gargiani, Matilde Lygeros, John Summers, Tyler H. The Control Optimization and Networks Lab University of Texas Dallas United States The Automatic Control Laboratory ETH Zürich Switzerland

关键词： Phase noise

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