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第42届中国控制会议

作者： Ao Zong Chen Chen Kai Meng School of Automation Beijing Institute of Technology The National Key Lab of Autonomous Intelligent Unmanned Systems

In this paper,we study a novel sensor weapon target assignment(SWTA) problem,called the multi-stage SWTA problem,and the multi-stage refers to the division of the operational process into several interception *** objective of the multi-stage SWTA is to maximize the total value of destroyed targets by using as few sensors and weapons as *** solve this problem,a modified multi-objective evolutionary algorithm based on decomposition(MOEA/D) has been ***,a constructive heuristic mechanism based on the marginal benefit is introduced to ensure the feasibility of the initial ***,a novel penalty value is added to the penalty-based boundary intersection to expand the choice of solutions,and combined with the non-dominated solutions selection to improve the convergence of the ***,a mechanism for deleting invalid genes is designed to transform infeasible solutions into feasible *** results show that compared with existing algorithms,the proposed method provides a high-quality multi-stage SWTA scheme in terms of solution accuracy,convergence,and diversity performance.

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MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotating Machine based on Multi-scale Convolutional Extraction and Adaptive Pruning Fuzzy Inference System

MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotatin...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhou, Yanrun Lei, Zihao Wen, Guangrui Su, Yu Zhang, Zhifen National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an710049 China

ISBN: (纸本)9789819639687

intelligent fault diagnosis on mechanical transmission system of the train is of great importance to ensure the efficient operation of high-speed trains. However, due to the complex and changeable operation conditions (OCs), it is difficult to ensure the uniformity of the data distribution obtained by real-time acquisition, which sets a higher standard for the design of intelligent algorithms. Empowered by deep transfer learning (DTL) methods, the fuzzy inference system provides an effective solution to the abovementioned problem. In this paper, an Adaptive Pruning Fuzzy Inference System (APFIS) is proposed predicated on the fuzzy theory and the basic structure of fuzzy neural network, along with the hybrid model MSC-APFNN constructed by combining the multi-scale convolutional network with the proposed APFIS. This model can be employed as a backbone for the application to transfer diagnostic detection of rotating systems based on adversarial networks. Experimental results demonstrate that the proposed model achieves excellent diagnostic results under variable operating conditions, which provide a novel insight into the optimization of the traditional convolutional and fully-connected deep network structure and offer a promising avenue for the research of fuzzy decision-making in the context of fault diagnostics for high-speed trains. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Fuzzy neural networks

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Deep Reinforcement Learning for Dynamic Error Compensation in 3D Printing 19

Deep Reinforcement Learning for Dynamic Error Compensation i...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Wang, Dong Shen, Zhen Dong, Xiangyang Fang, Qihang Wang, Weixing Dong, Xisong Xiong, Gang The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266109 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9798350320695

In the 3D printing process, various error factors can affect the accuracy of the final printing quality. However, current 3D printing error compensation methods have limited effects and usually cannot work in real-time. The 3D printing error compensation process is modeled as a Markov decision process (MDP) in this paper, and Deep Reinforcement Learning (DRL) is applied for dynamic error compensation. This method learns autonomously through trial and error by interacting with the printing environment, which makes it adaptable to various types of 3D printers without specific training. Then, we simulate the digital light processing (DLP) 3D printing. Due to the huge state and action space of sliced images, applying the DRL algorithm to DLP is challenging. We propose an error compensation method based on morphological image operation and use Autoencoder to extract error features to reduce the state space. We then implement our method using a Twin Delayed Deep Deterministic policy gradient algorithm (TD3). The results demonstrate the effects of our method in compensating 3D printing errors. © 2023 IEEE.

关键词： 3D printing

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Different Height Stairs Climbing Pattern Generation for Humanoids

Different Height Stairs Climbing Pattern Generation for Huma...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Yang, Ming Tian, Dingkui Xu, Minjia Li, Feng Sun, Jianquan Shang, Weiwei Wu, Xinyu University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Daqing Vocational College Daqing163255 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen518055 China

ISBN: (纸本)9798350327182

Climbing stairs has been an indispensable ability for humanoids. This paper presents a novel and practical problem: climbing stairs of varying heights. In this study, the humanoid is modeled as an inverted pendulum and set the pelvis trajectory on the vertical axis, representing the center of mass (CoM), as a quadratic function. We also use a polynomial to design the ZMP and CoM trajectory on the horizontal plane. To generate the CoM trajectory, the sliding mode controller is utilized to track the reference trajectory. To eliminate chattering of the sliding mode controller, the universal approximation property of the radial basis function (RBF) network is leveraged to approximate the switching law. Finally, simulation experiments are performed in CoppeliaSim, and the biped robot is capable of walking stably and quickly. © 2023 IEEE.

关键词： Stairs

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Multimodal Multilevel Attention for Semi-Supervised Skeleton-Based Gesture Recognition

SSRN

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SSRN 2024年

作者： Liu, Jinting Gan, Ming-Gang He, Yuxuan Guo, Jia Hu, Kang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

Although skeleton-based gesture recognition based on supervised learning has made promising achievements, the reliance on large amounts of annotation for training poses a significant cost. This paper addresses semi-supervised gesture recognition, aiming to learn feature representations effectively from unlabeled data. To resolve this problem, we propose a novel multimodal multilevel attention network to achieve semi-supervised learning. This model uses an attention mechanism to polymerize multimodal and multilevel complementary semantic information of the hand skeleton, designing a multimodal multilevel contrastive loss to measure the similarity of features for semi-supervised learning. Specifically, our method explores the relationships between joint, bone, and motion to learn more discriminative feature representations. Considering the hierarchy of the hand skeleton, the skeleton data is divided into multilevel (i.e., hand-level, finger-level, and joint-level) to capture complementary semantic information. Furthermore, a multimodal contrastive loss measure similarity among multilevel representations. The proposed method outperforms the semi-supervised skeleton-based gesture recognition tasks, according to extensive experimentation on two challenging datasets from SHREC-17 and DHG 14/28 datasets. © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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A Coupling Algorithm for Task and Path Planning of Multi-UGVs under Environmental Inspiration

A Coupling Algorithm for Task and Path Planning of Multi-UGV...

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International Conference on Control and Automation (ICCA)

作者： Bochen Xu Hao Fang Yuchen Mao Yujie Wei Qingkai Yang Lei Zhou Zhi Gao National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China School of Remote Sensing and Information Engineering Wuhan University Wuhan China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a representative model for multi-UGVs task planning. It describes the allocation for tasks of several unmanned vehicles, considering the capacity of each vehicle and the requirements, as well as the time windows, of various tasks. However, existing studies primarily focus on the task assignment and do not consider the connection between path costs and obstacles in complicated scenarios. In this paper, we propose a coupled task and path planning algorithm for multiple unmanned vehicles under environmental inspiration. We introduces a novel path cost function and achieve seamless integration of solutions from the task level to the path level. Experimental results indicate that, in static obstacle environments, our algorithm module can provide more accurate task assignment and path planning results with smaller costs under similar time consumption.

关键词： Couplings Accuracy Costs Vehicle routing Cost function Path planning Planning

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Piecewise Constant Tuning Gain Based Singularity-Free MRAC with Application to Aircraft Control systems

arXiv

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arXiv 2024年

作者： Zhang, Zhipeng Zhang, Yanjun Sun, Jian State Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

This paper introduces an innovative singularityfree output feedback model reference adaptive control (MRAC) method applicable to a wide range of continuous-time linear time-invariant (LTI) systems with general relative degrees. Unlike existing solutions such as Nussbaum and multiple-model-based methods, which manage unknown high-frequency gains through persistent switching and repeated parameter estimation, the proposed method circumvents these issues without prior knowledge of the high-frequency gain or additional design conditions. The key innovation of this method lies in transforming the estimation error equation into a linear regression form via a modified MRAC law with a piecewise constant tuning gain developed in this work. This represents a significant departure from existing MRAC systems, where the estimation error equation is typically in a bilinear regression form. The linear regression form facilitates the direct estimation of all unknown parameters, thereby simplifying the adaptive control process. The proposed method preserves closed-loop stability and ensures asymptotic output tracking, overcoming some of the limitations associated with existing methods like Nussbaum and multiple-model based methods. The practical efficacy of the developed MRAC method is demonstrated through detailed simulation results within an aircraft control system *** Codes 93A10, 93B52, 93C40, 93D20 Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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3D Point Cloud-Based Lithium Battery Surface Defects Detection Using Region Growing Proposal Approach 8th

3D Point Cloud-Based Lithium Battery Surface Defects Detecti...

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8th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2023

作者： Rehman, Zia Ur Wang, Xin Alsumeri, Abdulrahman Abdo Ali Khan, Malak Abid Ali Ma, Hongbin State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975891

Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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Hypersonic Re-entry Vehicle Fault-Tolerant Control Against Actuator Failure via Integral Sliding Mode

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Journal of Beijing Institute of technology (English Edition) 2021年第ZK期30卷 111-120页

作者： Guo, Fuhui Lu, Pingli School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

A fault-tolerant control (FTC) scheme is proposed based on integral sliding mode(ISM) for attitude control of hypersonic re-entry vehicle (HRV) under partial loss of actuator effectiveness. First, the inner/outer loop mathematical model of HRV in the present of actuator failure is given with specific analysis on torque. Then prescribed performance approach is introduced to the ISM controller design by error transformation. Specifically, the transformation error rather than tracking error is chosen as the sliding variable to establish the sliding mode surface and reaching law. As a result, the attitude tracking error of HRV is limited into the expected range. Meanwhile, the transient process and the steady state behavior are both considered. Considering that the unknown fault information of actuator can cause adverse effect on HRV, an extended state observer(ESO) is adopted to estimate the unknown actuator failure. Thus the observer result is served as compensation for the loss of the controlled system performance. Simulation results show that the proposed scheme can fulfil the tracking task with prescribed performance. © 2020 Journal of Beijing Institute of technology

关键词： Actuators

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Instruction Pre-Training: Language Models are Supervised Multitask Learners

arXiv

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arXiv 2024年

作者： Cheng, Daixuan Gu, Yuxian Huang, Shaohan Bi, Junyu Huang, Minlie Wei, Furu Microsoft Research The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University China

Unsupervised multitask pre-training has been the critical method behind the recent success of language models (LMs). However, supervised multitask learning still holds significant promise, as scaling it in the post-training stage trends towards better generalization. In this paper, we explore supervised multitask pretraining by proposing Instruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train LMs. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of Instruction Pre-Training. In pre-training from scratch, Instruction Pre-Training not only consistently enhances pre-trained base models but also benefits more from further instruction tuning. In continual pre-training, Instruction Pre-Training enables Llama3-8B to be comparable to or even outperform Llama3-70B. Our model, code, and data are available at https://***/microsoft/LMOps. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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