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Surface-PMSM Sensorless Control Strategy Based on Reduced-Order Linear Kalman Filter Cooperating with Prediction Error Rolling Compensation

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IEEE Transactions on Power Electronics 2025年第8期40卷 10804-10815页

作者： Xu, Sang Shen, Anwen Tang, Qipeng Wang, Miaomiao Luo, Pan Luo, Xin Xu, Jinbang National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors (SPMSMs) due to its strong robustness against measurement and system noise. However, conventional method suffers from poor position and speed estimation accuracy under dynamic conditions, and high computational cost. In order to solve these problems, an enhanced sensorless control strategy based on reduced-order linear Kalman filter (RLKF) cooperating with prediction error rolling compensation is introduced in this paper. Differing from conventional method, a reduced-order linear state equation encompassing rotor position, speed and torque disturbance is constructed. Then, by obtaining the prediction error of system output using the motor current equation in the predictive reference frame, the optimal estimation of rotor position and speed in KF algorithm is achieved based on the reduced-order model. This not only simplifies the calculation process, but also enhances the position and speed estimation accuracy under dynamic conditions. Meanwhile, a prediction error rolling compensation algorithm is developed to minimize the estimation deviations caused by motor parameters variations. Additionally, the system's dynamic performance is further improved with an adopted speed controller based on torque disturbance feedforward and acceleration feedback. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy.1 © 1986-2012 IEEE.

关键词： Permanent magnets

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Short-Term Residential Load Forecasting via Pooling-Ensemble Model with Smoothing Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3690-3702页

作者： Xiao, Jiang-Wen Fang, Hongliang Wang, Yan-Wu Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this article proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole short-term load forecasting framework in this article contains three steps. Specifically and first, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Second, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Third, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2020 IEEE.

关键词： Probability distributions

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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Attitude Detection Model Based on Improved High Resolution Network 13

Attitude Detection Model Based on Improved High Resolution N...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Shi, Dongliang Li, Yuhan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

2D human posture detection, a challenging task in computer vision, aims to identify human body key points and is widely used. The HRNet model performs well in human key point detection. To enhance its effect in human posture detection and address suboptimal prediction accuracy in interactive occlusion, this paper combines HRNet and Transformer. The Transformer's attention mechanism handles complex image dependencies, and HRNet obtains image features. By embedding the Transformer's attention mechanism module into HRNet's backbone network and integrating a self-designed double-branch structure and convolutional attention module, an improved HRNet model is constructed. The DConvAttention module processes extracted features, and the convolutional attention mechanism balances image frequencies, enhancing processing and discrimination of complex backgrounds and occlusions and improving detection performance. In tests of complex and occluded images, the improved HRNet Local model has a 4.2M parameter increase, 0.5G FLOPs increase, and 1% mAP increase compared to HRNet, with the same recall rate. The improved HRNet Global model has 11M more parameters, 1.1G more FLOPs, a similar or slightly higher recall rate, and a 2% higher mAP value. © 2025 IEEE.

关键词： Face recognition

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A Novel Self-Learning Network Integrating Contrastive Learning, Perceptual Learning and Masked image Modelling 15

A Novel Self-Learning Network Integrating Contrastive Learni...

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15th International Conference on Graphics and image processing, ICGIP 2023

作者： Chen, Yingxian Yang, Rui Lan, Rushi Guangxi Key Laboratory Image and Graphic Intelligent Processing Guilin University of Electronic Technology China

ISBN: (纸本)9781510675018

Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain similarities, particularly in their data augmentation techniques. Masking, a type of data augmentation, can be utilized for both contrastive learning and masked image modelling. This paper presents a novel deep learning approach on visual unsupervised learning. It integrates previous methods such as contrastive learning, perceptual learning, self-distillation and masked image modelling. In our method, we treat the network that handles the original images as the teacher network, and the network that handles the masked images as the student network. The student network employs the representations extracted by the projection head for contrastive learning, while the features generated by the decoder are employed for masked image modeling. The process of self-knowledge distillation is facilitated by perceptual learning between the teacher and student networks. This model aligns with the main idea of contrastive learning, which aims to pull similar images closer while pushing dissimilar images further apart. Simultaneously, it reflects the main idea of masked image modelling, which enables the extraction of semantic information from large scale masked pixel reconstruction tasks. Additionally, we compare the effect of self-supervised methods to the performance of the model. Our results show that with only 75 epochs of fine-tuning, our 29M-parameter model achieves 78.5% top-1 accuracy on the imageNet-1k dataset. © 2024 SPIE. All rights reserved.

关键词： Unsupervised learning

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Design of Dual-mode DPE Receiver Based on GPS L1/BDS B1C Signals

Design of Dual-mode DPE Receiver Based on GPS L1/BDS B1C Sig...

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2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Jia, Qiongqiong Guo, Qiqi Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300000 China

ISBN: (纸本)9780936406404

With the expanding applications of the Global Navigation Satellite System (GNSS), its operating environment has become increasingly complex, with challenges such as multipath effects, non-line-of-sight (NLOS) propagation, and signal attenuation caused by tall urban buildings and trees. This complexity poses additional challenges for traditional GNSS receivers. Direct Position Estimation (DPE) addresses some of the limitations of traditional receivers by accumulating signals from all visible satellites directly in the navigation domain. However, when severe obstructions result in a reduced number of visible satellites, DPE may fail to function properly. The introduction of multiconstellation satellite signals can help mitigate the issue of insufficient visible satellites in complex environments. Based on this, this paper designs a dual-mode DPE receiver utilizing Global Positioning System (GPS) L1 signals and BeiDou Navigation Satellite System (BDS) B1C signals. © 2025 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM. All rights reserved.

关键词： Global positioning system

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Distributed Fixed-Time Dynamic Event-Triggered Algorithm for Economic Dispatch in Smart Grids Over Directed Networks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 173-180页

作者： Ji, Lianghao Liu, Qi Zhang, Cuijuan Yang, Shasha Li, Huaqing Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

A fresh algorithm is presented for tackling the economic dispatch problem (EDP) in smart grids with directed network topology. This algorithm is based on distributed consensus and aims to minimize the total cost of power generation while maintaining the balance between power supply and demand and complying with generation capacity restrictions. Even with unknown and bounded disturbances, this algorithm can achieve optimal dispatch within a fixed time. In addition, an expected settling time can be calculated, which is a more accurate upper bound estimation that relies solely on the generators' initial conditions. Meanwhile, the dynamic event-triggered strategy is employed for reducing sampling frequency and network resource consumption. Finally, numerical simulations validate the effectiveness of the designed algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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Improved LYT-Net image Enhancement Model for Underwater Robotics Vision 13

Improved LYT-Net Image Enhancement Model for Underwater Robo...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Wenjie Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Güttin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

—Although underwater robots can replace humans to explore the ocean which is rich in resources but fraught with unknown risks, there are phenomena such as monotonous colors, complex backgrounds and uneven illumination in the underwater environment, which makes it difficult for underwater robots relying on the optical vision system to obtain clear images and is not conducive to subsequent recognition and grasping tasks. In this paper, relevant preparatory work was carried out for underwater image processing applicable to underwater robots, and the underwater imaging model as well as other underwater image processing models were analyzed. Subsequently, a method combining the underwater physical model and the dark channel image was proposed to improve LYT-Net, enabling it to be applicable to mobile embedded devices such as underwater robots. After training and testing on the real datasets LSUI and UIEB, our model achieved good results on important indicators such as PSNR and SSIM. Meanwhile, it met the theoretical requirements of mobile devices in terms of FLOPs, Params and single-frame processing time, which verified its robustness and real-time processing ability in various underwater environments. © 2025 IEEE.

关键词： Underwater imaging

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A High-Efficiency Doppler Frequency Parallel Acquisition Method Based on A-GPS

A High-Efficiency Doppler Frequency Parallel Acquisition Met...

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2025 International Technical Meeting of The Institute of Navigation, ITM 2025

作者： Jia, Qiongqiong Zhao, Yuchen China Civil Aviation University Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin300000 China

ISBN: (纸本)9780936406404

Acquisition is a key step for Global Navigation Satellite System (GNSS) receivers to achieve positioning and other functions, and the acquisition speed directly affects the Time To First Fix (TTFF) of the receiver. The acquisition process typically involves generating local reference signals for all satellite signals and matching them with the received signals. For each satellite signal, this matching involves searching in the unknown Doppler frequency and code phase space. Traditional parallel acquisition methods can only perform parallel processing in either the Doppler frequency dimension or the code phase dimension, and still require linear search in the other dimension. To reduce the computational load and improve the acquisition efficiency, this paper proposes a high-efficiency parallel acquisition method based on A-GPS. This method utilizes the auxiliary information provided by the A-GPS base station to convert the linear search of visible star signals in Doppler frequency into a single parallel search in the Doppler search space, thereby reducing the computational load required for acquisition to 1/N of the conventional method, where N is the total number of frequency bands searched. The paper verifies through theoretical analysis and experimental results that the proposed method can significantly improve acquisition efficiency while ensuring acquisition performance. © 2025 Proceedings of the International Technical Meeting of The Institute of Navigation, ITM. All rights reserved.

关键词： Global positioning system

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Geometric Algebra Based Kalman Filter Trajectory Prediction 3

Geometric Algebra Based Kalman Filter Trajectory Prediction

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3rd International Symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Li, Taiyu Jiao, Weidong Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China

ISBN: (纸本)9798350350418

To improve the safety and efficiency of aviation operation, a Kalman filtered trajectory prediction algorithm based on geometric algebra is proposed in this space. Firstly, the Kalman filter equations for trajectory prediction in geometric algebraic space are given by taking position, velocity and acceleration as state variables and position, velocity and acceleration in a typical trajectory as observations. Secondly, the matrix operation in the geometric algebraic space is simplified to the matrix operation by establishing a composite geometric algebraic space. Then, the versor operator in the conformal geometric algebraic space is introduced into the Kalman filter, and the steps of geometric algebra-based Kalman filter trajectory prediction are given to improve the prediction rate and accuracy. The experimental results show that the proposed trajectory prediction algorithm has the highest accuracy and the operation efficiency is improved by 27.66% compared with the Kalman filter algorithm in Euclidean space and the DBSCAN clustering algorithm. © 2024 IEEE.

关键词： Trajectories

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