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Sustainable Power and Energy Conference (iSPEC)

作者： Tong Li Shimao Yu Chunhe Song Tongwei Yu Peng Zeng Li Shen Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China

Abnormal state accumulation over a long period will cause an electrical equipment fault. Therefore, Substation equipment state forecasting plays a vital role in smart grids. Fault forecasting method based on deep learning have been put forward in recent years, but they have deficiencies dealing with temporal features. This paper proposes an abnormal forecasting method for substation equipment based on improved LSTM, and temporal features of power equipment fault can be extracted effectively for a long-time span. The method reads secondary equipment information through SCADA system, then extracts feature to carry out deep learning training. In this paper, RNN and LSTM are firstly compared, and then the structure and algorithm of improved LSTM are given. This method increases coupling between forget gate and cell state compared with the standard LSTM structure, so as to make better use of information at previous moment. Meanwhile problems of network training and overfitting are discussed. Experiments are conducted to demonstrate that improved LSTM is capable of forecasting accurately the complex electric equipment state with a long forecasting horizon. The accuracy of abnormal state forecasting can reach more than 93% which has a better performance compared with many other methods.

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Giant-cavity-based quantum sensors with enhanced performance

arXiv

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arXiv 2022年

作者： Zhu, Y.T. Wu, R.B. Peng, Z.H. Xue, Shibei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Center for intelligent and networked systems Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China

Recent progresses have revealed that quantum systems with multiple position-dependent couplings, e.g., giant atoms, can exhibit some unconventional phenomena, such as non-exponential decay etc. However, their potential applications are still open questions. In this paper, we propose a giant-cavity-based quantum sensor for the first time, whose performance can be greatly enhanced compared to traditional cavity-based sensors. In our proposal, two cavities couple to a dissipative reservoir at multiple points while they couple to a gain reservoir in a single-point way. To detecting a unknown parameter using this sensor, a waveguide is coupled to one of the cavities where detecting fields can pass through for homodyne detection. We find that multiple position-dependent couplings can induce an inherent non-reciprocal coupling between the cavities, which can enhance the performance of sensors. Output noise in our scheme can be reduced to the shot noise level, which is about one order magnitude lower than the results in Ref. [Lau and Clerk (2018)]. Besides, the signal-to-noise ratio per photon is also enhanced by about one order of magnitude. These results show that the multiple-point-coupling structure is beneficial to nowadays quantum devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Couplings

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Comprehensive Evaluation Index of Wind Turbine Operation Fatigue Based on Equivalent Economic Loss

Comprehensive Evaluation Index of Wind Turbine Operation Fat...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Jing He Shaolin Li Qi Yao State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Energy and Electricity Research Center Jinan University Zhuhai China

ISBN: (纸本)9781665476881

The fatigue analysis is important in the operation of the wind turbine. However, integrating the fatigue index of each substructure to construct the overall fatigue index of the wind turbine is difficult. Different from the existing research that only uses the weighted summation method to design the overall fatigue index, an index construction method that relates the operating fatigue to the economic loss is proposed. The cumulative damage of the structure is derived by using the damage equivalent load in the fatigue evaluation of each substructure of the wind turbine, and then the maintenance cost of each structure is analyzed in depth, and the maintenance cost function of each substructure is constructed to determine the economic loss equivalent to the cumulative damage of the structure. Since the proposed method converts fatigue damage into economic loss, the economic loss of each structure can be directly added to construct the overall equivalent economic loss index, which avoids the problem of custom weighting of the comprehensive index structure. Finally, simulation experiments verify the scientificity and superiority of the proposed indicator.

关键词： Costs Simulation Design methodology Optimal control Maintenance engineering Fatigue Wind turbines

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MrDMD-Based Sensor Placement in Distributed Flow Estimation for the Design of the Artificial Lateral Line of an Underwater Robot

MrDMD-Based Sensor Placement in Distributed Flow Estimation ...

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IEEE Conference on Decision and control

作者： Jun Wang Tongsheng Shen Dexin Zhao Feitian Zhang Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China National Innovation Institute of Defense Technology Beijing China Department of Advanced Manufacturing and Robotics State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

An artificial lateral line (ALL) is a sensing system that imitates the distributed perception organs of fish and plays a major role in enhancing the flow estimation capability of underwater robots. Whereas various ALLs have been designed and developed, it is still an open question how to better place ALL sensors on underwater robots, especially for those with complex shapes and working in dynamic flow and robot operating conditions. Aiming to answer this question, this paper presents a novel data-driven sensor placement method for ALLs of underwater robots. This method adopts distributed pressure sensors to measure the flow field along the profile or the outermost boundary of an underwater robot, and quantifies the dynamic information embedded within these measurements using multi-resolution dynamic mode decomposition (mrDMD). The sensors are then positioned by optimizing the dynamic flow information to enhance the perception. Compared with existing sensor placement methods, such as observability maximization and exhaustive experimental search, the proposed method focuses on the modes of dynamics variability at various spatio-temporal scales, thus leading to improved sensing ability especially in complex and dynamic flows. In addition, comprehensively considering the sensor placement under different flow and robot operating conditions, the proposed method is expected to provide an optimal solution for the overall sensing performance of the ALL system. To demonstrate the effectiveness of the proposed method, a case study of background flow speed estimation of oscillating underwater robots of different shapes in a uniform flow is presented.

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Robust alignment of multi-exposed images with saturated regions

arXiv

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arXiv 2020年

作者： Jiang, Jun Li, Zhengguo Xie, Shoulie Wu, Shiqian Zeng, Liangcai Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and Technology Wuhan China Institute for Infocomm Research Singapore Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

It is challenging to align multi-exposed images due to large illumination variations, especially in presence of saturated regions. In this paper, a novel image alignment algorithm is proposed to cope with the multi-exposed images with saturated regions. Specifically, the multi-exposed images are first normalized by using intensity mapping functions (IMFs) in consideration of saturated pixels. Then, the normalized images are coded by using the local binary pattern (LBP). Finally, the coded images are aligned by formulating an optimization problem by using a differentiable Hamming distance. Experimental results show that the proposed algorithm outperforms state-of-the-art alignment methods for multi-exposed images in terms of alignment accuracy and robustness to exposure *** Codes CS © 2020, CC0.

关键词： Hamming distance

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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Hover-to-Cruise Transition control for High-Speed Level Flight of Ducted Fan UAV

Hover-to-Cruise Transition Control for High-Speed Level Flig...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Zihuan Cheng Hailong Pei College of Automation Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong College of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In this paper, we address the hover-to-cruise transition control problem for high-speed level flight of a ducted fan UAV. Firstly, the nonlinear aircraft dynamic system is derived and relevant key aerodynamic characteristics are presented. Then, we divide the dynamic system into known parts and uncertain parts. In order to track a reference system formed by the known parts, we propose a neural-network-based control strategy to learn the error dynamics and cancel the tracking error with proved stability. Finally, simulations and experiments are conducted to perform the transition process at fixed altitude and constant horizontal acceleration, with satisfactory results accomplishing the designed flight course.

关键词： Fans Aerodynamics Aircraft Vehicle dynamics Aerospace control Unmanned aerial vehicles Neural networks

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Position control of XY precision planar motion table based on input shaping filter

Position control of XY precision planar motion table based o...

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International Conference on Electronics, Communications and Information Technology (CECIT)

作者： HuaLiang Zhang Xu Lu DanPing Jia BingJie Zhao Tao Zhang Yang Liu HongYu Yan Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China

ISBN: (纸本)9781665437585

In order to solve the vibration problem of mining equipment, a Simulink simulation model of current loop, speed loop and position loop of permanent magnet synchronous motor was established to track the input position signal of the motor system.[1] At the same time, the input shaper is introduced into the motor three loop control model to study the vibration suppression effect of the input shaper.[2] The essence of the input shaper is to filter the input signal. Usually, the parameters of the input shaper need to be accurately identified and the detailed parameters of the system need to be identified. However, the problem that the identification accuracy of the system is not enough often makes the parameters of the shaper uncertain. In view of the problem that the system identification is not accurate, a method of adjusting and setting input shaper parameters is designed, which is not very dependent on the system. The results show that the optimal parameters of the shaper can be selected by the method. Compared with the parameters of the shaper selected according to the identification model, the vibration suppression effect is better.

关键词： Vibrations Analytical models Tracking loops Software packages Magnetic separation Transfer functions Position control

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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Chinese control Conference (CCC)

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

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Compliant Impedance control for a Redundant Manipulator During Human Robot Interaction

Compliant Impedance Control for a Redundant Manipulator Duri...

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International Conference on Automation and Computing (ICAC)

作者： Yiming Jiang Chenguang Yang Zhaojie Ju Andy Annamalai Honghai Liu Key Laboratory of Autonomous Systems and Networked Control South China University of Technology Guangzhou China Zienkiewicz Center for Computational Engineering Swansea University Swansea UK School of Computing University of Portsmouth Portsmouth UK Moray College University of Highlands and Islands Elgin UK

ISBN: (纸本)9781538648919

Robot control with a compliant motion behaviour is important to guarantee the safety of human robot interaction. In this paper, we aim to guarantee the desired task impedance of the end-effector and to ensure a compliant behaviour of a redundant robot during human robot interactions. A task space impedance control scheme is proposed by designing a transferred impedance error, such that the robot end-effector can be governed to follow a target impedance model. Additionally, a low-priority controller is designed using the null space projection, such that compliant joint motion is guaranteed without affecting the main task. The stability of the whole system is illustrated using a conditional Lyapunov theorem. Simulation studies based on a plane redundant robot is carried out to demonstrate the validity of the proposed controller.

关键词： Robots Impedance Task analysis Aerospace electronics Null space Torque Force

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