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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yinghan Wang Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by point clouds, MNP first efficiently compresses point clouds into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

关键词： Point cloud compression Learning systems Training Simulation Neurons Training data Encoding

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A discrete-time looped functional approach and its application to discrete-time systems with cyclically varying delays

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Science China(information Sciences) 2025年

作者： Wen-Hu CHEN Chuan-Ke ZHANG Chen-Rui WANG Ke-You XIE Yong HE Hong-Bing ZENG School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Electrical and Information Engineering Hunan University of Technology

This paper investigates the stability analysis problem of discrete-time systems with cyclically varying *** previous studies, the delay investigated in this paper cyclically alternates between increasing and decreasing changes within its upper and lower bounds. First, to reasonably describe the cyclic variation characteristics of the delay, a discretetime looped functional(DTLF) approach and a refined allowable delay set(ADS) are proposed. Then, based on the constructed DTLF with delay-product terms and using matrix-separation-based inequality to estimate its forward difference,delay-variation-interval-dependent stability conditions considering two different ADSs are derived. Finally, the effectiveness and superiority of the obtained conditions are demonstrated through two numerical examples.

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Distributed Secondary control of DC Microgrids with Operation Loss Reduction

Distributed Secondary Control of DC Microgrids with Operatio...

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第43届中国控制会议

作者： Maoxiang Qiu Zhicheng Zhang Zhiqiang Zuo Yijing Wang Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In DC microgrids, current sharing and voltage regulation are two main objectives for real-time control, while the operation loss reduction is typically achieved by steady-state optimization. However, the ratio of current sharing is generally determined by the result of the centralized optimization in the classical hierarchical control framework of DC microgrids. Such an issue brings insufficient capability in respond to the changes of photovoltaic power and stored energy in the solar-powered energy systems. To handle this, this work proposes a distributed control scheme for single-bus DC microgrids. Under the proposed controller, the objectives of current sharing, bus voltage regulation and the total operation loss(converter loss and line loss) reduction can be guaranteed simultaneously. Moreover, the constraints on line currents and bus voltage are tackled as ***, simulations verify the performance of the proposed control scheme.

关键词： DC Microgrids Current Sharing Voltage Regulation Operation Loss Reduction Distributed Secondary control

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Distributed Task Allocation with Time Constraints Under Limited Communication Resources in Multi-Agent System

Distributed Task Allocation with Time Constraints Under Limi...

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Asian control Conference

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

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, a new distributed allocation algorithm based on performance impact with collective influence (PI-CI) is proposed. Considering inefficiency of creating time slots in PI-MaxAss, two completely independent phases with different communication frequencies are designed to ensure allocation both real-time and conflict-free under limited communication. To solve the misconvergence of the baseline algorithm PI-MaxAss in allocation results, an evaluation function is put forward in consensus phase to directly determines the final attribution of repeatedly assigned tasks, guaranteeing convergence of allocation. Comparison experiments validate the performance improvement of the PI-CI algorithm over the baseline algorithm.

关键词： Time-frequency analysis Real-time systems Resource management Time factors Multi-agent systems Radio spectrum management Convergence

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An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 670-679页

作者： Guancheng Wang Zhihao Hao Haisheng Li Bob Zhang PAMI Research Group Department of Computer and Information ScienceUniversity of MacaoTaipaMacaoChina China Industrial Control Systems Cyber Emergency Response Team BeijingChina Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijingChina

This study proposes a novel gradient‐based neural network model with an activated variable parameter,named as the activated variable parameter gradient‐based neural network(AVPGNN)model,to solve time‐varying constrained quadratic programming(TVCQP)*** with the existing models,the AVPGNN model has the following advantages:(1)avoids the matrix inverse,which can significantly reduce the computing complexity;(2)introduces the time‐derivative of the time‐varying param-eters in the TVCQP problem by adding an activated variable parameter,enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory;(3)adopts the activation function to accelerate the convergence *** solve the TVCQP problem with the AVPGNN model,the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker ***,a variable parameter with an activation function is employed to design the AVPGNN *** accuracy and convergence rate of the AVPGNN model are rigorously analysed in ***,numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed ***,to explore the feasibility of the AVPGNN model,appli-cations to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated.

关键词： computational intelligence mathematics computing optimisation

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Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicing in Time-Sensitive Networking

Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicin...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Shouliang Xu, Qimin Zhang, Yanzhou Xu, Lei Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai China 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781728119489

The development of the Industrial Internet of Things (IIoT) is increasingly relying on the low-latency and low-jitter information exchange. Time-Sensitive Networking (TSN) guarantees the deterministic transmission by a set of mechanisms, such as Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). However, the deterministic scheduling of hybrid traffic with different Quality of Service (QoS) is still unresolved since traffic performance is hard to meet by a single mechanism or an integrated mechanism with fixed time slot. In this paper, to realize the low-latency and low-jitter hybrid traffic scheduling, an adaptive time slot slicing (ATSS) method is proposed integrating TAS and CQF. With dynamically slicing the time slot to centralize time-triggered (TT) traffic, ATSS eliminates the irregular distribution of TT flow to support multi-hop networks. A resource calculation method based on slot occupancy judgment is devised to reduce the scheduling complexity, which raises the analysis objects into flows instead of frames. To reduce the complexity further, an incremental algorithm with the search boundary of resources is designed. The evaluation results show that ATSS reduces the jitter by 12 times and time cost by 200 times when handling over 125 mixed flows in different network topologies compared to previous work. © 2022 IEEE.

关键词： Jitter

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Multi-Agent Path Planning for Finite Horizon Tasks with Counting Time Temporal Logics

Multi-Agent Path Planning for Finite Horizon Tasks with Coun...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Peng Lv Shaoyuan Li Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we consider the multi-agent path planning problem for high-level tasks with finite horizons. In many situations, there is the need to count how many times a sub-task is satisfied in order to achieve the overall task. However, existing temporal logic languages, such as linear temporal logic, may not be efficient in describing such requirements. To address this issue, we propose a new temporal logic language called Counting Time Temporal Logic (CTTL) that extends linear temporal logic by explicitly counting the number of times that some tasks are satisfied. To solve the CTTL path planning problem, we use integer linear programming to encode the satisfaction of the task. We demonstrate that our approach is both sound and complete. To validate our results, we present numerical experiments to show the scalability of the proposed approach. Furthermore, we provide a simulation case study of a team of autonomous robots to illustrate the synthesis procedure.

关键词： Computer aided software engineering Automation Scalability Integer linear programming Path planning Planning Delays Logic Standards Autonomous robots

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DMAM-ECG: A Diffusion Model with Self-Attention Module for ECG Signal Denoising

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1278-1296页

作者： Hu, Zheng-Dong Hong, Yang Huang, Jia-Yan Chen, Kai-Hong Zhao, Wan-Qi Grau, Antoni Guerra, Edmundo Wang, Chuan-Sheng Zhang, Fu-Quan New Engineering Industry College Putian University Putian351100 China School of Mathematics and Data Science Minjiang University Fuzhou350108 China The University of Sheffield SheffieldS10 2TN United Kingdom Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Electrocardiogram (ECG) is the most common and non-invasive auxiliary diagnostic technique for heart disease. However, the ECG signal is susceptible to various noises during the collection process, which will impact subsequent analysis and diagnosis. Therefore, ECG signal denoising has become the primary task after signal acquisition. ECG signal denoising aims to remove noise interference from the collected noisy signals for clarify abnormal waveforms in it, and thus diagnose diseases more accurately. In this paper, to obtain high-quality and high-fidelity denoised ECG signals, we propose a novel diffusion model with self-attention module for ECG signal denoising (DMAM-ECG). Specifically, the DMAM-ECG contains two main branches used to extract features of input noise signal observations and potential variables, respectively. Each branch consists of 6 designed Convolution-Instancenormal-LeakyReLU Filter Blocks (CIL-Filter Blocks). Meanwhile, DMAM-ECG introduces the self-Attention Module Fusion Blocks (SAM-Fusion Blocks) to further extract features for the first branch, and then fuse the output features with the second branch. Besides, we use a multi-shot reconstruction strategy to further improve signal denoising. Quantitative and qualitative results on the QT database show that the proposed DMAM-ECG performs better than the existing 5 state-of-the-art ECG signal denoising methods, especially for strong noise interference ECG signal removal. To sum up, our DMAM-ECG introduces self-attention module into the diffusion model to improve the grasp of global information and achieve high-quality information preservation for extremely noise removal of ECG signal. In particular, it uses only 3-shot reconstruction gets much better denoised results than that of the best baseline with 10-shot. The code of the proposed DMAM-ECG is available at https://***/luvletterh/DMAM-ECG. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Signal denoising

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Time-to-go Estimation for Guidance Law with Impact Angle Constraint 41

Time-to-go Estimation for Guidance Law with Impact Angle Con...

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第41届中国控制会议

作者： Binghe An Zongzhun Zheng Bo Wang Huijin Fan Lei Liu Yongji Wang National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Aerospace Automatic Control Institute

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the time-to-go estimation problem for the impact-angle-constrained guidance *** facilitate the time-to-go estimation,the whole guidance process is divided into two *** sliding mode guidance law based on the constant reaching law is designed for the first stage and the proportional guidance law is utilized in the second *** choosing the sliding mode variable appropriately,not only an attack with a small miss distance but also a prescribed impact angle can be *** to the feature of the constant reaching law,the time-to-go estimation for the first stage can be established *** obtain the time-to-go of the second stage,the estimated look-ahead angle and range at the switching point between the first stage and second stage are ***,the time-to-go estimation of the second stage is obtained according to the trajectory shape of the missile steered by the proportional guidance *** the second stage,rather than the estimated look-ahead angle and range but the current look-ahead angle and range are used to calculate the *** this way,an actual time-to-go estimation of the second stage can be ***,various numerical simulations are provided to show the effectiveness and superiority of the proposed estimation scheme.

关键词： Time-to-go estimation Impact angle constraint Sliding mode guidance law Two-stage guidance law

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