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Data Driven control and Learning Systems (DDCLS)

作者： Zhong-Xin Fan Lintao Tang Shihua Li Rongjie Liu School of Automation Southeast University Nanjing P. R. China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing P. R. China Department of Statistics Florida State University Tallahassee FL USA

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Training Linear systems Learning systems Neural networks Optimal control Reinforcement learning Data collection

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FOTS: A Fast Optical Tactile Simulator for Sim2Real Learning of Tactile-motor Robot Manipulation Skills

arXiv

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arXiv 2024年

作者： Zhao, Yongqiang Qian, Kun Duan, Boyi Luo, Shan The School of Automation Southeast University The Key Laboratory of Measurement and Control of CSE Ministry of Education No.2 Sipailou Nanjing210096 China The Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Simulation is a widely used tool in robotics to reduce hardware consumption and gather large-scale data. Despite previous efforts to simulate optical tactile sensors, there remain challenges in efficiently synthesizing images and replicating marker motion under different contact loads. In this work, we propose a fast optical tactile simulator, named FOTS, for simulating optical tactile sensors. We utilize multi-layer perceptron mapping and planar shadow generation to simulate the optical response, while employing marker distribution approximation to simulate the motion of surface markers caused by the elastomer deformation. Experimental results demonstrate that FOTS outperforms other methods in terms of image generation quality and rendering speed, achieving 28.6 fps for optical simulation and 326.1 fps for marker motion simulation on a single CPU without GPU acceleration. In addition, we integrate the FOTS simulation model with physical engines like MuJoCo, and the peg-in-hole task demonstrates the effectiveness of our method in achieving zero-shot Sim2Real learning of tactile-motor robot manipulation skills. Our code is available at https://***/Rancho-zhao/FOTS. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Thick 3D-Printed Hierarchical Li4Ti5O12@MOF anode and Grid-Lined micropores for High-Performance Lithium-Ion batteries

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Journal of Colloid and Interface Science 2025年 696卷 137883页

作者： Mwizerwa, Jean Pierre Li, Jie Nsengiyumva, Walter Li, Chen Xu, Kun Rasaki, Sefiu Abolaji Liu, Changyong School of Materials Science and Engineering Fujian University of Technology Fuzhou350118 China Additive Manufacturing Institute College of Mechatronics & Control Engineering Shenzhen University Shenzhen518060 China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and Automation Fuzhou University Fuzhou China The Institute of Precision Instrument and Intelligent Measurement & Control Fuzhou University Fuzhou China Institute: Department of Chemical and Petroleum Engineering University of Calgary AlbertaT2N 1N4 Canada

Li4Ti5O12 (LTO) anode is a promising candidate for high-energy–density lithium-ion batteries (LIBs), but achieving high mass loading, a porous structure, and efficient ion transport remains a challenge. Herein, we present a high-mass-loading, hierarchically porous LTO anode with a conductive network and grid-lined micropores, embedded with a metal–organic framework (MOF) as both an electrode additive and surface protective layer. This novel structure is fabricated using extrusion-based three-dimensional (3D) printing technology. The self-standing framework provides mechanical stability and high conductivity, enabling a thick (316 μm) 3D-printed LTO@UiO-66-MOF (3D-LTO@U) anode with a mass loading of 11 mg/cm2. It delivers a high-rate capability of 161 mAh/g at 5C, an areal specific capacity of 5.37 mAh/cm2, and 78.9 % areal capacity retention after 150 cycles. Additionally, it achieves a high specific energy density of 382.35 Wh/kg. The UiO-66 MOF provides strong binding affinity, suppressing side reactions and enhancing Li-ion/electron transport within the 3D-printed interconnected channels. This improves active material utilization during charge and discharge. Furthermore, a 3D-printed full cell integrating a grid-lined 3D-LTO@U anode and a 3D-printed LiFePO4 cathode exhibits enhanced electrochemical performance. This work demonstrates an effective strategy for designing thick, high-mass-loading, and porous conductive network anodes for advanced LIBs. © 2025 Elsevier Inc.

关键词： Lithium-ion batteries

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Real-time Semantic Segmentation of Solar Photovoltaic Arrays for Autonomous UAV Flights 43

Real-time Semantic Segmentation of Solar Photovoltaic Arrays...

引用

43rd Chinese control Conference, CCC 2024

作者： Li, Nanyan Chen, Hao Sun, Zhigang Gao, Jing Yi, Dewei Liu, Cunjia Su, Jinya Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China State Grid Jiangsu Integrated Energy Service CO. Ltd Nanjing210096 China Jiangsu Sudian Industry Management CO. Ltd Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9789887581581

This paper considers the real-time semantic segmentation of solar PhotoVoltaic (PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles (UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg 16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union (IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and 96.5% IoU, with a 27.59 fps (using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Semantic Segmentation

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Bearing remaining useful life prediction using spatial-temporal multiscale graph convolutional neural network

引用

measurement Science and Technology 2023年第8期34卷 085009-085009页

作者： Yang, Xiaoyu Li, Xinye Zheng, Ying Zhang, Yong Wong, David Shan-Hill The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China Department of Chemical Engineering National Tsing-Hua University Hsin-Chu Taiwan

Remaining useful life (RUL) prediction of bearings is important to guarantee their reliability and formulate a maintenance strategy. Recently, deep graph neural networks (GNNs) have been applied to predict the RUL of bearings. However, they usuallylack dynamic features, use manual stage identification, and experience the over-smoothing problem, which will have a negative effect on the prediction accuracy. This paper proposes a new framework for bearing RUL prediction based on spatial-temporal multi-scale graph convolutional neural network (STMSGCN), which can improve the accuracy of prediction by solving the above-mentioned problems. Specifically, different to the most-used static feature of bearings, a dynamic feature that can capture the time-varying change of vibration energy is proposed. A sliding window alarm method is proposed to detect the fault occurrence time (FOT), which can offer an accurate healthy stage rather than human-defined methods. Then, the STMSGCN is proposed to predict the RUL of bearings, which solves the over-smoothing problem of the deep GNN model. The PRONOSTIA platform is adopted to verify the proposed method. The results verify that the sliding window alarm method can detect the FOT faster for slowly degrading bearings, and the proposed STMSGCN structure gives higher prediction accuracy compared to the existing methods. © 2023 IOP Publishing Ltd

关键词： Forecasting

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Guarding Force: Safety-Critical Compliant control for Robot-Environment Interaction

arXiv

引用

arXiv 2024年

作者： Wang, Xinming Yang, Jun Mao, Jianliang Liang, Jinzhuo Li, Shihua Yan, Yunda The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China The Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The College of Automation Engineering Shanghai University of Electric Power Shanghai201306 China The Department of Computer Science University College London LondonWC1E 6BT United Kingdom

In this study, we propose a safety-critical compliant control strategy designed to strictly enforce interaction force constraints during the physical interaction of robots with unknown environments. The interaction force constraint is interpreted as a new force-constrained control barrier function (FC-CBF) by exploiting the generalized contact model and the prior information of the environment, i.e., the prior stiffness and rest position, for robot kinematics. The difference between the real environment and the generalized contact model is approximated by constructing a tracking differentiator, and its estimation error is quantified based on Lyapunov theory. By interpreting strict interaction safety specification as a dynamic constraint, restricting the desired joint angular rates in kinematics, the proposed approach modifies nominal compliant controllers using quadratic programming, ensuring adherence to interaction force constraints in unknown environments. The strict force constraint and the stability of the closed-loop system are rigorously analyzed. Experimental tests using a UR3e industrial robot with different environments verify the effectiveness of the proposed method in achieving the force constraints in unknown environments. Note to Practitioners—Robotic manipulators are increasingly involved in complex interaction missions with humans and various environments. As these interactions become more prevalent, ensuring the safety of both robots and their human and environmental counterparts has become imperative. Although advanced controllers with safety constraints have been explored, ensuring strict interaction force guarantees remains challenging, particularly in unknown environments. To address this challenge, this paper proposes a new safety-critical compliant control scheme using the control barrier function (CBF) technique. By formulating the approach as quadratic programming and dynamically adjusting the nominal compliant controller online, the propose

关键词： controllers

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Smith predictor-based nonlinear internal model control for water temperature regulation of combi-boiler systems

Smith predictor-based nonlinear internal model control for w...

引用

Jiangsu Annual Conference on automation (JACA 2023)

作者： R. Liu M. Cao S. Li J. Yang Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing 210096 People's Republic of China Department of Aeronautical and Automotive Engineering Loughborough University Loughborough LE11 3TU UK

This paper proposes a reliable and practical water temperature control scheme for combi-boiler systems working in domestic hot water mode. In practice, the water temperature regulation process of a combi-boiler is strongly affected by the nonlinearities of actuators, the variation of system parameters caused by the fluctuation of water flow rate, external disturbances, and large, variable time delays. Although there are some results that can address one or two of these factors, a comprehensive and concise control scheme with consideration of all these factors is lacking. This paper aims to change this situation. First, based on the modeling and identification of real combi-boiler systems, the feedback linearization method is adopted in the controller synthesis to solve the effects of actuator nonlinearity. Next, a Smith predictor-based control structure is designed to eliminate system time delays. Finally, based on the internal model control (IMC) theory, an IMC-based parameter tuning method is proposed for the parameter selection of feedback controllers, in which the model parameters used in the controller synthesis are calculated in real-time based on the system identification results. As a result, the influence of model uncertainties and external disturbances can be effectively attenuated, and only one parameter is required to be tuned for the desired performance. The feasibility and effectiveness of the proposed control scheme are verified by simulations.

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Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach 12th

Classification-Detection of Medical Images by Visualizing Hi...

引用

12th International Conference on Computer engineering and Networks, CENet 2022

作者： Tong, Ziyu Deng, Xing Shao, Haijian Wang, Xia School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing Jiangsu210096 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Context-aware Emotion Recognition Based on Vision-Language Pre-trained Model

Context-aware Emotion Recognition Based on Vision-Language P...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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Reinforcement Learning based Data-driven Optimal control Strategy for Systems with Disturbance

Reinforcement Learning based Data-driven Optimal Control Str...

引用

Data Driven control and Learning Systems (DDCLS)

作者： Zhong-Xin Fan Shihua Li Rongjie Liu School of Automation Southeast University Nanjing P. R. China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing P. R. China Department of Statistics Florida State University Tallahassee FL USA

This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. System dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.

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