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arXiv 2024年

作者： Ming, Chenlin Wang, Zitong Zhang, Boxuan Duan, Xiaoming He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Germany

Humans often learn new skills by imitating the experts and gradually developing their proficiency. In this work, we introduce Stochastic Trajectory Optimization for Demonstration Imitation (STODI), a trajectory optimization framework for robots to imitate the shape of demonstration trajectories with improved dynamic performance. Consistent with the human learning process, demonstration imitation serves as an initial step, while trajectory optimization aims to enhance robot motion performance. By generating random noise and constructing proper cost functions, the STODI effectively explores and exploits generated noisy trajectories while preserving the demonstration shape characteristics. We employ three metrics to measure the similarity of trajectories in both the time and frequency domains to help with demonstration imitation. Theoretical analysis reveals relationships among these metrics, emphasizing the benefits of frequency-domain analysis for specific tasks. Experiments on a 7-DOF robotic arm in the PyBullet simulator validate the efficacy of the STODI framework, showcasing the improved optimization performance and stability compared to previous methods. The source code can be found at ming-bot/STODI. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost functions

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Short-Term Traffic Flow Prediction of Highway Tunnels Based on SSA and Improved TSK Fuzzy system

Short-Term Traffic Flow Prediction of Highway Tunnels Based ...

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2023 China Automation Congress, CAC 2023

作者： Huang, Zhijing Chen, Kang Xu, Zhezhuang Zeng, Qinbo Xu, Zhibin College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Aiming at the problem that the traffic flow has an low prediction accuracy due to varying randomness and high nonlinearity, an improved TSK fuzzy neural network prediction model by singular spectrum analysis is ***, the original traffic flow time series can be decomposed into several subseries by singular spectrum analysis (SSA). Then, the Takagi-Sugeno-Kang (TSK) fuzzy system was trained using simulated annealing genetic algorithm (SAGA) and minibatch gradient descent method (MBGD) combined with three techniques (RDA) of regularization, DropRule and *** prediction model of TSK fuzzy neural network is established to predict each subseries independently, and the forecasted traffic flow is obtained by superimposing the prediction results of each subseries. Finally, the traffic flow dataset of expressway tunnels in a city is used to verify the effectiveness of the model. Experimental results can show high traffic flow prediction accuracy of the model, and the model can accurately predict multiple different highway tunnels. © 2023 IEEE.

关键词： Forecasting

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Cross-Domain Buoy Placement for Underwater Target Tracking in Marine Wireless Networks

Cross-Domain Buoy Placement for Underwater Target Tracking i...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Yichen Li Wenbin Yu Liangyu Jiang Buyiyi Wang Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Underwater target tracking can continuously provide position estimates of the interested target. It is the basis for marine applications. Usually, a proper deployment topology of the available devices could guarantee that the optimal tracking performance can be obtained. This paper investigates the optimal placement problem of buoys under a cross-domain tracking framework, where the buoys are deployed with unmanned aerial vehicles (UAVs) to perform coarse tracking and provide prior information for the subsequent arriving underwater vehicles. By taking advantage of the high-speed movement of UAVs, the disadvantage of underwater tracking (difficult to quickly form an effective tracking formation when the target is first found) is made up. Specifically, the placement is formulated as a convex optimization problem based on the idea of sensor selection, and the output makes the buoys and pre-deployed anchors form a combination that optimizes the tracking accuracy while guaranteeing the detection probability. Moreover, the target mobility and deployment convenience are also considered to favor practical uses. Through various simulation results, the advantages of the proposed method are illustrated.

关键词： Performance evaluation Target tracking Wireless networks Simulation Design methodology Convex functions Underwater tracking

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Distributionally Robust Probabilistic Prediction for Stochastic Dynamical systems

arXiv

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arXiv 2024年

作者： Xu, Tao He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Probabilistic prediction of stochastic dynamical systems (SDSs) aims to accurately predict the conditional probability distributions of future states. However, accurate probabilistic predictions tightly hinge on accurate distributional information from a nominal model, which is hardly available in practice. To address this issue, we propose a novel functional-maximin-based distributionally robust probabilistic prediction (DRPP) framework. In this framework, one can design probabilistic predictors that have worst-case performance guarantees over a pre-defined ambiguity set of SDSs. Nevertheless, DRPP requires optimizing over the function space of probability distributions, which is generally intractable. We develop a methodology that equivalently transforms the original maximin in function spaces into convex-nonconcave minimax in Euclidean spaces. Although it remains intractable to seek a global optimal solution, two suboptimal solutions are derived. By relaxing the inner constraints, we obtain a suboptimal predictor called Noise-DRPP. Relaxing the outer constraints yields another suboptimal predictor, Eig-DRPP. More importantly, we establish an explicit upper bound on the optimality gap between Eig-DRPP and the global optimal predictor. Utilizing the theoretical results obtained from solving the two suboptimal predictors, we synthesize practical rules to judge whether the pre-defined ambiguity set is too large or small. Finally, we conduct elaborate numerical simulations to compare the performance of different predictors under different SDSs. © 2024, CC BY-NC-ND.

关键词： Prediction models

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Dynamic Event-Triggered Consensus on Matrix-Weighted Networks: Leadless Case 42

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

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42nd Chinese control Conference, CCC 2023

作者： Dou, Lintao Wei, Haoyu Huang, Jian Pan, Lulu Shao, Haibin Li, Dewei College of Intelligent Science National University of Defense Technology Changsha410003 China 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 Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581543

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 network. Specifically, 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 behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, 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 networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Optimal controller Design for Non-Affine Nonlinear Power systems with Static Var Compensators for Hybrid UAVs

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Tsinghua Science and Technology 2022年第1期27卷 196-206页

作者： Yanchu Li Qingqing Ding Shufang Li Stanimir Valtchev Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networkand School of Information&Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua UniversityBeijing 100084China the Nova School of Science and Technology(FCT/UNL) Caparica 2829-516LisbonPortugal Technical University of Sofia(TUS) Sofia 2125Bulgaria

A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var system(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is constructed by differential algebraic equations on the MATLAB-Simulink platform with the programming technique of its *** the inverse system method and the Linear Quadratic Regulation(LQR),an optimized SVC controller is *** simulations under three fault conditions show that the proposed controller can effectively improve the power system transient performance.

关键词： Static Var Compensator(SVC) Unmanned Aerial Vehicles(UAV) power system non-affine nonlinear control inverse system method Linear Quadratic Regulation(LQR)

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Co-Scheduling of Energy and Production in Discrete Manufacturing Considering Decision-Dependent Uncertainties

arXiv

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arXiv 2024年

作者： Pan, Yiyuan Wang, Zhaojian The Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncertainties (DDUs) pose additional challenges in finding optimal production strategies, as the signature or the shape of the uncertainty set cannot be determined before solving the model. However, existing research does not account for decision-dependent uncertainties (DDUs) present in discrete manufacturing;moreover, current algorithms for solving models with DDUs suffer from high computational complexity, making them unsuitable for the real-time control requirements of modern industry. To this end, we proposed an energy-production co-scheduling model for discrete manufacturing, taking into account decision-dependent uncertainties (DDUs). Subsequently, we devised a method for transforming the constraints associated with DDUs into linear form ones. Finally, we designed a novel algorithm based on the column-and-constraint generation (C&CG) algorithm and undertook a theoretical analysis of its performance of convergence and algorithmic complexity. A real-world engine assembly line was used to test our model and algorithm. Simulation results demonstrate that our method significantly reduces production costs and achieves better frequency regulation performance. © 2024, CC BY.

关键词： Costs

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OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical systems

arXiv

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arXiv 2024年

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system’s ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Research on Autonomous Foot Movement Recognition Based on SVM 2022

Research on Autonomous Foot Movement Recognition Based on SV...

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7th International Conference on Multimedia and Image processing, ICMIP 2022

作者： Meng, Tongning Zhao, Li Zhang, Zhiwen He, Xinglin Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Intelligent Information Processing Tianjin300222 China

ISBN: (纸本)9781450387408

In order to improve the effectiveness of rehabilitation of stroke patients, active training can be used to treat and recover the patient's foot dyskinesia. Recognizing the different movement characteristics of the feet is an important part of the active rehabilitation of stroke patients. In this paper, the EMG signals of different movements of the right foot are classified and studied. The EMG signals of three different movement states of the foot resting state, foot stretched 15° and foot stretched 45° are collected, absolute mean and filter common space mode were used for feature extraction of EMG signal, and support vector machine (SVM) was used for classification and recognition after extraction. The experimental results show that the classification accuracy rate of resting state-foot-stretched 45° is 89.9%, which exceeds the classification accuracy rate of resting state-foot-stretched 15° of 86.8%. It shows that when the subjects stretch the foot at 45°, more motion units are activated and the characteristics are more obvious than when the feet are stretched at 15°. Therefore, by classifying the characteristics of EMG signals and identifying different autonomic movements of feet, it can be used as the basis for rehabilitation treatment of stroke patients. At the same time, the average classification accuracy of 15°-45 ° and the resting state-15 °-45 ° is above 80%, which confirms the feasibility of the signal processing method and support vector machine classification algorithm used in this paper for the study of automatic foot motion recognition. © 2022 ACM.

关键词： Support vector machines

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