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RETRACTED: Dual-mode predictive flexible rotor control for active magnetic bearings with input constraints

Journal of Vibration and control 2016年第20期23卷 NP2页

作者： Yue Wu Jie Zhao Hai-Tao Zhang Ming-Can Fan Xi Yu Guangdong HUST Industrial Technology Research Institute Guangdong Province Key Lab of Digital Manufacturing Equipment ChinaKey Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology China Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology China Department of Mathematics Huizhou University China School of Electrical and Information Engineering Xi'an Technology University China

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The entropy of stochastic processes based on practical considerations

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AIP ADVANCES 2020年第4期10卷

作者： Zhou, Yangyang Hu, Hanping Diao, Jin Chen, Yitao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Maritime Commun Res Inst Wuhan 430074 Peoples R China

The limitations of the Shannon entropy and the dynamic Shannon entropy are discussed. They only measure the aleatory uncertainty of stochastic processes. In this paper, based on the practical considerations, an entropy formula for measuring the uncertainty of stochastic processes is proposed, which measures not only the aleatory uncertainty but also the epistemic uncertainty. The uncertainty of the Gaussian process in four different situations is discussed. These works fill the gap between information theory and signal processing and thus can be used as a design guideline for constructing a secure source in the security field.

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A deep learning-based remaining useful life prediction approach for bearings

arXiv

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arXiv 2018年

作者： Cheng, Cheng Ma, Guijun Zhang, Yong Sun, Mingyang Teng, Fei Ding, Han Yuan, Ye The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The College of Control Science and Engineering Zhejiang University Hangzhou310007 China The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using a ǫ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

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Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Ge, Leijiao Lou, Suhua Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China The Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Performance Analysis of Full-Duplex NOMA Assisted Satellite-Terrestrial Systems with Hardware Impairments and Imperfect CSI

Performance Analysis of Full-Duplex NOMA Assisted Satellite-...

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International Conference on Information, Communication and Networks (ICICN)

作者： Jinlong Zhao Mingming Huo Wanwei Tang Xinwei Yue School of Artificial Intelligence Gansu University of Political Science and Law Lanzhou China School of Electronic and Information Engineering Beihang university Beijing China Archives Center Lanzhou University of Technology Lanzhou China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665490832

In this paper, we investigate the satellite-terrestrial system employing non-orthogonal multiple access (NOMA) scheme, where a full-duplex (FD) decode-and-forward (DF) relaying assists the user in weak channel condition. The effects of hardware impairments, imperfect channel state information (CSI) and imperfect interference cancellation on system performance are considered. For this setting, we derive the closed-form expressions of the exact and asymptotic outage probabilities as well as delay-limited sum throughput for NOMA users. Numerical results are provided to demonstrate the correctness of theoretical analysis and the superiority of adopting FD NOMA scheme in the proposed system.

关键词： NOMA Interference cancellation Satellites System performance Full-duplex system Probability Throughput

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A direct adaptive fuzzy sliding mode controller and its application

A direct adaptive fuzzy sliding mode controller and its appl...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Song Hyok Ri Jian Huang Myong Ho Kim Ji Yong Kim Gwangmin Choe Chol Jun Rim School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Science Pyongyang D. P. R. of Korea (Democratic People's Republic of Korea) Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Science Pyongyang D.P.R. of Korea (Democratic People's Republic of Korea) School of Computer Science Kim II Sung university Pyongyang D. P. R. of Korea (Democratic People's Republic of Korea)

ISBN: (纸本)9781479987313

An approach of direct adaptive fuzzy sliding-mode control which combines the fuzzy control with the sliding-mode control, is proposed for the control of a class of unknown nonlinear dynamic systems. The control goal is to obtain a direct adaptive fuzzy sliding-mode control law and a constructive Lyapunov synthesis approach with respect to a class of nonlinear systems without the knowledge of uncertainties. For improving the approximate performance of the fuzzy system, the proposed approach in this study not only online updates the parameter values in the consequence fuzzy sets, but also updates the shape parameters of the membership functions of the prime fuzzy sets. The fuzzy control rules are updated through the online adaptive learning, which makes the output of fuzzy control to approximate to a sliding-mode equivalent control. The asymptotic stability of the overall system based on Lyapunov theory is proved. Some numerical simulation results show the efficiency of the proposed approach.

关键词： Adaptive systems Nonlinear dynamical systems Sliding mode control Fuzzy control Adaptation models

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Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Phase Synchronization Between Motor Cortices During Gait Movement in Patients With Spinal Cord Injury

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2016年第1期24卷 151-157页

作者： Li, Wei Xu, Jiang Chen, Xi He, Jiping Huang, Yue Huazhong Univ Sci & Technol Coll Automat Wuhan 430074 Peoples R China Minist China Image Proc & Intelligent Control Key Lab Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Dept Rehabil Med Wuhan 430030 Peoples R China Arizona State Univ Harrington Dept Bioengn Ctr Neural Interface Design Tempe AZ 85287 USA East China Jiaotong Univ Sch Elect & Elect Engn Nanchang 330013 Peoples R China

Spinal cord injury (SCI) frequently leads to generalized locomotor disability and gait disturbances which cause serious discomfort among patients. Human gait is a complex process in the central nervous system that results from the integration of various mechanisms which remain unclear. Therefore, it is of great theoretical and practical significance to investigate the cortical activity patterns during gait movement in SCI. In this study, brain activity was recorded by electroencephalogram (EEG) during two kinds of gait-like movements. Phase synchronization between motor cortices was investigated through source analysis and phase locking. Results revealed that diverse neural networks with different resonance-like frequencies exist in the brain. Further, we found that the premotor cortex played an important role in the control of passive gait-like movement. In attempted/active movement, spatial function and multimodal integration with somatosensory information are crucial aspects of posterior parietal cortex function which need to be considered separately in different EEG bands. Our results further confirmed that neural system control patterns in passive gait-like movement differ from those in attempted or active gait-like movement. Novel insights into human gait will provide a basis for improvements in future neurorehabilitation applications.

关键词： Electroencephalogram (EEG) gait movement phase synchronization spinal cord injury

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An Evolutionary Real-Time 3D Route Planner for Aircraft

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Chinese Journal of Systems Engineering and Electronics 2003年第1期000卷

A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time,with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.

关键词： Evolutionary computation Route planning Route constraints Real time Aircraft

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Adaptive Synchronization of Complex Dynamical Networks Governed by Local Lipschitz Nonlinearlity on Switching Topology

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JOURNAL OF APPLIED MATHEMATICS 2013年第unknown期2013卷 818242-1-818242-8页

作者： Liu, Bo Wang, Xiaoling Gao, Yanping Xie, Guangming Su, Housheng North China Univ Technol Coll Sci Beijing 100144 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Beijing Technol & Business Univ Coll Comp & Informat Engn Beijing 100048 Peoples R China Peking Univ Coll Engn Dept Ind Engn & Management Beijing 100871 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Dept Control Sci & Engn Wuhan 430074 Peoples R China

This paper investigates the adaptive synchronization of complex dynamical networks satisfying the local Lipschitz condition with switching topology. Based on differential inclusion and nonsmooth analysis, it is proved that all nodes can converge to the synchronous state, even though only one node is informed by the synchronous state via introducing decentralized adaptive strategies to the coupling strengths and feedback gains. Finally, some numerical simulations are worked out to illustrate the analytical results.

关键词： Adaptive Synchronization Complex Dynamical Networks Governed Local Lipschitz Nonlinearlity topology switching feedback gain dynamic network Network Lipschitz condition Self tuning

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