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第43届中国控制会议

作者： Wei Cai Yahui Gan Fang Fang Bo Zhou Xianzhong Dai School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper presents a smooth and time-optimal improved-Bezier trajectory planning algorithm that addresses the avoidance of solving switching points and solves the problem of discontinuous torque on the basis of the time-optimal path parameterization(TOPP) algorithm. The proposed approach employs the TOPP-RA algorithm with a friction model, to construct the optimal velocity trajectory under the constraints of the geometric paths and joint torques. Moreover, the proposed algorithm utilizes an enhanced Bezier curve instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The modified Bezier curve optimizes the peaks of the pseudo-velocity curve in the phase plane to change the discontinuity of joint torque. The proposed algorithm ensures absolute time optimality and achieve a s mooth tr ajectory. The results of the MATLAB simulation experiment on a two-link motion system indicate the validity of the proposed algorithm.

关键词： time-optimal smooth trajectory planning improved-Bezier trajectory

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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Disturbance Rejection control Method Based on Adaptive Composite Disturbance Observer for Permanent Magnet Synchronous Motor

Disturbance Rejection Control Method Based on Adaptive Compo...

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第43届中国控制会议

作者： Yu Fang Di Wu Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The vehicle electronic scroll compressors exhibits significant speed fluctuations during low-speed and heavy-load *** periodic fluctuation of load torque leads to speed *** limited sensitivity gain of traditional disturbance observers results in poor performance in suppressing periodic *** response to this issue,this study adopts an adaptive composite disturbance observer to estimate and compensate for load disturbances,in order to reduce speed fluctuations in the low-speed *** adaptive composite disturbance observer has achieved simultaneous and adaptive observation and estimation of periodic and aperiodic slow varying *** adaptive notch filter(ANF) based on variable step-size quantize least mean square(VSQLMS) algorithm is used to estimate the fundamental frequency of periodic *** simulation experiments,the method proposed has improved the control accuracy and dynamic response performance of the system,and has enhanced its robustness.

关键词： Extended State Observer internal model principle ANF multiple disturbance

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A Multi-Gas Detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Air quality

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Modified Deep Deterministic Policy Gradient Based Optimal Speed Tracking Strategy for PMSMs 13

Modified Deep Deterministic Policy Gradient Based Optimal Sp...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Fan, Zhong-Xin Tang, Lintao Li, Shihua Liu, Rongjie School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Florida State University Department of Statistics TallahasseeFL32304 United States

ISBN: (纸本)9798350361674

Achieving optimal speed regulation for permanent magnet synchronous motors (PMSMs) remains a challenging task, particularly in selecting the most suitable controller to meet desired objectives. This paper considers the optimal speed tracking problem of PMSMs. We employ a reinforcement learning algorithm to obtain optimal controller with a linear form. The reinforcement learning approach incorporates a critic-actor network structure, which approximates the value function and optimal control, respectively. Specifically, a weight updating method for the critic network is introduced, where the update of the actor network is guided by the critic network. Through data collection and neural network training, dynamic acquisition of the optimal controller gain is achieved. In addition, a novel activation function is implemented in the network, leading to improved control performance concerning dataset size and reduced training time. At final, a real-world PMSM application is presented along with comparisons to demonstrate effectiveness. The desire idea of resulting optimal controllers is not only applicable to linear systems but also capable of approximating arbitrary nonlinear optimal controllers, thus offering practical engineering versatility. © 2024 IEEE.

关键词： Reinforcement learning

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Model Prediction Based Adaptive Variable Impedance control of Manipulator with Passivity

Model Prediction Based Adaptive Variable Impedance Control o...

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第43届中国控制会议

作者： Yang Xiao Wenbo Liu Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the compliance control of rigid manipulators in human-robot interaction scenarios, a new variable impedance control(VIC) model is proposed to improve the adaptive ability of the manipulator to adjust compliance in different phases of the task. The parameter selection problem is transformed into a controller design problem. Impedance constraints are set by introducing the prior knowledge of the person, information about the state and contact force, and manipulability. The one-step model predictive control(MPC) method is used to search for the optimal solution of impedance parameters, so that the manipulator can adaptively select different balance relations between tracking accuracy and compliance. Moreover, in order to avoid the damage of time-varying impedance parameters to the stability, a tank-based method is implemented to ensure the passivity of the impedance system. Finally, the simulation results verify the effectiveness of the proposed control scheme.

关键词： Variable Impedance control(VIC) Model Predictive control(MPC) Tank-based method Passivity

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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43rd Chinese control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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Can ChatGPT Boost Artistic Creation:The Need of Imaginative Intelligence for Parallel Art

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 835-838页

作者： Chao Guo Yue Lu Yong Dou Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

CHATGPT,one of the leading Large Language Models(LLMs),has acquired linguistic capabilities such as text comprehension and logical reasoning,enabling it to engage in natural conversations with humans.

关键词： such has reasoning

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Decision-Making and Path Planning for Highway Autonomous Driving Based on Spatio-Temporal Lane-Change Gaps

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IEEE systems Journal 2022年第2期16卷 3249-3259页

作者： Feng, Zhiqi Song, Wenjie Fu, Mengyin Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

Safe and efficient decision-making and path planning is a challenging problem for autonomous driving in highway because of numerous dynamic vehicles around the ego-vehicle. For ego-vehicle driving on structured roads, behavioral decisions tend to pay more attention to the relative distribution of its surrounding vehicles, while path planning based on the decision requires more detailed consideration of vehicle model and lane curvature. Therefore, these two modules are separated in our article and executed successively in different coordinate systems. Specifically, we create a decision topology for lane changing or following decision based on spatio-temporal lane-change gaps in relative moving coordinate, which consists of the node representing the gap and the edges representing the connectivity between two gaps. Combining the decision topology with the behaviors of surrounding dynamic vehicles, a local cost map is generated and the target decision gap is selected. Once the decision is done, a smooth and safe trajectory from the ego-vehicle to the target decision gap, that satisfies the constraints of acceleration and curvature, is obtained in the geodetic coordinate through polynomial trajectory generation. The real-time performance and effectiveness of this method were verified in the dynamic high-speed interaction scenarios. © 2007-2012 IEEE.

关键词： Decision making

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Inverse Reinforcement Learning Integrated Reinforcement Learning for Single Intersection Traffic Signal control 5

Inverse Reinforcement Learning Integrated Reinforcement Lear...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Gu, Shiyi Zhang, Tingting Zhang, Ya Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China Army Engineering University College of Command and Control Engineering Nanjing210017 China

ISBN: (纸本)9798350325294

Traffic Signal control (TSC) optimization is vital for efficient traffic flow in urban intersections. Recent research has explored the application of Reinforcement Learning (RL) agents to tackle TSC challenges. However, the selection of an appropriate reward function in RL agent training still relies on subjective judgment and experience. To address this issue, an Inverse Reinforcement Learning (IRL) integrated RL algorithm is proposed. In the IRL learning section, expert trajectory data are collected and analyzed by Relative Entropy IRL (REIRL). The latent reward of expert policy is reconstructed and utilized in RL agent training process. In the RL control section, a Double Dueling Deep Q Network is applied under a cycle control framework. As verified in simulations, the introduction of expert experience improves the performance of the RL agent to the expert level and concurrently enables robustness to expert policy noises. © 2023 IEEE.

关键词： Reinforcement learning

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