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Distributed MPC for Reconfigurable Architecture systems via Alternating Direction Method of Multipliers

IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1336-1344页

作者： Ting Bai Shaoyuan Li Yuanyuan Zou the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China

This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different *** equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is *** this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration ***,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control ***,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.

关键词： Alternating direction method of multipliers(ADMM)algorithm distributed control model predictive control(MPC) reconfigurable architecture systems.

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De-Redundant Biomedical Feature Extraction Based On Improved MultiSURF

De-Redundant Biomedical Feature Extraction Based On Improved...

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2023 China automation Congress, CAC 2023

作者： Ma, Ruixiang Zhang, Bin Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350303759

Biomedical data contain valuable information which contributes to the prediction of clinical disease risk. However, the presence of redundant and irrelevant features will diminish prediction accuracy and augment model complexity. According to our research, MultiSURF is a famous feature selection method but falls short in eliminating redundant features. To address this constraint, this paper introduced an improved MultiSURF method known as EN-MultiSURF. The proposed method integrated the strengths of Elastic Net and MultiSURF, which is capable of detecting similarity between features as well as eliminating redundant and irrelevant features. The proposed method was compared with Relief, MultiSURF, Elastic Net, Random Forest and Decision Tree on one binary along with four multiclass numerical datasets. The experimental results substantiated that our method surpassed other methods and exhibited a higher degree of consistency with medical prior knowledge, which established itself as a valuable tool for biomedical data mining. © 2023 IEEE.

关键词： Feature Selection

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Network system capacity:Towards integrating sensing,communication and control

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National Science Open 2024年第1期3卷 147-149页

作者： Xinping Guan Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

Network systems refer to a new generation of systems with integrated information perception,transmission and utilization capabilities through communication networks,which are adopted to achieve desirable objectives under physical and information related uncertainties and/or adversary *** the information-rich era[1,2],one of the fundamental issues is to exploit the limit of feedback control on dissipating such uncertainties in the scenarios of networked sensing and communication[3].

关键词： communication integrating system

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Learning-Based Defense Against Prediction-Based Displacement Attacks on Formation control

Learning-Based Defense Against Prediction-Based Displacement...

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2024 China automation Congress, CAC 2024

作者： Chen, Bingtong Li, Yushan Wu, Shukun Fang, Chongrong He, Jianping Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Student Innovation Center Shanghai Jiao Tong University China

ISBN: (纸本)9798350368604

Formation control is a widely performed task in multi-agent systems. However, agents under formation control are susceptible to attacks, and how to defend against the attack is important for system security. This paper aims to design a new method to protect the system from prediction-based displacement attack, which predicts the movement of agents, diverts them from their original tracks and drives them to a preset trap. The defense is challenging due to the robustness of the attacker’s prediction mechanism. Also, undermining the attack can be costly and harm the system’s performance. To address this issue, we first build obstacle-avoidance and formation control models of the holonomic and non-holonomic agents and specify the prediction-based attacks to limit the cases where the defense strategy can be effective. Then, we propose a learning-based defense strategy, which learns the prediction mechanism of the attackers. It modifies the control input based on self-adaptive fuzzy methods to reduce prediction accuracy and guarantee that the agents can reach the goal. Simulation results demonstrate that our method is better than the previous noise-based one at reducing the accuracy of the attacker’s prediction. © 2024 IEEE.

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Traffic Pricing Based on Network Congestion Games with Player and Resource Failures 43

Traffic Pricing Based on Network Congestion Games with Playe...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Shuqi Wang, Jinhuan Wang, Lin Hebei University of Technology Institute of Mathematics School of Science Tianjin300401 China Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581581

This paper investigates the pricing problem of transportation based on a framework of network congestion game with both player and resource failures. Firstly, the traffic pricing problem is modeled as a network congestion game by redefining the player's cost function in the event of player and resource failures. Secondly, by using the method of semi-tensor product of matrices, the game with player and resource failures is algebraized and verified to be a weighted potential game. The weighted potential function is also obtained. Then, the impact of player and resource failures on Nash equilibria (NEs) is studied, and the condition on keeping the NE of the original game unchanged is discussed. Finally, the theoretical results are verified by numerical examples. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Tensors

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Towards Disturbance Rejection in Feature Pyramid Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 946-958页

作者： Hu, Xiaobo Xu, Wei Gan, Yi Su, Jianbo Zhang, Jun Shanghai Jiao Tong University Joint Institute of UM-SJTU Shanghai200240 China Shanghai Jiao Tong University Research Center of Intelligent Robotics Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

The multiscale problem is one of the challenging topics in the object detection field. Among the current studies toward this problem, feature pyramid network (FPN) has been shown a superior performance. The core principle of FPN is a mapping for the detectable objects into hierarchy features. These features are designed with different size of receptive fields, which brings good scale invariance in the classification and localization subtasks. However, a disturbance of information dilution is generated by the assignment for the detectable objects into different features. This dilution disturbance is associated with the convolution or pooling operation during the hierarchy feature extraction process. Both of the imprecise localization information in the top features and the fuzzy classification information in the bottom ones limit the performance of FPN. Aiming at the disturbance rejection problem of information dilution, this article presents a feature compensation method. A cross compensation between the semantic information and the localization information in hierarchy features is used to enhance the vanilla FPN. Then, the evaluation metrics in an object detection task are examined. Not only the overall performance, but also the respective metrics for multiscale objects validate a considerable improvement of the FPN with a disturbance compensation mechanism. The experimental results demonstrate an effectiveness of the proposed method in this article. © 2020 IEEE.

关键词： Object detection

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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 388-399页

作者：刘增敏王申涛姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China JD Business Growth Department Beijing 100176China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is ***,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking *** the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking *** simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

关键词： moving unmanned aerial vehicle(UAV)platform small object feature extraction image registration multi-object tracking

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Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 313-328页

作者： Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China IEEE Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology University of CambridgeCambridge CB30FDUnited Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous *** retrieval is an efficient and effective technique in image-based localization *** to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging *** this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image ***,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high *** also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised *** final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM *** experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons *** strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons *** performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time ***,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

关键词： Deep representation learning place recognition visual localization

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Formation control for Unmanned Surface Vehicles Based on Integrative APF and MPC

Formation Control for Unmanned Surface Vehicles Based on Int...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Li, Weilin Wu, Jing Long, Chengnian Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

This paper takes the dual water jet propulsion USV as the research object, and studies formation obstacle avoidance optimization problem. An integrative APF algorithm is proposed to merge path planning and trajectory tracking into one optimization problem, where adaptive searching step is adopted to guarantee that the planned trajectory is reachable. In addition, a distance-triggered codesign of integrative APF and MPC is presented to maintain formation during the obstacle avoidance process. Finally, the results in MATLAB simulation demonstrate the validity and efficiency of the algorithm. © 2022 IEEE.

关键词： MATLAB

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yu, Guanfeng Zhang, Han Wang, Zehao Wang, Jingchuan Shanghai Jiao Tong University Institute of Medical Robotics Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities. © 2024 IEEE.

关键词： Iterative methods

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