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Finite-time control for the Hypersonic Morphing Flight Vehicle with FTESO 37

Finite-time Control for the Hypersonic Morphing Flight Vehic...

引用

37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Zhang, Yuan Huang, Wanwei Nie, Ying Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China

ISBN: (纸本)9781665465366

To address the large uncertainty issues caused by large parameters perturbation, difficult modeling of the deformation process, and complex external disturbances for the hypersonic morphing flight vehicle attitude control, a class of variable back-swept vehicle models are established and a finite-time attitude control scheme with FTESO is designed. Firstly, the three-DOF models with deformations for attitude control are developed, which can reflect the intrinsic effects of the morphing process. Secondly, the aerodynamic properties of the morphing vehicle under some typical states are analyzed, and a feasible way to handle the key aerodynamic data of continuous deformation is presented. Furthermore, a finite-time control scheme based on sliding-mode method is designed for the continuously deformable hypersonic morphing flight vehicle. Additionally, considering the command differentiation terms are necessary for the control law, the command convergence filter is introduced. Then, the finite-time extended state observer is used to estimate the 'combined disturbances'. Finally, numerical simulations along with three circumstances verify the efficiency and robustness of the proposed control scheme. © 2022 IEEE.

关键词： Sliding mode control

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Infrared small target detection algorithm with complex background based on YOLO-NWD 22

Infrared small target detection algorithm with complex backg...

引用

4th International Conference on Image Processing and Machine Vision, IPMV 2022

作者： Zhou, Xiao Jiang, Lang Guan, Xujun Mou, Xingang Wuhan University Of Technology China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory China

ISBN: (纸本)9781450395823

Because of small number of occupied pixels, lacking shape and texture information, the reliability of infrared remote target detection has always been a difficult research topic. To improve the accuracy and precision of detection of infrared small targets under complex background conditions, a deep learning-based infrared small target detection algorithm YOLO-NWD is proposed. According to the characteristics of small and medium targets in infrared images, multi-channel feature fusion image was used as the input of YOLO detection framework combined with image preprocessing method. Combined with SE module and ASPP module, feature weights are explored to improve feature utilization efficiency. Finally, the normalized Wasserstein distance (NWD) loss is used to replace the original IoU calculation loss to reduce the sensitivity of small target position deviation. The experimental results show that the algorithm proposed in this paper improves the accuracy by 2.5% and the recall rate by 4%. © 2022 ACM.

关键词： Deep learning

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Feature Attention Complex-Valued-Based CNN for Ship Target Recognition of SAR Images 2

Feature Attention Complex-Valued-Based CNN for Ship Target R...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Pan, Hongxin Yang, Chao Yin, Yifei Liang, Bin Shi, Hao Wu, Guanghui Beijing Institute of Technology Chongqing Innovation Center Chongqing China Shanghai Institute of Satellite Engineering Shanghai China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

The details regarding targets within Synthetic Aperture Radar (SAR) imagery are typically conveyed and maintained in complex-valued form. Namely, the information regarding both the amplitude and phase is essential. In the process of recognizing targets with SAR, the network only utilizes the amplitude image as input, while the phase information is neglected, resulting in poor recognition performance. This paper proposes a Feature Attention Complex-Valued-Based convolutional neural Network (FACV-Net) for SAR ship recognition to overcome these issues, which effectively utilizes phase information to improve target recognition. Firstly, to meet the requirements of network training in complex domains, a series of complex-valued operation blocks have been constructed. Moreover, a new complex-valued Attention Module (CAM) is introduced to ensure that the network focuses on the amplitude and phase characteristics of the target separately. Additionally, to address the mismatch problem between the amplitude and phase data, two distinct methods for complex-valued max-pooling are utilized. Ultimately, the performance of the proposed FACV-Net is assessed using the OpenSARship dataset, and the results indicate that this novel approach surpasses conventional networks in terms of recognition accuracy. When the CAM is added, the performance of the network sees a further enhancement of around 3% in accuracy. These findings confirm the proposed method's effectiveness and advantage over others. © 2024 IEEE.

关键词： Cams

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WDNet: A Water Depth Perception Network with Global Information 42

WDNet: A Water Depth Perception Network with Global Informat...

引用

42nd Chinese control Conference, CCC 2023

作者： Huang, Zhihui Tang, Yifeng Tang, Chuancong Xu, Yifan Luo, Chuanshang Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures (sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network (WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image, and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Errors

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Dynamic Graph Dismantling with Reinforcement Supervised Learning 42

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Yumei Tang, Chuancong Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

There are always some 'key' nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the 'key' nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, ϵ-greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Complex networks

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Koopman Operator Learning control of the Piezoelectric Actuator 42

Koopman Operator Learning Control of the Piezoelectric Actua...

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42nd Chinese control Conference, CCC 2023

作者： Huang, Xiang Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

The piezoelectric actuator is one kind of device that can drive nanoscale motion. However, the nonlinear hysteresis effect induced by its natural material greatly degrades its positioning accuracy. To handle this challenging issue, this work develops a Koopman model predict control (Koopman- MPC) framework for the piezoelectric actuator. Specifically, the Koopman operator theory is adapted for modeling the piezoelectric actuator dynamics. A simple yet powerful linear model spanned in a high-dimensional space is thus constructed to characterize the hysteresis dynamics. Subsequently, upon the established Koopman model, an MPC scheme is put forward for tracking control of piezoelectric actuators. Therein, by sustained optimizing a cost function containing future outputs and control increments, the control input is obtained. Moreover, extensive tracking simulations are carried out on a simulated piezoelectric actuator for verifying the feasibility and effectiveness of the Koopman- Mpc scheme. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Model predictive control

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Fault Diagnosis of Rolling Bearings Under Variable Speed Conditions Based on Order Analysis

Fault Diagnosis of Rolling Bearings Under Variable Speed Con...

引用

TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Guiyi Zhang, Chao Wu, Le Liu, Tongtong Ouyang, Bing School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic System Baotou014010 China

ISBN: (纸本)9783031702341

To address the issue of bearing fault diagnosis without speed signal, a phase-ratio analysis method based on speed tracking is proposed. Firstly, the Short-Time Fourier Transform (STFT) is employed to obtain a time-frequency map. Then, the rotation frequency is calculated based on the parameters of the bearings, as well as the speed range. Subsequently, the speed matching tracking method is utilized to track the rotation frequency from the time-frequency map, followed by linear fitting and resampling to approximate the speed signal. Finally, the obtained signal is angularly sampled and subjected to FFT spectral analysis to obtain the order ratio spectrum. Based on the features of the order ratio spectrum, bearing faults are diagnosed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Roller bearings

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Xu, Shuai Zhang, Chao Zhang, Jing Fei, Hongbo Wu, Le College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China

ISBN: (纸本)9783031702341

To solve the problem of low prediction accuracy of the traditional similarity matching method, a similarity matching remaining life prediction method combining monotonicity and Spearman correlation is proposed. Firstly, monotonicity is used to screen the time domain and frequency domain features of the bearings, and then Spearman correlation is used to extract the nonlinear relationship of the screened features. Next, the extracted features are fused using principal component analysis to construct the health indexes that characterize the degradation trend of the bearings. An autoregressive moving model is then used to construct a similarity model, in which the Manhattan distance is used to measure the degree of similarity between the two curves and to make the remaining life estimation. Finally, the validation is carried out on the public dataset PHM2012 dataset and compared with Poly2 and Exp1. The experimental results show that the proposed method can effectively improve the remaining life prediction accuracy, which proves the effectiveness and superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Prediction models

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Adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection 7th

Adaptive Communication Spectrum Sensing Algorithm Based on E...

引用

7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Shi, Weihong Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent loT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Based on the traditional energy detection algorithm, an adaptive communication spectrum sensing algorithm based on energy detection is proposed for signals whose noise power is difficult to be estimated accurately. The algorithm takes into account the problem of large error in calculating the energy detection judgment threshold by noise power when the estimated value of noise power is inaccurate, and is able to perform adaptive communication spectrum sensing for signals with less a priori information and noise power that cannot be estimated accurately. Simulation results show that the adaptive communication spectrum sensing algorithm based on energy detection can effectively improve the performance of spectrum sensing for signals, and the algorithm can effectively detect and sense signals under the condition of reasonable setting of judgment coefficients. © The Author(s) 2025.

关键词： Adaptive Cognitive radio Energy detection Spectrum sensing

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Escape with Self-adaptive Decision Radius based on Deep Deterministic Policy Gradient in Pursuit Games 13

Escape with Self-adaptive Decision Radius based on Deep Dete...

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

作者： Zhou, Xiaojie Yang, Chunxi Wang, Wenbo Sun, Pengqi Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China

ISBN: (纸本)9798350361674

The Pursuit-Evasion (PE) game of Unmanned Surface Vehicles (USVs) is a classic antagonistic problem for the intelligent agent system. To enhance the escaping success rate of evaders with better effort, this paper proposes an escape strategy based on the geometrical characteristics of Apollonius circles. An improved self-adaptive escaping strategy for the evader utilizing the deep deterministic policy gradient algorithm is proposed. Then, the criteria for successful encirclement by pursuer are given. A DDPG algorithm-based framework is proposed on the basis of markov decision process formulation. Specifically, an action space based on adaptive decision radius of evader is designed. Our simulation shows the proposed method has more advantages in terms of escape distance and escape time. © 2024 IEEE.

关键词： Unmanned surface vehicles

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