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A trajectory-level robot skill learning method based on fuzzy systems

Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Multi-Feature Fusion Sea Surface Floating Small Target Detection Based on Bagging Decision Tree

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IEICE Transactions on Communications 2025年第5期E108.B卷 681-692页

作者： Cheng, Yi Guo, Yuanyuan Xiu, Chunbo Li, Yiyang Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin300387 China School of Control Science and Engineering Tiangong University Tianjin300387 China

In order to improve the detection performance of small targets under sea clutter background, a detection method based on bagging decision tree with multi-feature information fusion is proposed. First, the time-frequency domain characterization of the standard deviation ratio of the average range of the pulse and amplitudes (SDRARPA) is proposed to describe the discrete echo signal sharpness. Second, the frequency peak-to-average standard deviation ratio (FPASDR) is proposed to optimize the frequency peak-to-average ratio (FPAR) feature. The first two features and the average relative average amplitude (ARAA) form a three-feature information fusion. Weak target detection at sea surface using false alarm controllable bagging decision tree detector based on bagging decision tree classification method. Finally, based on the IPIX measured data, the performance of the algorithms is compared and analyzed for the radar signal data under the four polarization modes. Experimental results show that the detection method proposed in this paper obtains 98.1% and 98.8% average detection probability in 1.024 s and 2.048 s observation time, respectively. There is a significant improvement in the detection performance compared to the traditional detection methods based on convex packet detector and SVM detector. Copyright © 2025 The Institute of Electronics, Information and Communication Engineers.

关键词： Image fusion

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Learning-Observer-Based Adaptive Tracking control of Multiagent Systems Using Compensation Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 358-369页

作者： Li, Zhiqiang Liu, Yang Ma, Hui Li, Hongyi Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Mathematics and Statistics Guangzhou510006 China

This article focuses on the adaptive consensus tracking problem for nonlinear multiagent systems with unmeasurable state and input saturation. For unmeasurable state and nonlinearities, a learning-observer inspired by the idea of extended state observer and the learning ability of neural network are established to observe the reconstructed system. In view of input saturation, a nonlinear auxiliary system with the same order is constructed to obtain the compensation signals. Different from the previous auxiliary system, the proposed method simultaneously compensates the change of nonlinearity caused by input saturation. Subsequently, a consensus tracking error is designed to ensure that the follower tracks the neighbor compensated output. Moreover, an adaptive consensus tracking control method based on learning-observer and compensation mechanism is proposed under the backstepping framework. The stability analysis is given by virtue of the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through the simulation of unmanned air vehicles with one virtual leader and four followers. © 2020 IEEE.

关键词： Neural networks

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Managing Battery Performance Degradation Using Physics-Informed Learning Scheme for Multiple Health Indicators

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7309-7321页

作者： Qin, Linxiao Sun, Tao Sun, Xi-Ming Xia, Weiguo Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Tsinghua University Department of Automation Beijing100084 China

To address the challenges in battery health management, this paper introduces a physics-informed neural network predictor-estimator scheme. In the framework, the predictor forecasts health degradation, setting benchmarks for the estimator, while the estimator provides calibrations for the predictor as substitution to infeasible measurement techniques. For the predictor design, a linear-exponential model is employed for two-stage degradation prediction, enabling online parameter updates and knee point detection. For the estimator design, three neural networks embedded with Coulomb counting and equivalent circuits are proposed to estimate state of charge, internal resistance, and capacity, respectively. Experimental results on the A123 dataset demonstrate superior accuracy of our scheme, with a maximum R-squared improvement of 22.8% in prediction tasks and 11.5% in estimation tasks, while maintaining lightweight network structures with fewer than 1000 floating point operations and 200 parameters. © 2015 IEEE.

关键词： Electronic health record

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Leader-following consensus control for multi-agent systems under DoS attacks and average dwell time switching topology

Leader-following consensus control for multi-agent systems u...

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2024 China automation Congress, CAC 2024

作者： Yuan, Li Liu, Shuai Li, Panshuo Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou Guangzhou510006 China

ISBN: (纸本)9798350368604

This article studies the leader-following consensus problem of multi-agent systems (MASs) in the presence of denial-of-service (DoS) attacks and switching topology. The problem that all edges of one follower are blocked under each attack is considered in this work. By using the multiple Lyapunov functions approach and average dwell time switching technique, a piecewise consensus protocol is designed. Sufficient conditions are established for the consensus of linear MAS. Moreover, the lower bound of average dwell time of the switching topology is determined under DoS attacks. Finally, we provided simulations to validate our theoretical finding. © 2024 IEEE.

关键词： Multi agent systems

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Structured Music Transformer: Structured conditional music generation based on stylistic clustering using Transformer 43

Structured Music Transformer: Structured conditional music g...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Dan Chen, Xin Xiao, Zhe China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9789887581581

Recent advancements in music generation research have significantly progressed the field. However, a prevalent issue among current models is their tendency to overlook music's intrinsic structure, leading to compositions that lack coherence and thematic consistency. This paper proposes a novel approach to structured music generation that addresses these challenges by considering music structure as styles of different subsegments. The approach enhances the deep structural aspects of generative music by deriving embedding vectors from pre-trained contrastive models and clustering them into discrete structured tokens for generative conditions. Furthermore, this study introduces a self-similarity-based evaluation method to assess the structural features of the generated music. Experimental results indicate that the Structured Music Transformer (SMT) significantly outperforms contemporary models in producing compositions that are stylistically coherent and structurally robust, as demonstrated across two diverse datasets. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Contrastive Learning

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A Fine-Grained Object Detection Model for Aerial Images Based on YOLOv5 Deep Neural Network

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Chinese Journal of Electronics 2023年第1期32卷 51-63页

作者： ZHANG Rui XIE Cong DENG Liwei Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration School of AutomationHarbin University of Science and Technology

Many advanced object detection algorithms are mainly based on natural scenes object and rarely dedicated to fine-grained objects. This seriously limits the application of these advanced detection algorithms in remote sensing object detection. How to apply horizontal detection in remote sensing images has important research significance. The mainstream remote sensing object detection algorithms achieve this task by angle regression, but the periodicity of angle leads to very large losses in this regression method, which increases the difficulty of model learning. Circular smooth label(CSL)solved this problem well by transforming the regression of angle into a classification form. YOLOv5 combines many excellent modules and methods in recent years, which greatly improves the detection accuracy of small *** use YOLOv5 as a baseline and combine the CSL method to learn the angle of arbitrarily oriented targets,and distinguish the fine-grained between instance classes by adding an attention mechanism module to accomplish the fine-grained target detection task for remote sensing images. Our improved model achieves an average category accuracy of 39.2% on the FAIR1M dataset. Although our method does not achieve satisfactory results,this approach is very efficient and simple, reducing the hardware requirements of the model.

关键词： Deep learning Neural networks Object detection Transformers Hardware Classification algorithms Task analysis

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Research and application of composite stochastic resonance in enhancement detection

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Chinese Physics B 2024年第1期33卷 264-273页

作者：高蕊焦尚彬薛琼婕 School of Automation and Information Engineering Xi'an University of TechnologyXi'an 710048China School of Electronic and Electrical Engineering Baoji University of Arts and SciencesBaoji 721016China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an 710048China

Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance(NCSR) model is proposed by combining the Woods–Saxon(WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection ***, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio(PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.

关键词： Woods–Saxon improved piecewise model composite stochastic resonance(SR) image denoising

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Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 595-607页

作者： Jianguo Zhao Chunyu Yang Weinan Gao Linna Zhou Xiaomin Liu the Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationChina University of Mining and TechnologyXuzhou 221116 the School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116 IEEE the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation *** the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost *** the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement *** combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop *** proposed decomposition design not only bypasses the numerical stiffness but also alleviates the *** efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

关键词： Adaptive optimal control decentralized control output regulation reinforcement learning(RL) singularly perturbed systems(SPSs)

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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