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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics 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 Shanghai200240 China

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC. © 2023, CC BY-NC-SA.

关键词： Model predictive control

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BEARING-BASED TARGET ENTRAPPING control OF MULTIPLE UNCERTAIN AGENTS WITH ARBITRARY MANEUVERS

arXiv

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arXiv 2023年

作者： Su, Haifan Yang, Ziwen Zhu, Shanying Chen, Cailian Yu, Wenbin Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

This paper is concerned with bearing-based cooperative target entrapping control of multiple uncertain agents with arbitrary maneuvers including shape deformation, rotations, scalings, etc. A leader-follower structure is used, where the leaders move with the predesigned trajectories, and the followers are steered by an estimation-based control method, integrating a distance estimator using bearing measurements and a stress matrix-based formation controller. The signum functions are used to compensate for the uncertainties so that the agents’ accelerations can be piecewise continuous and bounded to track the desired dynamics. With proper design of the leaders’ trajectories and a geometric configuration, an affine matrix is determined so that the persistently exciting conditions of the inter-agent relative bearings can be satisfied since the bearing rates are related to different weighted combinations of the affine matrix vectors. The asymptotic convergence of the estimation error and control error is proved using Filipov properties and cascaded system theories. A sufficient condition for inter-agent collision avoidance is also proposed. Finally, simulation results are given to validate the effectiveness of the method in both 2D and 3D cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Acceleration

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Exact Noise-Robust Distributed Gradient-Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Exact Noise-Robust Distributed Gradient-Tracking Algorithm f...

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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource allocation method with good robustness is urgently needed under complex industrial environments. This paper considers the distributed constraint- coupled resource allocation problem with noisy information exchange over an undirected network, where each agent holds a private cost function and obtains the solution via only local communications. Communication noise poses a challenge to gradient-tracking based algorithm as the impact of noise will accumulate and its variance tends to infinity when the noise is persistent. Adopting noise-tracing scheme, we propose an exact noise-robust distributed gradient-tracking algorithm to achieve cost-optimal distribution of resources, which can avoid noise-accumulation in the tracking step. Moreover, noise suppression parameters are introduced to further attenuate the impact of noise. With diminishing suppression parameters, it is theoretically proved that the proposed algorithm is able to achieve exact convergence to the optimal solution. Finally, a numerical example is provided for verification.

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Design and Experimental Evaluation of Nighttime Traffic-Sign Detection and Classification Based on Low-Light Enhancement 6

Design and Experimental Evaluation of Nighttime Traffic-Sign...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Zhou, Shaodong Wang, Huijie Nie, Chang Zhang, Hui Sun, Zhifeng Electricity Facilities Guangri Guangzhou Co. Ltd. Guangzhou China School of Transportation Science and Engineering Beihang University Beijing China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

This paper focuses on solving the problem of traffic-sign detection and classification at nighttime when lighting is often lower. Images taken by the camera at night are underexposed and fail to show details. Low-light enhancement method can improve the brightness of the image. By studying four different low-light enhancement methods and applying four methods to the detection and classification of traffic signs, the performance of low-light image enhancement is compared. The evaluation results show that the nighttime traffic-sign detection and classification method, which combines a robust end-to-end convolutional neural network (CNN) and Zero-Reference Deep Curve Estimation (Zero-DCE) low-light enhancement, has high precision and a higher recall rate compared to other methods. © 2022 IEEE.

关键词： Image enhancement

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent systems

作者： Ting Cheng Shuai Liu Qimin Xu Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources.

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Multi-objective Group Decision and Cooperative Planning Based on Preference and Fuzzy Measure 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Ma, Xiwen Xie, Wei Yang, Jingsong Zhang, Weidong Lu, Jinhui School of Oceanography Shanghai Jiao Tong University Shanghai China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China Zhuhai China School of Information and Communication Engineering Hainan University Hainan Haikou China National Engineering Research Center of Ship and Shipping Control System Shanghai China Crossocean of Suzhou technology No. 218 East Oingdao Road Suzhou China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

To pursue interest balance and improve collaboration efficiency, this paper proposes a multi-agent cooperation method integrating decision-making and planning. Based on preference and fuzzy measures, this method can balance individual and group benefits under fuzzy uncertain environment. The equilibrium solution of a multi-agent multi-objective game, namely group decision-making (GDM) solution, can be obtained by the Gaussian oscillation particle swarm optimization (GOPSO) algorithm, which overcomes the dilemma of a multi-objective group game and improves the computational efficiency. Considering the smoothness and continuity of the trajectory, a heuristic motion planning (MP) generation model that satisfies the kinematic constraints and minimizes differential thrust is proposed, which improves the cooperation efficiency. To verify the performance of this proposed method, simulation results are presented and some comparative statements are given. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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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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Simulation Research on Generalized Proportional Integral Observer Based Active Disturbance Rejection control for Coarse Pointing Assembly

Simulation Research on Generalized Proportional Integral Obs...

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2024 8th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Jijun Ma Yulong Guo YuNing Wang Yongkang Shi Beijing Research Institute of Telemetry Beijing China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Research Institute of Telemetry Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

The coarse pointing assembly (CPA), as the outer loop of the laser terminal, its tracking stability is the basis for ensuring laser communication. This paper presents the model of the CPA. Aiming at the disturbance factors in the low-speed operation process of the CPA, an active disturbance rejection control (ADRC) method for the position loop of the CPA is designed based on the generalized proportional integral observer (GPIO) to achieve disturbance compensation control. Finally, simulations are carried out on the system model. The simulation results show that compared with the PID control strategy adopted, the proposed ADRC strategy based on GPIO of the CPA can effectively improve the tracking accuracy.

关键词： Robust control Tracking loops Accuracy Simulation Computational modeling Laser stability Observers Laser modes Stability analysis Assembly

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DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation

arXiv

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arXiv 2024年

作者： Zhang, Huixin Wang, Guangming Wu, Xinrui Xu, Chenfeng Ding, Mingyu Tomizuka, Masayoshi Zhan, Wei Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom UC Berkeley BerkeleyCA94720 United States

This paper introduces a 3D point cloud sequence learning model based on inconsistent spatio-temporal propagation for LiDAR odometry, termed DSLO. It consists of a pyramid structure with a spatial information reuse strategy, a sequential pose initialization module, a gated hierarchical pose refinement module, and a temporal feature propagation module. First, spatial features are encoded using a point feature pyramid, with features reused in successive pose estimations to reduce computational overhead. Second, a sequential pose initialization method is introduced, leveraging the high-frequency sampling characteristic of LiDAR to initialize the LiDAR pose. Then, a gated hierarchical pose refinement mechanism refines poses from coarse to fine by selectively retaining or discarding motion information from different layers based on gate estimations. Finally, temporal feature propagation is proposed to incorporate the historical motion information from point cloud sequences, and address the spatial inconsistency issue when transmitting motion information embedded in point clouds between frames. Experimental results on the KITTI odometry dataset and Argoverse dataset demonstrate that DSLO outperforms state-of-the-art methods, achieving at least a 15.67% improvement on RTE and a 12.64% improvement on RRE, while also achieving a 34.69% reduction in runtime compared to baseline methods. Our implementation will be available at https://***/IRMVLab/DSLO. © 2024, CC BY-NC-ND.

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Dynamic Event-Triggered Consensus on Matrix-weighted Networks:Leadless Case

Dynamic Event-Triggered Consensus on Matrix-weighted Network...

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第42届中国控制会议

作者： Lintao Dou Haoyu Wei Jian Huang Lulu Pan Haibin Shao Dewei Li the College of Intelligent Science National University of Defense Technology the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of Chinaand Shanghai Engineering Research Center of Intelligent Control and Management

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks,where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the ***,a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks,in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold,which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno *** event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks,***,the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks,generalizing those results only applicable for scalar-weighted *** proposed approach allows each agent to broadcast and receive information only at its triggering ***,simulation examples are provided to demonstrate the theoretical results.

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