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Neural-Network-Based Recursive State Estimation for Nonlinear Networked systems With Binary-Encoding Mechanisms

IEEE Transactions on Neural Networks and Learning systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

arXiv

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arXiv 2023年

作者： Wang, Siqi Li, Shaoyuan Yin, Li Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Institute of Systems Engineering Macau University of Science and Technology Taipa China

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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MFANet: Mixed Feature Attention Network for Port Lane Detection

MFANet: Mixed Feature Attention Network for Port Lane Detect...

引用

Chinese control Conference (CCC)

作者： Jinwei Zhang Jinya Su Dewei Yi Jun Yang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education and the School of Automation Southeast University Nanjing China Department of Computing Science University of Aberdeen Aberdeen U.K.

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario.

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Distance-based Multiple Non-cooperative Ground Target Encirclement for Complex Environments

arXiv

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arXiv 2024年

作者： Liu, Fen Yuan, Shenghai Cao, Kun Meng, Wei Xie, Lihua School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200000 China Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou510006 China

This paper proposes a comprehensive strategy for complex multi-target-multi-drone encirclement in an obstacle-rich and GPS-denied environment, motivated by practical scenarios such as pursuing vehicles or humans in urban canyons. The drones have omnidirectional range sensors that can robustly detect ground targets and obtain noisy relative distances. After each drone task is assigned, a novel distance-based target state estimator (DTSE) is proposed by estimating the measurement output noise variance and utilizing the Kalman filter. By integrating anti-synchronization techniques and pseudo-force functions, an acceleration controller enables two tasking drones to cooperatively encircle a target from opposing positions while navigating obstacles. The algorithm’s effectiveness for the discrete-time double-integrator system is established theoretically, particularly regarding observability. Moreover, the versatility of the algorithm is showcased in aerial-to-ground scenarios, supported by compelling simulation results. Experimental validation demonstrates the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Drones

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Accurate Identification and Localization of Transmission Lines under Complex Lighting for Distribution Network Power Supply Operation Robots

Accurate Identification and Localization of Transmission Lin...

引用

2024 China automation Congress, CAC 2024

作者： Liu, Tianhe Chen, Xin Hu, Chenyu Jian, Xu Li, Shengzu Wang, Gang 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 Equipment Technology Center State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan430077 China Distribution Department State Grid Hubei Electric Power Co. Ltd. Wuhan Power Supply Company 430070 China

ISBN: (纸本)9798350368604

Accurate identification and position of transmission lines is critical for robots to safely and effectively perform distribution network operations. However, such tasks are often challenged by complex lighting conditions, where depth information can be easily lost. Transmission lines have limited texture features and reflective surfaces, making it difficult to match image feature points. To address these challenges, this paper proposes a visual perception system for weak texture targets under complex lighting, based on an "eye-on-hand" configuration and a depth complementation algorithm. Furthermore, a high-precision identification and localization method for transmission line grasping points is developed, utilizing the fusion of binocular visual color maps and depth maps. The proposed method has been shown to effectively solve the problem of identifying and localizing the grasping points of transmission lines under complex lighting conditions. Experiments demonstrated that the method can improve the localization accuracy to within ±4mm, outperforming other algorithms. The method is characterized as simple and efficient, and it enhances the environmental adaptability of distribution network robots. © 2024 IEEE.

关键词： Binocular vision

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A Swarm Model for Target Capturing in a Polygonal Strip

A Swarm Model for Target Capturing in a Polygonal Strip

引用

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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Formation control of Discrete-time Multi-agent systems Based on Distributed Filter Observer

Formation Control of Discrete-time Multi-agent Systems Based...

引用

Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Huanli Gao Wei Li Ming Qiu He Cai School of Automation Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou China Department of Shanhaiguan District People’s Armed Forces Qinhuangdao City China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper explores the formation control of discrete multi-agent systems under external communication disturbance. Firstly, leveraging the modified algebraic Riccati equation, a disturbed output based discrete filter observer is proposed to minimize network information exchange and mitigate disturbance effects. Then, the formation control issue is transformed into a cooperative output regulation problem, which a certainty equivalent state feedback controller solves. The effectiveness of the control strategy is demonstrated by numerical simulations.

关键词： State feedback Riccati equations Observers Information filters Numerical simulation Filtering theory Formation control

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AirVista: Empowering UAVs with 3D Spatial Reasoning Abilities Through a Multimodal Large Language Model Agent

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilitie...

引用

International Conference on Intelligent Transportation

作者： Fei Lin Yonglin Tian Yunzhe Wang Tengchao Zhang Xinyuan Zhang Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Capital University of Economics and Business Beijing China Department of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the Artificial systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks.

关键词： Three-dimensional displays Large language models Urban areas Land transportation Autonomous aerial vehicles Cognition Space exploration Planning

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A Collaboration Evaluation Method for Substation Protection and Monitoring System Based on G1 Method and Expert Investigation 3

A Collaboration Evaluation Method for Substation Protection ...

引用

3rd International Conference on New Energy and Power engineering, ICNEPE 2023

作者： Yan, Yuxiang Deng, Xiongyao Yu, Xinyang Ma, Shizhong Xue, Ancheng Chang, Naichao Wang, Shenqiang Zhang, Long Li, Zhongqing Meng, Jiangwen Fan, Chen Teng, Jingyu Zheng, Jingrong North China Electric Power University Beijing China National Electric Power Dispatching and Control Center of State Grid Corporation of China Beijing China Beijing Sifang Engineering Co. Ltd. Beijing China China Electric Power Research Institute Beijing China China Electric Power Research Institute Power Automation Department Nanjing China Nari Technology Nanjing Control Systems Co. Ltd. Nanjing China

ISBN: (纸本)9798350393224

The substation is the core facility of the power system. The interaction and coordination of different equipment in the protection and monitoring system(PMS) directly affects the working efficiency and stable operation of the system. However, the current protection and monitoring system of substation(SPMS) generally lacks evaluation method for collaboration. To the above problems, this paper proposes an evaluation method for collaboration in S-PMS based on the G1 method. Firstly, according to the requirements of the coordination for PMS, the corresponding indicators are proposed to form the index system for evaluating the coordination. Then, combined with G1 method, a comprehensive collaboration evaluation model is constructed. Finally, the coordination of two different structures of substations is evaluated and analyzed, and the feasibility of the evaluation method is verified. © 2023 IEEE.

关键词： Collaboration evaluation G1 method Protection and monitoring system

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Rapid dynamical pattern recognition for sampling sequences

引用

Science China(Information Sciences) 2021年第3期64卷 118-135页

作者： Weiming WU Qian WANG Chengzhi YUAN Cong WANG School of Automation Science and Engineering South China University of Technology School of Control Science and Engineering Shandong University Department of Mechanical Industrial and Systems Engineering University of Rhode Island

In this paper, based on deterministic learning, we propose a method for rapid recognition of dynamical patterns consisting of sampling sequences. First, for the sequences yielded by sampling a periodic or recurrent trajectory(a dynamical pattern) generated from a nonlinear dynamical system, a sampled-data deterministic learning algorithm is employed for modeling/identification of inherent system dynamics. Second,a definition is formulated to characterize similarities between sampling sequences(dynamical patterns) based on differences in the system dynamics. Third, by constructing a set of discrete-time dynamical estimators based on the learned knowledge, similarities between the test and training patterns are measured by using the average L1 norms of synchronization errors, and general conditions for accurate and rapid recognition of dynamical patterns are given in a sampled-data framework. Finally, numerical examples are discussed to illustrate the effectiveness of the proposed method. We demonstrate that not only a test pattern can be rapidly recognized corresponding to a similar training pattern, but also the proposed recognition conditions can be verified step by step based on historical sampling data. This makes a distinction compared with the previous work on rapid dynamical pattern recognition for continuous-time nonlinear systems, in which the recognition conditions are difficult to be verified by using continuous-time signals.

关键词： deterministic learning dynamical pattern recognition sampling sequence synchronization

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