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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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Fixed-time extended state observer-based trajectory tracking control for autonomous underwater vehicles

Fixed-time extended state observer-based trajectory tracking...

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作者： Zheng, Jiaqi Song, Lei Liu, Lingya Yu, Wenbin Zhu, Shanying Wang, Yiyin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

In this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time-varying external disturbances of currents and waves in the sea. To solve the problem, a fixed-time extended state observer (FTESO)-based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed-time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed-time convergence but also reduces the trajectory tracking errors of the AUV. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Autonomous underwater vehicles

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Double STAR-RIS Enhanced Secure Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Cai, Yujin Yu, Wenwu Nie, Xiaokai Cheng, Qiang Cui, Tiejun Southeast University School of Mathematics Jiangsu Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently been employed to enhance the secure transmission due to the ability to manipulate the full-space electromagnetic signals. Prior works mainly investigate the single STAR-RIS aided secure communication system, which does not reveal the potential of the cooperative inter-STAR-RIS link. In this paper, a double STAR-RIS enhanced secure communication network involving multiple legitimate users (LUs) and eavesdroppers (Eves) is proposed. For fairness, the minimum secrecy rate maximization problem is formulated by jointly optimizing the active beamforming and passive transmission and reflection (T&R) beamforming. To deal with the highly non-convex nonlinear optimization problem, an active and passive beamforming optimization algorithm is proposed by employing the alternating optimization method. Wherein, a low-complexity sequential constraint relaxation (SCR) based algorithm is designed to solve the passive T&R beamforming optimization problem. Simulation results demonstrate that the secure performance in the double STAR-RIS assisted system outperforms that in the single RIS/double RIS/single STAR-RIS aided network. Furthermore, the proposed algorithm significantly reduces computational time (i.e., resource allocation time) compared with the traditional SCR based beamforming optimization algorithm. © 2002-2012 IEEE.

关键词： Cooperative communication

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Stability and Bifurcation control of Fractional BAM Neural Networks with Diffusion and Time Delays 42

Stability and Bifurcation control of Fractional BAM Neural N...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Feng Wang, Wei Cai, Gang Zhou, Feifan Wang, Hua O Yang, Xiuqin School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Ministry of Education for Intelligent Technology of Earth Exploration China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Boston University Department of Mechanical Engineering BostonMA02215 United States School of Education Minzu University of China Beijing100081 China

ISBN: (纸本)9789887581543

Based on fractional calculus theory and reaction-diffusion equation theory, a fractional-order time-delay reaction-diffusion neural network with Neumann boundary conditions is investigated. By constructing the phase space basis based on the Laplace operator eigenvector, the system equation is linearized to obtain the characteristic equation. Then, the characteristic equation is analyzed, and the local stability of the system at the equilibrium point is discussed. And taking the time delay as the bifurcation parameter, the stability changes of the system at the equilibrium point and the generation conditions of the Hopf bifurcation are studied when the time delay changes. Moreover, a state feedback controller is designed to control the bifurcation of the system. Finally, the theoretical derivation is verified by numerical simulation. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Time delay

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Dynamic Analysis and control of Fractional Order Memristor Hopfield Neural Network 42

Dynamic Analysis and Control of Fractional Order Memristor H...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Feng Cai, Gang Zhou, Feifan Wang, Wei Wang, Hua Yang, Xiuqin School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Ministry of Education for Intelligent Technology of Earth Exploration China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan4300748 China Boston University Department of Mechanical Engineering BostonMA02215 United States School of Education Minzu University of China Beijing100081 China

ISBN: (纸本)9789887581543

In this paper, a novel smooth magnetron is introduced to construct a fractional memristor Hopfield neural network (fractional order M-HNN). The local stability of equilibrium point are analyzed theoretically. Taking the memristor coupling strength coefficient and the fractional order as bifurcation parameters, the phase trajectory diagram, the bifurcation diagram of the system are drawn to analyze the influence on the dynamic behavior of the neural network. When the system parameters are fixed, the hyperchaos phenomenon of the fractional order M-HNN model is revealed. Finally, the PD controller is applied to the model to enhance the stability of the system. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Hopfield neural networks

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Wind Speed Data Repairing Method Based on Bidirectional Prediction

Wind Speed Data Repairing Method Based on Bidirectional Pred...

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2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021

作者： Shen, Xincheng Qu, Yi Huang, Shaoxiong Li, Zhi Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Anhui Electric Power Co. Ltd Department of Dispatch Center Hefei China

ISBN: (纸本)9781728190037

In order to repair the lost data in distributed wind power system, this paper puts forward a wind speed data repairing model based on a new bidirectional prediction method. This model consists of two one-way prediction models. In each prediction model, the original wind speed data are decomposed into several intrinsic mode functions (IMFs) and a residue signal by ensemble empirical mode decomposition (EEMD) method. Then the Savitzky-Golay (SG) filter is used to reduce noise for high-frequency IMFs. Next the long short-term memory (LSTM) model and autoregressive integrated moving average (ARIMA) model are combined to predict low-frequency IMFs and the noise reduction results respectively. At the end, all those forecast results are added and form a one-way result. By weighted average of two one -way results, the repairing result is calculated. The experimental results from multiple prediction cases show that this method can get more accurate results. © 2021 IEEE.

关键词： Empirical mode decomposition

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The SA-UNet temperature field reconstruction based on GAN data augmentation of wet friction components

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measurement: Journal of the International measurement Confederation 2025年 254卷

作者： Wu, Jianpeng Ding, Ao Zhang, Hanshu Zhao, Peng Yang, Chengbing Wang, Liyong Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing China Rice University Department of civil and environmental engineering HoustonTX United States

Wet friction components are critical in mechanical equipment and are crucial for ensuring their safe and stable operation. Ulteriorly, the distribution of temperature fields in wet friction components directly impacts their transmission efficiency and safety performance, thus investigating temperature field reconstruction is significant for the health management of mechanical transmission devices. Firstly, in order to expand the data, this study uses a Generative Adversarial Network (GAN) to augment temperature field data from a finite element model simulation. This augmented dataset is trained by a Self-Attention UNet (SA-UNet) deep learning model. Additionally, 30 sets of experimental data are employed as test samples to reconstruct the surface temperature fields of friction components using the trained SA-UNet reconstruction model. Subsequently, the model performance is analysed to assess the impact of varying numbers, locations, and conditions of the observation points in training samples. The results demonstrate that the MRE of the SA-UNet reconstruction model is 2.4%, reflecting higher accuracy compared to other deep learning models. This study aims to develop a precise and reliable model for reconstructing the temperature distribution in wet friction components, providing valuable insights into their thermal health status. © 2025

关键词： Finite element simulation GAN SA-UNet Temperature field reconstruction Wet friction components

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A Semi-Supervised Railway Foreign Object Detection Method Based on GAN 21

A Semi-Supervised Railway Foreign Object Detection Method Ba...

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Chen, Yanqi Tong, Shuzhen Lu, Xiaobo Wei, Yun School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beijing Mass Transit Railway Operation Corporation Limited Beijing China

ISBN: (纸本)9781450389853

The rapid development of deep learning provides new technical means for railway foreign object detection. However, in practical applications, the datasets of railways with foreign objects are scarce. In order to solve this problem, by improving the loss function and anomaly image evaluation standard, this paper proposes a newsemisupervised anomaly detection method based on GAN (Generative Adversarial Networks). Experiments show that our method can achieve railway foreign object detection without anomaly prior knowledge. Regarding anomaly recognition, a 0.058 AUC (Area Under Curve) and a 6% classification accuracy relative improvement for the railway dataset used in this paper are obtained.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Generative adversarial networks

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TransTracking for UAV: An Autonomous Real-time Target Tracking system for UAV via Transformer Tracking

TransTracking for UAV: An Autonomous Real-time Target Tracki...

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2021 International Conference on Intelligent Technology and Embedded systems, ICITES 2021

作者： Sun, Xiaolou Xie, Fei Wang, Qi Yao, Yuncong Wang, Hao Wang, Wei Yang, Wankou School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Nanjing University of Information Science and Technology School of Automation Nanjing211800 China

ISBN: (纸本)9781665427555

Most of the existing Siamese-type trackers usually adopt pre-defined anchor boxes or anchor-free schemes to accurately estimate the bounding box of targets. Unfortunately, they suffer from complicated hand-designed components and tedious post-processings. It is not easy to adjust parameters for unique scenes in real applications. So, we propose a new scheme by formulating visual tracking as a direct set prediction problem to alleviate this issue. The main component is a transformer attached to the Siamese-type feature extraction networks. Thus, our new framework can be summarized as Siamese Network with Transformers (SiamTFR). With a fixed small set of learned object queries, we force the final set of predictions via bipartite matching, significantly reducing hyper-parameters associated with the candidate boxes. Due to the unique predictions of this framework, we significantly ease the heavy burden of hyper-parameters search of post-processings in visual tracking. Extensive experiments on visual tracking benchmarks, including GOT-10K, demonstrate that SiamTFR achieves competitive performance and runs at 50 FPS. Specifically, SiamTFR outperforms leading anchor-based tracker SiamRPN++ in the GOT-10K benchmark, confirming its effectiveness and efficiency. Furthermore, SiamTFR is deployed on the embedded device in which the algorithm can be run at 30FPS or 54FPS with TensorRT meeting the real-time requirements. In addition, we design the complete tracking system demo that can work in the real road to narrow the gap between the academic models and industrial deployments. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Image Enhancement Method of Impact Damage of Carbon Fiber Reinforced Plastics Based on Infrared Sequence Images

Research on Image Enhancement Method of Impact Damage of Car...

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China Instrument and control Society (ACCIS), Academic Conference of

作者： Lihua Yuan Jianlong Liu Yishun Jin Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang China Department of Measurement and Control Technology and Instruments School of Testing and Optoelectronic Engineering Nanchang Hangkong University Nanchang China

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

The long pulse reflection method was used to detect the internal defects of the impact damage of carbon fiber reinforced plastics. Under the excitation of halogen lamp, the thermal imager synchronized the change of the surface temperature field of the test block and stored in the form of infrared sequence images. In order to improve the imaging quality of infrared images, a variety of infrared sequence image processing algorithms were studied, including polynomial fitting method, robust principal component analysis, independent component analysis and pulse phase method. The performance of each sequence image processing algorithm was quantitatively analyzed by using peak signal-to-noise ratio, root mean square error and entropy. The results show that the image quality evaluation indexes of these four sequence image processing algorithms are better than those of the original image, indicating that these four algorithms are suitable for enhancing the infrared thermal image of impact damage. In particular, the peak signal-to-noise ratio and entropy of the robust principal component analysis enhancement results are optimal among all the processing methods, and the root mean square error is suboptimal.

关键词： PSNR Process control Independent component analysis Fiber reinforced plastics Entropy Polynomials Image sequences Carbon Root mean square Principal component analysis

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