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Leader-Following Entrapping control of AUVs Using Bearing Measurements in Local Coordinate Frames

Guidance, Navigation and control 2022年第4期2卷 85-121页

作者： Yingbo Fu Ziwen Yang Shanying Zhu Cailian Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240P.R.China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240P.R.China Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240P.R.China

In this paper,the entrapping control problem of discrete-time AUVs with local coordinate frames is *** achieve entrapment in arbitrarily shaped orbits and formations,we design a bearingbased leader-following framework fully in the discrete-time domain with four parts:the orientation estimation unit,estimator unit,controller unit and parameters tuning *** bearing measurements and communication information,the orientation estimation unit can estimate orientations of local coordinate frames infinite time,and the estimator unit can achieve local *** on estimation,the controller unit can drive each AUV to track the desired orbit or formation with an arbitrary *** present su±cient conditions under which stability of the overall system is proved using the theorem offinite-time stability and LaSalle's theorem for the discrete-time ***,the parameters tuning unit can calculate optimal parameters to improve overall ***,we extend our schemes to nonholonomic AUVs with unicycle ***,simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Entrapping control discrete-time AUVs bearing measurements local coordinate frames

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Non-convex Optimization Based On Deep Koopman Operator

Non-convex Optimization Based On Deep Koopman Operator

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Jun Guo Yubin Jia Panxiao Yong Jun Zhou Zhimin Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing China

Non-convex optimization problems are prevalent in various fields, and finding the global optimal solution for such problems remains challenging. This paper proposes an optimization method based on deep Koopman operator, which leverages the power of deep neural networks to provide a data-driven architecture for the Koopman function. The Koopman operator is used to linearly represent non-convex problems, and the proposed method utilizes model predictive control to optimize the resulting linear systems. Simulation results show that this method can solve the non-convex optimization problem effectively, quickly and accurately, while obtaining the global optimal solution.

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Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Vulnerability of EV Charging Stations to Cyber Attacks Manip...

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Future Energy Solutions (FES), International Conference on

作者： Amirhossein Akbarian Mahdi Bahrami Mehdi Vakilian Matti Lehtonen Electrical Engineering Department Center of Excellence in Power System Management and Control Sharif University of Technology Tehran Iran Department of Electrical Engineering and Automation Aalto University Espoo Finland

Electric vehicles (EVs) have experienced an unprecedented increase in their penetration; in well developed countries; due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers’ perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users’ response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account.

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Dynamics of coupled implicit and explicit opinions

arXiv

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arXiv 2023年

作者： Zhang, Qi Wang, Lin Wang, Xiaofan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

This paper proposes a dual opinions co-evolution model based on the dual attitudes theory in social psychology, where every individual has dual opinions of an object: implicit and explicit opinions. The implicit opinions are individuals’ inner evaluations affected by the experience, while the explicit opinions are external expressions of the evaluations and are influenced by public opinion. We theoretically analyze the dynamics of coupled dual opinions in an adversarial discussion scenario, in which two opposing groups participate in the discussion. We rigorously give a method to grade the degree of individual bias. When individuals have a slight bias, dual opinions reach a consensus, showing the phenomenon of acceptance. When individuals are moderately or even extremely biased, they will exhibit the phenomenon of outward compliance, that is, their publicly explicit opinions conform to public opinion, while implicit opinions will reach a persistent disagreement and even polarization. Our investigation indicates that dual opinions may not be monotone initially but eventually converge monotonically. Besides, we analyze the influence of parameters on the co-evolutionary process and results. Numerical simulations of the dual opinions formation process produce inward and outward conformity phenomena, namely acceptance and compliance. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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MA-RRT* Algorithm for Path Planning with Maximum Rotation Angle Constraint and Adaptive Crossing Threat Zone

MA-RRT* Algorithm for Path Planning with Maximum Rotation An...

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第42届中国控制会议

作者： Jiong Luo Yong Heng Qi Li Yongzhao Hua Xiwang Dong Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Beijing Institute of Electronic System Engineering Institute of Artificial Intelligence Beihang University

With the gradual diversification of modern application scenes,the demand for UAV path planning is becoming more and more *** paper first introduces a possible battle scenario,and the problems that must be considered in path planning in this scene are analyzed:the maximum rotation angle constraint and the demand that the UAV can cross the threat zone *** rotation angle and Adaptive crossing threat zone RRT*(MA-RRT*) algorithm is proposed to solve the above *** algorithm improves the reconnection process of neighbor set in RRT*,and adds the maximum rotation angle constraint to the RRT* framework,so that the generated path strictly meets this *** addition,the above process also reduces the time consumption of RRT*,and the speed advantage of MA-RRT*becomes more and more obvious with the increase of iteration ***,the obstacle information is regarded as one of the components of the node cost of the path,and the original obstacle is regarded as a soft *** enables the path to adaptively cross the threat zone,which is more in line with the actual *** compare our algorithm with RRT* in numerical simulation,which verifies the effectiveness and time consumption advantages of the algorithm.

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Improved Auction Algorithm for Weapon Target Assignment with Firepower-Health Model

Improved Auction Algorithm for Weapon Target Assignment with...

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第42届中国控制会议

作者： Shuobo Wang Yong Heng Yongzhao Hua Xiwang Dong Zhang Ren Institute of Artificial Intelligence Beihang University Beijing Institute of Electronic System Engineering School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University

This paper proposes an improved auction algorithm to optimize the efficiency of dynamic weapon target assignment(WTA) ***,the dynamic battlefield environment of firepower-health model is analyzed and the constraints of weapon assignment problem are *** firepower-health model is about the idea of adding constraints of weapon firepower and target health to promote the result of assignment more ***,in order to improve the computing speed of WTA algorithm,the penalty mechanism is introduced in the compensation auction *** main idea of improved algorithm is to reduce the extra consuming of the weapon during the assignment,from which penalty mechanism can optimize the assignment results and reduce the number of algorithm iterations by punishing the overkilled target under certain ***,the simulation results based on the dynamic firepower-health model show that the proposed algorithm can meet the requirements of firepower allocation,and can promote the effectiveness of assignment with high real-time response.

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ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments

arXiv

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arXiv 2025年

作者： Xin, Jinghao Liang, Zhichao Zhang, Zihuan Wang, Peng Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments. To address these challenges, this study proposes the ColorDynamic framework. First, an end-to-end DRL formulation is established, which maps raw sensor data directly to control commands, thereby ensuring compatibility with unstructured environments. Under this formulation, a novel network, Transqer, is introduced. The Transqer enables online DRL learning from temporal transitions, substantially enhancing decision-making in dynamic scenarios. To facilitate scalable training of Transqer with diverse data, an efficient simulation platform E-Sparrow, along with a data augmentation technique leveraging symmetric invariance, are developed. Comparative evaluations against state-of-the-art methods, alongside assessments of generalizability, scalability, and real-time performance, were conducted to validate the effectiveness of ColorDynamic. Results indicate that our approach achieves a success rate exceeding 90% while exhibiting real-time capacity (1.2–1.3 ms per planning). Additionally, ablation studies were performed to corroborate the contributions of individual components. Building on this, the OkayPlan-ColorDynamic (OPCD) navigation system is presented, with simulated and real-world experiments demonstrating its superiority and applicability in complex scenarios. The codebase and experimental demonstrations have been open-sourced on our website1 to facilitate reproducibility and further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Designing Cluster Consensus on Higher-Order Interaction Networks

Designing Cluster Consensus on Higher-Order Interaction Netw...

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IEEE Conference on Decision and control

作者： Haoyu Wei Lulu Pan Haibin Shao Dewei Li Wenbin Yu Shibei Xue Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

This paper examines the cluster consensus design problem on higher-order interaction networks. Specifically, the higher-order interaction mechanism is captured by matrix-weighted networks that allow the interdependency across the dimensions of the agents' states, and the matrix-valued weight matrices $A_{ij}\in \mathbb{R}^{d\times d}$ associated with specific edges are further assumed to share the same nullspace for design purposes. Under mild assumptions on network connectivity, we first examine the case that the nullspace of positive semi-definite edges is spanned by a nonzero vector $\boldsymbol{\xi}\in \mathbb{R}^{d}$ and show that the predictable cluster consensus can be achieved, which is eventually located in the 1-dimensional linear space determined by span $\{\boldsymbol{\xi}\}$ and the average of agents' initial states. Moreover, the transient state of agents in each cluster can also be explicitly characterized. Namely, the derivative of the average state of agents in each cluster is perpendicular to span $\{\boldsymbol{\xi}\}$ . To generalize the above results, we proceed to examine the case that the nullspace of positive semi-definite edges is spanned by more than one linearly independent $d$ -dimensional vector, in which case, analogous results can be obtained, and the explicit geometric interpretation is also provided.

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Construction of Minecraft Virtual Reality Scene Based on Voxel Point Cloud 8th

Construction of Minecraft Virtual Reality Scene Based on Vox...

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8th International Conference on Cognitive systems and Information Processing, ICCSIP 2023

作者： Ji, Nuocheng Zhuang, Hanyang Yang, Ming The University of Michigan – Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9789819980208

Virtual Reality is an important development direction in recent years. Minecraft, a popular sandbox game, has a great potential to become a virtual reality platform for building immersive environments. Traditional methods of building virtual reality scenes rely heavily on massive human labors and require high levels of proficiency. To address these limitations, we propose an automatic, voxelated 3D modeling method with foreground and background environment segmented that can reduce modeling costs and achieve effective application in Minecraft virtual reality scenes such as virtual campuses. The proposed method leverages the use of point cloud reconstruction using a voxelization approach. A pipeline of converting point cloud to Minecraft scene is developed. Through preprocessing, segmenting, coloring, and exporting, a Minecraft virtual reality scene can be automatically generated based on voxel point cloud. Due to the noise in point cloud may cause defect during voxelization, a denoising and smoothing method using slice scanning and voxel connectivity segmentation has been implemented. It can generate building structures with clear outlines and uniform colors. And the entire process requires low computational complexity and achieves fast generation. We conducted experiments based on this method in indoor scenes, outdoor building landmarks, and complex underground scenes, and obtained results with clear structures and smooth surfaces, saving a lot of time for subsequent detailed modeling. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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Distributed Neighbor Selection on Matrix-weighted Networks

Distributed Neighbor Selection on Matrix-weighted Networks

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Haoyu Wei Lulu Pan Haibin Shao Dewei Li Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

This paper explores distributed neighbor selection in matrix-weighted networks, where a matrix-valued weight captures the interdependencies among agents’ states. We demonstrate the feasibility of preserving and, in certain cases, improving the convergence rate of matrix-weighted networks by having agents exclusively engage with a subset of their neighbors. Leveraging the inherent monotonicity of Laplacian matrix eigenvectors, we introduce a novel neighbor selection criterion that reduces network connections while enhancing convergence rates. Existing neighbor selection strategies often require acquiring information from all nodes before making decisions. In order to propose a distributed neighbor selection strategy for matrix-weighted networks, we establish a numerical relationship between Laplacian eigenvectors and a locally measurable metric. In essence, this paper extends prior findings from scalar-weighted networks to matrix-weighted networks.

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