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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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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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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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On a novel tracking differentiator design based on iterative learning in a moving window

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control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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High-Value Target Escort and Defense Scenario with Differential Subgame Cost 63

High-Value Target Escort and Defense Scenario with Different...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Judy, Rachael L. Fuchs, Zachariah E. Casbeer, David W. University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States Control Science Center of Excellence Air Force Research Laboratory 2210 8th St. WPAFBOH45433 United States

ISBN: (纸本)9798350316339

An Escort and Defense Scenario is presented in which a high-value Target maneuvers through a high-risk region while being escorted by a mobile, defensive agent. Along this trajectory, an Attacker may be launched from one of several possible launch locations. If a launch occurs, the three agents play out a Target-Attacker-Defender (TAD) differential game in which the Defender attempts to intercept the Attacker at maximal distance from the Target while the Attacker strives to maneuver as close as possible to the Target. Prior to launch, the Target and Defender strive to preposition themselves in advantageous positions to effectively respond to a potential threat while simultaneously moving towards a safety region. An augmented collocation-based direct optimal control method is developed to solve the escort problem by solving and utilizing the value of the TAD differential subgame at each collocation point while simultaneously optimizing the primary optimal control problem. © 2024 IEEE.

关键词： Optimal control systems

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Comparative Techno-Economic-Environmental Analysis of HVDC and HVAC Technologies for an Offshore Wind Farm in China 7

Comparative Techno-Economic-Environmental Analysis of HVDC a...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Liang, Yingqi Wu, Shuangxi Liu, Yang Fu, Dongqiang Lee, Yan Rui Guo, Gengrui Luo, Tianhao Wang, Tianhao National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou510060 China

ISBN: (纸本)9798350345094

DC collection systems offer advantages by reducing the weight and size of DC cables without requiring reactive power compensation. This enables the replacement of the bulky 50/60 Hz transformers typically used in AC collection systems on offshore platforms with smaller medium-frequency transformers in DC collection configurations. Nonetheless, challenges persist in implementing high-power DC-DC converters with high-voltage transformation ratios and DC protection methods in DC collection systems. It is worth noting that while HVDC (High Voltage DC) transmission can transfer offshore wind power from collection systems to onshore grids, DC collection systems do not always result in fewer power conversion stages compared to AC collection systems. To tackle these challenges, this paper conducts a comparative analysis of the technological, economic, and environmental aspects of DC and AC collection systems for offshore wind farms, using a wind farm in China as an illustrative example. Our approach involves an innovative method for estimating losses and a technical comparison. Simulation results validate that DC collection systems exhibit higher total losses than AC collection systems. We also explore the impact of collection bus voltages on these losses in DC systems. Additionally, we develop an economic cost assessment method, and the sensitivity analysis results confirm that cost reductions primarily stem from the reduced size of DC cables and offshore platforms rather than improvements in DC protective devices and DC-DC converters. Lastly, we investigate the environmental implications of these systems. © 2023 IEEE.

关键词： Offshore wind farms

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