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Model-free Based Reinforcement Learning Control Strategy of Aircraft Attitude systems

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Model-free Based Reinforcement Learning Control Strategy of ...

Chinese Automation Congress (CAC)

作者： Dingcui Huang Jiangping Hu Zhinan Peng Bo Chen Mingrui Hao Bijoy Kumar Ghosh School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Texas Tech University Lubbock USA

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Traditional aircraft control algorithms have a strong dependence on system models, and are difficult to cope with the increasingly complex battlefield environment for intelligent aircrafts. In this paper, a model-free reinforcement learning is proposed to solve an attitude stabilization problem of an aircraft based online intelligent control strategy. The attitude control problem is firstly formulated as an optimal control problem, and then an adaptive dynamic programming (ADP) technology is applied to compute the corresponding nonlinear Hamilton-Jacobi-Bellman (HJB) equation. Then, an actor-critic neural network structure is established to learn the optimal controller online not requiring the information of the aircraft dynamics. The proposed intelligent control strategy enables the aircraft to adjust its attitude according to the actual mission targets and environments under the proposed online control strategy, so that autonomous learning and intelligent operation can be realized. Finally, simulation examples are presented to validate the proposed model-free based control strategy.

关键词： Atmospheric modeling Aircraft Adaptation models Mathematical model Aerodynamics Attitude control Vehicle dynamics

BLOWN: A Blockchain Protocol for Single-Hop Wireless Networks under Adversarial SINR

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arXiv 2021年

作者： Xu, Minghui Zhao, Feng Zou, Yifei Liu, Chunchi Cheng, Xiuzhen Dressler, Falko The School of Computer Science and Technology Shandong University Qingdao266510 China The Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China Ernst & Young Shanghai200120 China The School of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

Design of a compact ultra-wideband MIMO antenna

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Design of a compact ultra-wideband MIMO antenna

IET International Radar Conference 2018, IRC 2018

作者： Youcheng, Wang Yanjiao, Yang Qingxi, Chi Hucheng, Pei Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

A compact ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna is presented. The MIMO antenna consists of four monopoles which are printed on FR4 substrate, whose impedance bandwidth is from 2 to 12 GHz, and the isolation of two adjacent antennas is less than−20 dB in mostly frequency band. The current distribution of the antenna is discussed by simulation, and the radiation characteristics of the antenna are improved by increasing the parasitic radiation patch and digging holes in the radiation patch. Four monopoles are placed with polarisation orthogonality, and the adjacent units are separated by slot to cut off the coupling current on the floor, thus effectively improving the isolation of the antenna. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Electric impedance

Failure Mode and Effects Analysis of UAV Power system Based on Generalized Dempster-Shafer Structures

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Failure Mode and Effects Analysis of UAV Power System Based ...

2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Zhuo Hao, Mingrui Northwestern Polytechnical University School of Electronics and Information Xian China Intelligent Agent Cooperation Laboratory Science and Technology on Complex System Control Beijing China

ISBN: (纸本)9781728137926

The unmanned aerial vehicle(UAV) power system consists of components that provide flight power for the UAV, which mainly includes three parts: motor, electronic speed controller(ESC), and battery. Since the UAV always operates in the open air, which requires high stability of the power system, the failure mode and effects analysis(FMEA) is particularly significant and it has been widely used to examine potential failures in systems or components. However, the traditional FMEA method has many unreasonable points. In order to better deal with the uncertainty and improve the stability of the UAV power system, we propose a FMEA method which is based on the generalized Dempster-Shafer structures, the failure modes are comprehensively analyzed and the risks are ranked by combining the characteristic information of risk factors with the generalized evidence theory. The new method can not only deal with the uncertainty of risk assessment, but also further improve the reliability of effects analysis. In addition, the FMEA experiment of the UAV power system is carried out in this paper. The experimental results show that the method is effective for practical applications. © 2019 IEEE.

关键词： Fuzzy set theory

complex Value Image Group Sparse Coding Denoising Algorithm Based on K-means Clustering Method

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Ruan Jian Xue Bao/Journal of Software 2019年 30卷 17-24页

作者： Liu, Bo-Yu Wu, Ling-Da Hao, Hong-Xing Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

Sparse coding has been widely used in complex value image demising. In recent years, the proposed block sparse coding has more advantages in noise filtering and noise reduction because it can make full use of the similarity of patches in the same block. In this paper, a K-means clustering method based sparse demising algorithm for complex image grouping is studied. By improving the clustering algorithm, the grouping effectiveness of K-means algorithm for sparse block coding algorithm is verified. The online complex dictionary training algorithm is used to acquire the coded dictionary quickly, and the sparse coding of block image is realized by using the grouping orthogonal matching pursuit algorithm. By inducing the similarity of the coding in each block, the coding of noise in the block is effectively suppressed and the noise reduction of the complex value image is improved. In order to verify the effectiveness of the proposed algorithm, the demising of simulated and real interferometric synthetic aperture radar images is quantitatively analyzed, which proves that the proposed algorithm has a certain improvement in peak signal-to-noise ratio (PSNR) compared with the previous block sparse coding algorithm. Finally, the real interferometric synthetic aperture radar image is demised, which further verifies the de-noising ability of the proposed algorithm for real noise. © Copyright 2019, Institute of Software, the Chinese Academy of sciences. All rights reserved.

关键词： Image denoising

A novel DV-Hop method based on coupling algorithm used for Wireless Sensor Network localisation

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International Journal of Wireless and Mobile Computing 2019年第2期16卷 128-137页

作者： Wang, Yechuang Wang, Penghong Zhang, Jiangjiang Cai, Xingjuan Li, Wuchao Ma, Yanyan Complex System and Computational Intelligence Laboratory TaiYuan University of Science and Technology Taiyuan003024 China Jiaxing Vocational Technical College Zhejiang Sheng314001 China Shanxi Cloud Era TISCO Information Automation Technology Co. Ltd. Taiyuan030003 China

Wireless Sensor Network (WSN) localisation is an essential requirement in the increasing prevalence of WSN applications. It is an important part of the Internet of Things (IOT) and has become a hot research area. Distance Vector-Hop algorithm (DV-Hop), a range-free algorithm, is widely deployed to solve the localisation problem in WSN. However, the results of the estimation precision are usually not satisfactory. In order to improve the WSN positioning accuracy, in this paper, we propose a new coupling algorithm based on Bacterial Foraging Algorithm (BFA) and Glow-worm Swarm Optimisation (GSO) (BFO-GSO). The algorithm has good convergence speed, local search ability of BFO and global convergence of GSO. The optimisation performance is verified by CEC2013 benchmarks in those designs against the original algorithm. Furthermore, Wilcoxon’s rank-sum nonparametric statistical test and Friedman test are carried out to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The numerical results prove that it is able to significantly outperform others on majority of the benchmark functions. Finally, the proposed algorithm is also combined into the DV-Hop algorithm to improve the WSN positioning accuracy. Experimental results show that our improved algorithm achieves better performance when compared with other DV-Hop algorithms. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

Sliding sequential CVAE with time variant socially-aware rethinking for trajectory prediction

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arXiv 2021年

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China Research Center for Autonomous Vehicles Meituan Beijing China College of Computer Science Wenzhou University Wenzhou China National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively. © 2021, CC BY.

关键词： Trajectories

Neural architecture search for fault diagnosis

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arXiv 2020年

作者： Li, Xudong Hu, Yang Zheng, Jianhua Li, Mingtao National Space Science Center. CAS University of Chinese Academy of Sciences Beijing China Science and Technology on Complex Aviation System Simulation Laboratory 9236 mailbox Beijing China

Data-driven methods has made great progress in fault diagnosis, especially deep learning method. Deep learning is suitable for processing big data, and has a strong feature extraction ability to realize end-to-end fault diagnosis systems. Due to the complexity and variability of experimental data, some deep learning models are designed to be complex. However, designing neural network architecture requires rich professional knowledge and debugging experience, and a lot of experiments are needed to screen models and hyperparameters, increasing the difficulty of developing deep learning models. Fortunately, neural architecture search (NAS) is developing rapidly, and is becoming one of the next directions for deep learning. Given a search space, NAS can automatically search for the optimal network architecture. In this paper, we proposed a NAS method for fault diagnosis using reinforcement learning. A recurrent neural network is used as an agent to generate the network architecture. The accuracy of the generated network on the validation dataset is fed back to the agent as a reward, and the parameters of the agent are updated through the strategy gradient algorithm. In order to speed up the search process, parameters sharing method is adopted in this paper. We use PHM 2009 Data Challeng gearbox dataset to prove the effectiveness of propsed method, and obtain state-of-the-art results compared with other artificial designed network structures. To authors best knowledge, its the first time that NAS has been applied in fault diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Failure analysis

Discontinuous stochastic forcing in Greenland ice core data

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arXiv 2025年

作者： Riechers, Keno Morr, Andreas Lehnertz, Klaus Lind, Pedro G. Boers, Niklas Witthaut, Dirk Gorjão, Leonardo Rydin Climate Physics Max Planck Institute for Meteorology Germany Earth System Modelling School of Engineering & Design Technical University of Munich Germany Research Domain IV – Complexity Science Potsdam Institute for Climate Impact Research Potsdam14473 Germany Department of Epileptology University of Bonn Medical Center Bonn53105 Germany Helmholtz Institute for Radiation and Nuclear Physics University of Bonn Bonn53115 Germany Interdisciplinary Centre for Complex Systems University of Bonn Bonn53175 Germany Department of Computer Science OsloMet – Oslo Metropolitan University Oslo0130 Norway OsloMet Artificial Intelligence lab OsloMet Oslo0166 Norway Global Systems Institute Department of Mathematics University of Exeter United Kingdom Jülich52428 Germany Institute for Theoretical Physics University of Cologne Köln50937 Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås1432 Norway

Paleoclimate proxy records from Greenland ice cores, archiving e.g. δ18O as a proxy for surface temperature, show that sudden climatic shifts called Dansgaard–Oeschger events (DO) occurred repeatedly during the last glacial interval. They comprised substantial warming of the Arctic region from cold to milder conditions. Concomitant abrupt changes in the dust concentrations of the same ice cores suggest that sudden reorganisations of the hemispheric-scale atmospheric circulation have accompanied the warming events. Genuine bistability of the North Atlantic climate system is commonly hypothesised to explain the existence of stadial (cold) and interstadial (milder) periods in Greenland. However, the physical mechanisms that drove abrupt transitions from the stadial to the interstadial state, and more gradual yet still abrupt reverse transitions, remain debated. Here, we conduct a one-dimensional data-driven analysis of the Greenland temperature and atmospheric circulation proxies under the purview of stochastic processes. We take the Kramers–Moyal equation to estimate each proxy’s drift and diffusion terms within a Markovian model framework. We then assess noise contributions beyond Gaussian white noise. The resulting stochastic differential equation (SDE) models feature a monostable drift for the Greenland temperature proxy and a bistable one for the atmospheric circulation proxy. Indicators of discontinuity in stochastic processes suggest to include higher-order terms of the Kramers–Moyal equation when modelling the Greenland temperature proxy’s evolution. This constitutes a qualitative difference in the characteristics of the two time series, which should be further investigated from the standpoint of climate dynamics. © 2025, CC BY-NC-SA.

关键词： Stochastic systems