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International Conference on control and Automation (ICCA)

作者： Liping Wang Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems South China University of Technology Guangzhou P. R. China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper addresses the prescribed time performance attitude tracking control problem of un-manned aerial vehicles under unknown attacks and input saturation. The attitude error dynamic model is constructed under the framework of Lie group SO(3). By integrating a prescribed time performance function with coordinate transformation, the tracking error can remain within a given region in a prescribed time. In the backstepping design process, a first-order sliding mode differentiator is given to overcome the explosion of complexity problem, and nonlinear disturbance observer is developed to deal with the unknown attacks and input saturation. A prescribed time performance attitude tracking control method is designed to ensure that all of the closed-loop signals are bounded. Finally, the superiority and effectiveness of the designed controller are stated by the simulation results.

关键词： Backstepping Attitude control Communication systems Simulation Lie groups Explosions Disturbance observers

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Structure Optimization of Uio-66 Supported Metal Nanoclusters Based on High-Dimensional Neural network

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SSRN 2024年

作者： Fan, Tian-E Zhang, Yan-Ru Li, Han-Yu Yang, Ping-An College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing400065 China

Metal organic frameworks (MOFs) supported metal nanoclusters (MNCs) have attracted great interest in the field of catalysts because of their high catalytic activity, excellent thermal stability and high surface area. The stable geometric structure of MNCs@MOFs materials determines their catalytic performance, therefore, to predict the stable structure of MNCs@MOFs materials is necessary for understanding their catalytic properties. In this paper, the stable structures, the thermodynamic stability and electronic properties of MNCs on UiO-66 are investigated by using high dimensional neural network potential (HDNNP) and density functional theory (DFT). Moreover, an active learning strategy is combined with HDNNP in screening optimal structures of MNCs@MOFs nanocomposites. The accuracy of HDNNP combined with active learning strategy is verified by comparing the prediction results with DFT calculation. The results show that large MNCs move slightly in the UiO-66 pores due to their strong metal bonds inside, while small MNCs tend to form M-C bonds with organic linkers for maintaining thermodynamic stability near the support sites. In addition, increasing the interaction between metal clusters and organic connectors is help for the synthesis of small metal clusters encapsulated in UiO-66, which is important to understand the electronic properties and formation mechanism of MNCs@MOFs composites. © 2024, The Authors. All rights reserved.

关键词： Structural optimization

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Process Monitoring of Operational Cost for Wastewater Treatment Processes Using Variants of ARMA Models Based Soft-sensors 11

Process Monitoring of Operational Cost for Wastewater Treatm...

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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Lu, Yu Liu, Yiqi Li, Dong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Technical University of Denmark Department of Chemical and Biochemical Engineering Lyngby Denmark

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Process monitoring of operation cost index (OCI) is of great importance for wastewater treatment plants (WWTPs), which is not only able to support financial budget, but also to optimize local operation. This paper proposed four variants of auto-regressive and moving average (ARMA), based on recursive least squares algorithm (RLS), ARMA based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN), and nonlinear auto-regressive neural network with external input (NARXNN) respectively, to predict the operating cost in WWTPs. The proposed methods were validated in the simulation platform, Benchmark Simulation Model No.2-P (BSM2-P). On account of the strong nonlinearity of the wastewater treatment process, the nonlinear model, like NARXNN, achieved better performance in terms of mean square error (MSE) and correlation coefficient (R). © 2022 IEEE.

关键词： Wastewater treatment

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Structure Optimization of Uio-66 Supported Metal Nanoclusters Based on High-Dimensional Neural network

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SSRN 2024年

作者： Fan, Tian-E Zhang, Yan-Ru Li, Han-Yu Lei, Hao-Ran College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing400065 China

关键词： Thermodynamic stability

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics Automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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High-Throughput Screening of Ni-Rich Layered Cathode Materials for Sodium-Ion Batteries Based on Machine Learning

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SSRN 2024年

作者： Fan, Tian-E Lei, Hao-Ran Li, Han-Yu College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing400065 China

Ni-rich layered cathode materials have attracted widespread attention due to their high capacity and cycle life for sodium-ion batteries (SIBs). Screening stable Ni-rich layered cathode materials is extremely important for improving their electrochemical performance. In this paper, a transfer learning (TL) combined with deep neural networks (DNN) method is developed to predict the average voltage and specific capacity of Ni-rich layered cathode materials, and to screen doped cathode materials with excellent electrochemical performance. First, two training datasets from Materials Project (MP) are constructed for initial training and retraining of DNN model, respectively. 2673 potential cathode materials are obtained by using element substitution as test dataset for DNN model. The best predicted results by using initial trained DNN models showed the mean absolute errors of 0.44V and 27.36mAgh-1 for voltage and specific capacity, respectively. Then, a TL method is integrated into our DNN model for retraining. The performance of retrained DNN model is significantly improved to 0.25V for voltage and 60.23mAgh-1 for specific capacity, compared with the previous DNN model. Finally, 391 cathode materials are identified for Ni-rich SIBs with average voltage > 3.8V and specific capacity > 240 mAgh-1, most of which are Co-free materials. This work provides a fast and effective method for high-throughput screening of Ni-rich cathode materials with high average voltage and specific capacity. © 2024, The Authors. All rights reserved.

关键词： Cathodes

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on control of network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Multi-Objective Optimal control of Wastewater Treatment Processes Constrainted by Energy-Saving and Effluent Qualities 6

Multi-Objective Optimal Control of Wastewater Treatment Proc...

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6th International Conference on Robotics, control and Automation Engineering, RCAE 2023

作者： Zhang, Jing Yan, Haoming Chen, Hongtian Xu, Shuiqing Liu, Yiqi School of Automation Science & Engineering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China College of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350315301

Considering the high energy consumption problem in the wastewater treatment process, this paper proposes a multi-objective optimization control scheme based on NSGA-II algorithm. The proposed scheme can not only ensures meet effluent quality requirements but also can achieves energy cost savings. This method takes the aeration energy consumption and pumping energy consumption of the system as the optimization goal with the effluent water quality being the constraint condition. At the same time, the adaptive fuzzy PID controller is designed to ensure the control accuracy and stability of the controlled system. The results of simulation experiments validated in benchmark simulation platform, BSM1, show that, compared with PI control and single-objective optimization control, the proposed optimization scheme in this paper can achieve the desired quality of the effluent water withmore energy-saving. © 2023 IEEE.

关键词： Energy utilization

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A Small UAV Detection Method Based on Optical Flow and Visual Feature Fusion 23

A Small UAV Detection Method Based on Optical Flow and Visua...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, Miao Wang, Hanzhuo Mao, Shengjian Shi, Zhiguo Tao, Ran College of Control Science and Engineering Zhejiang University Hangzhou310027 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350325959

The rapid growth of the unmanned aerial vehicle (UAV) market poses threats to public safety and personal privacy. Visual detection has become a cost-effective and important method for UAV detection, but it struggles with detecting small and distant UAVs that lack clear morphological features. To address this, we propose Fusion Net, a network detection approach that combines optical flow features with visual features for improved detection. Fusion Net utilizes Convolutional Neural networks (CNNs) and Transformers for feature extraction and fusion, achieving excellent detection results. We also introduce Mask Augmentation, a new data augmentation method, to enhance network convergence and diversity in UAV scenes. Fusion Net, pre-trained on the Drone-vs-Bird dataset, exhibits excellent performance in detecting small drones, as demonstrated by its high detection accuracy in our self-made dataset evaluation. © 2023 IEEE.

关键词： feature fusion Unmanned aerial vehicle detection video object detection

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Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields

Adaptive Positional Encoding for Bundle-Adjusting Neural Rad...

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International Conference on Computer Vision (ICCV)

作者： Zelin Gao Weichen Dai Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province and School of Computer Science Hangzhou Dianzi University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grained gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.

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