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Recent Developments in Event-Triggered control of Nonlinear Systems:An Overview

无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Learning-Based State Estimation for Automated Lane-Changing 27

Learning-Based State Estimation for Automated Lane-Changing

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ha, Won Yong Chakraborty, Sayan Lin, Xiaoyi Ozbay, Kaan Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering 370 Jay Street BrooklynNY11201 United States C2SMARTER Center New York University Department of Civil and Urban Engineering 6 MetroTech Center BrooklynNY11201 United States

ISBN: (纸本)9798331505929

This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. This approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. The results from real-world experiments demon-strate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. This paper underscores the effectiveness of integrating Adaptive Dynamic Program-ming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. These experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving. © 2024 IEEE.

关键词： State estimation

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Optimal Time and Power Allocation in Full-Duplex Wireless-Powered Cognitive Radio Networks 13

Optimal Time and Power Allocation in Full-Duplex Wireless-Po...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Feng, Zhenyu Su, Ming Zhou, Meihua Liu, Zhixin School of Electrical Engineering Yanshan University Qinhuangdao066004 China Training Center of Liren College Yanshan University China Key Lab of Industrial Computer Control Engineering of Hebei Province Hebei Province Qinhuangdao066004 China

ISBN: (纸本)9798350315196

In this paper, we integrate wireless powered communication networks (WPCNs) with cognitive radio networks (CRNs) in order to develop wireless powered CRNs (WP-CRNs). The hybrid access point (HAP) is used to broadcast radio-frequency signals to the devices at the downlink stage, and to receive information from these devices. The HAP has two antennas to achieve full-duplex: one is for downlink wireless power transfer and the other is for receiving uplink information transmission from the users simultaneously in the uplink. In the underlay paradigms. The SUs can harvest energy to transmit data when the interference to primary users (PUs) does not exceed a tolerable threshold. To tackle the above network issues, an optimal resource allocation method is proposed to maximize the secondary system uplink throughput. The optimization problem with respect to time and power allocation is formulated including the constraint of energy causality and interference level. The non-convex problem is transformed into an equivalent convex optimization problem, then the joint optimal power and time allocation (JOPTA) algorithm is presented to determine the optimal solutions. The effectiveness of the proposed algorithm is verified in the simulations. © 2023 IEEE.

关键词： Cognitive radio

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Maintenance of High Fuzzy Utility Itemsets Using the Pre-Large-Itemset Concept and Tree Structure 23

Maintenance of High Fuzzy Utility Itemsets Using the Pre-Lar...

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10th Multidisciplinary International Social Networks Conference, MISNC 2023

作者： Hong, Tzung-Pei Hung, Wei-Teng Huang, Wei-Ming Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Library and Information Center National University of Kaohsiung Taiwan Department of Electrical and Control China Steel Inc. China

ISBN: (纸本)9798400708176

Utility mining has recently attracted much attention in real-world applications because it fits actual situations. Fuzzy utility mining approaches can discover important high-utility patterns in linguistic terms. Data, however, usually come intermittently, and users may want to know currently updated mining results instead of only the ones at the previous stage. Thus, maintaining correct knowledge is very important to this requirement. In the past, we used the fast-updated (FUP) approach and tree structures to manage the maintenance problem of fuzzy utility mining for incremental databases. In this work, we adopt the pre-large-itemset concept to speed up the tree-based maintenance of high fuzzy utility itemsets. The proposed method uses the header table and the pre-large threshold to reduce database scans for efficiency improvement. From the experimental results, the proposed approach performs better than the FUP-tree-based and batching method. © 2023 ACM.

关键词： Maintenance

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Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay 5

Bayesian Optimization of Double-SAC Robotic Arm Path Plannin...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Yuan, Shuai Wen, Ke Zhang, Feng Xiong, Jianqiang Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center School of Computer Science and Engineering Shengyang110168 China Shenyang Jianzhu University School of Electrical and Control Engineering Shengyang110168 China

ISBN: (纸本)9798350307320

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance. © 2023 IEEE.

关键词： Motion planning

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computationally Efficient Implementation of the Weighted Kalman Filter for Quadratic Systems

Computationally Efficient Implementation of the Weighted Kal...

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American control Conference (ACC)

作者： Damiano Rotondo Marcin Witczak Lothar Seybold Department of Electrical and Computer Engineering (IDE) University of Stavanger Norway Institute of Control and Computation Engineering University of Zielona Gora Poland RAFI GmbH & Co. KG Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The weighted Kalman filter (WKF) can be per- ceived as a variant of the extended Kalman filter (EKF), which incorporates the so-called weighted linearisation. The price behind such an extension is expressed by an increased computational complexity, which aims at improving the overall convergence of the filter. Owing to the increased complexity, the WKF is in general not suitable for high-order nonlinear systems. To overcome this difficulty, the objective of this paper is to show that a closed-form expression of the WKF can be obtained for a specific class of nonlinear systems, i.e., those characterised by a quadratic state equation. This closed-form of the WKF scales well with the order of the system, enabling the application of the WKF to high-order nonlinear quadratic systems, as exemplified in the final part of the paper using a comprehensive Monte Carlo analysis applied to a large number of random systems.

关键词： Closed-form solutions Monte Carlo methods Simulation Integral equations control systems Mathematical models computational efficiency

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An Overview of Finite/Fixed-Time control and Its Application in engineering Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2106-2120页

作者： Yang Liu Hongyi Li Zongyu Zuo Xiaodi Li Renquan Lu IEEE College of Automation and Electronics Engineering Qingdao University of Science and TechnologyQingdao 266061 School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Automation Guangdong University of Technologyand also with the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative ControlGuangzhou 510006China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China School of Mathematics and Statistics Shandong Normal UniversityBeijing 100083China Center for Control and Engineering Computation Shandong Normal UniversityJinan 250014China

The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability,the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial *** work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.

关键词： Adding a power integrator finite/fixed-time control and application homogeneous theory sliding mode control

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

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Asian Journal of control 2022年第1期24卷 388-400页

作者： Moaveni, Bijan Rashidi Fathabadi, Fatemeh Molavi, Ali Center of excellence for Modelling and Control of Complex Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Western Michigan University MI United States Institute of Robotics and Industrial Informatics Polytechnical University of Catalonia Barcelona Spain

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive. © 2020 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Traction motors

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Non-euclidean monotone operator theory and applications

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 14872-14904页

作者： Alexander Davydov Saber Jafarpour Anton V. Proskurnikov Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa Barbara CA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO Department of Electronics and Telecommunications Politecnico di Torino Turin Italy

While monotone operator theory is often studied on Hilbert spaces, many interesting problems in machine learning and optimization arise naturally in finite-dimensional vector spaces endowed with non-Euclidean norms, such as diagonally-weighted ℓ1 or ℓ1 norms. This paper provides a natural generalization of monotone operator theory to finitedimensional non-Euclidean spaces. The key tools are weak pairings and logarithmic norms. We show that the resolvent and reected resolvent operators of non-Euclidean monotone mappings exhibit similar properties to their counterparts in Hilbert spaces. Furthermore, classical iterative methods and splitting methods for finding zeros of monotone operators are shown to converge in the non-Euclidean case. We apply our theory to equilibrium computation and Lipschitz constant estimation of recurrent neural networks, obtaining novel iterations and tighter upper bounds via forward-backward splitting.

关键词： non-Euclidean norms monotone operator theory fixed point equations nonexpansive maps

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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